A View From Somewhere: Human-Centric Face Representations
Authors: Jerone Theodore Alexander Andrews
Przemyslaw Joniak
Alice Xiang
Venue: ICLR 2023 poster (2023)
Keywords: similarity
faces
annotator bias
computer vision
cognitive
mental representations
diversity

Abstract: Few datasets contain self-identified demographic information, inferring demographic information risks introducing additional biases, and collecting and storing data on sensitive attributes can carry legal risks. Besides, categorical demographic labels do not necessarily capture all the relevant dimensions of human diversity. We propose to implicitly learn a set of continuous face-varying dimensions, without ever asking an annotator to explicitly categorize a person. We uncover the dimensions by learning on A View From Somewhere (AVFS) dataset of 638,180 human judgments of face similarity. We demonstrate the utility of our learned embedding space for predicting face similarity judgments, collecting continuous face attribute values, attribute classification, and comparative dataset diversity auditing. Moreover, using a novel conditional framework, we show that an annotator's demographics influences the \emph{importance} they place on different attributes when judging similarity, underscoring the \emph{need} for diverse annotator groups to avoid biases. Data and code are available at \url{https://github.com/SonyAI/a_view_from_somewhere}.

Published as a conference paper at ICLR 2023
A VIEW FROM SOMEWHERE:
HUMAN-CENTRIC FACE REPRESENTATIONS
Jerone T. A. Andrews"
Przemyslaw Joniak University of Tokyo, Tokyo
Alice Xiang
Sony AI, Tokyo
Sony AI, New York
ABSTRACT
Few datasets contain self-identified sensitive attributes, inferring attributes risks introducing additional biases, and collecting attributes can carry legal risks. Besides, categorical labels can fail to reflect the continuous nature of human phenotypic diversity, making it difficult compare the similarity between same-labeled faces. To address these issues, we present A View From Somewhere (AVFS)-a dataset of 638,180 human judgments of face similarity." We demonstrate the utility of AVFS for learning continuous, low-dimensional embedding space aligned with human perception. Our embedding space, induced under novel conditional framework, not only enables the accurate prediction of face similarity, but also provides a human-interpretable decomposition of the dimensions used in the human-decision making process, and the importance distinct annotators place each dimension. We additionally show the practicality of the dimensions for collecting continuous attributes, performing classification, and comparing dataset attribute disparities.
INTRODUCTION
The canonical approach to evaluating human-centric image dataset diversity is based on demographic attribute labels. Many equate diversity with parity across the subgroup distributions (Kay al., 2015; Schwemmer et al., 2020), presupposing access to demographically labeled samples. However, most datasets are web scraped, lacking ground-truth information about image subjects (Andrews et al., 2023). Moreover, data protection legislation considers demographic attributes to be personal information and limits their collection and use (Andrus al., 2021; 2020).
Even when demographic labels are known, evaluating diversity based on subgroup counts fails to reflect the continuous nature of human phenotypic diversity (e.g., skin tone is often reduced to "light" vs. "dark"). ). Further, even within the same subpopulation, image subjects exhibit certain traits to greater or lesser extent than others (Becerra- -iera et al., 2019; Careagni et al., 2015; Feliciano, 2016).
When labels are unknown, researchers typically choose certain attributes they consider to be relevant for human diversity and use human annotators infer them (Karkkainen & Joo, 2021; Wang et al., 2019). Inferring labels, however, is difficult, especially for nebulous social constructs, e.g., race and gender (Hanna et al., 2020; Keyes, 2018), and can introduce additional biases (Freeman et al., 2011). Beyond the inclusion of derogatory categories (Koch et al., 2021: Birhane & Prabhu, 2021; Crawford & Paglen, 2019), label taxonomies often do not permit multi-group membership. resulting in the erasure of, e.g., multi-ethnic individuals (Robinson et al., 2020; Karkkainen & Joo, 2021). Significantly, discrepancies between inferred and self-identified attributes can induce psychological distress by invalidating an individual's self-image (Campbell & Troyer, 2007: Roth, 2016).
In this work, we avoid problematic semantic labels altogether and propose to learn a face embedding space aligned with human perception. To do so, we introduce A View From Somewhere (AVFS) dataset of 638.180 face similarity judgments over 4,921 faces. Each judgment corresponds to the odd-one-out (i.c., least similar) face triplet of faces and is accompanied by both the identifier and demographic attributes of the annotator who made the judgment. Our embedding space, induced under a novel conditional framework, not only enables the accurate prediction of face similarity, but also
"Correspondence jerone. andrews@ sony. com.
Work done while the author was an intern at Sony AI, Tokyo.
www.s/oww./oo.//cc/ooo.oo/.. /aavii///vvvee// tww.ffoooooowwerrreeere Code and data may be found https: h/g.ttb...t...////g

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Published as a conference paper at ICLR 2023
provides numan-interpretabbe decomposition of the dimensions used in the human decision-maie making process, as well as the importance distinct annotators place on each dimension. We demonstrate that the individual embedding dimensions (1) are related to concepts of gender, ethnicity, age, as well as face and hair morphology: and (2) can be used to collect continuous attributes, perform classification, and compare dataset attribute disparities. We further show that annotators are influenced by their sociocultural backgrounds, underscoring the need for diverse annotator groups to mitigate bias.
RELATED WORK
Similarity. The human mind is conjectured to have, "a considerable investment in similarity" (Medin et al., 1993). When two objects compared they mutually constrain the set of features that are activated in the human mind (Markman, 1996)-ic.. features are dynamically discovered and aligned based what is being compared. Alignment is contended to be central to similarity comparisons. Shanon (1988) goes as far as arguing that similarity is not a function of features, but that the features themselves are a function of the similarity comparison.
Contextual similarity. Human perception of similarity can vary with respect to context (Roth & Shoben, 1983). Context makes salient context-related properties and the extent to which objects being compared share these properties (Medin et al., 1993; Markman & Gentner, 2005; Goodman, 1972). For example, Barsalou (1987) found that "snakes" and "raccoons" were judged be more similar when placed in the context of pets than when no context was provided. The odd-one-out similarity task (Zheng et al., 2019) used in this work also provides context. By varying the context (i.e., the third object) in which two objects are experienced, it is possible to uncover different features that contribute to their pairwise similarity. This is important as there are an uncountable number of ways in which two objects may be similar (Love & Roads, 2021).
Psychological embeddings. Multidimensional scaling (MDS) often used to learn psychological embeddings from human similarity judgments (Zheng et al., 2019; Roads & Love, 2021; Dima et al., 2022; Josephs et al., 2021). As MDS approaches cannot embed images outside of the training set, researchers have used pretrained models feature extractors (Sanders & Nosofsky, 2020; Peterson et al., 2018; Attarian et al., 2020), which can introduce unwanted implicit biases (Krishnakumar et al., 2021; Steed & Caliskan, 2021). Moreover, previous approaches ignore inter- and intra-annotator variability. By contrast, our conditional model is trained end-to-end and can embed any arbitrary face from the perspective of specific annotator.
Face datasets. Most face datasets are semantically labeled images, created for the purposes of identity and attribute recognition (Karkkainen & Joo, 2021; Liu et al., 2015; Huang al., 2008; Cao et al., 2018). An implicit assumption is that semantic similarity is equivalent to visual similarity (Deselaers Ferrari, 2011). However, many semantic categories are funetional (Rosch, 1975; Rothbart & Taylor, 1992), i.e., unconstrained by visual features such as shape, color, and material. Moreover, semantic labels often only indicate the presence or absence of an attribute, as opposed to its magnitude, making it difficult to compare the similarity between same-labeled samples. While face similarity datasets exist, the judgments narrowly pertain to identity (McCauley et al., 2021; Sadovnik et al., 2018; Somai & Hancock, 2021) and expression similarity (Vemulapalli & Agarwala, 2019).
Annotator positionality. Semantic categorization by annotators not only depends on the image subject, but also extrinsic contextual cues (Freeman et al., 201 1) and the annotators' sociocultural backgrounds (Segall et al., 1966). Despite this, annotator positionality is rarely discussed in computer vision (Chen & Joo, 2021; Zhao et al., 2021; Denton et al., 2021); "'nlyy five publications [from 113] provided any [annotator] demographic information" (Scheuerman et al., 2021). To our knowledge, AVFS represents the first human-centric vision dataset, where each annotation is associated with the annotator who created it and their demographics, permitting the study of annotator bias.
A VIEW FROM SOMEWHERE DATASET
To learn a face embedding space aligned with human perception, we collect AVFS a dataset of odd-one-out similarity judgments collected from humans. An odd-one-out judgment corresponds to the least similar face in a triplet of faces, representing a altermatvee forced choice (3AFC) task. AVES dataset documentation can be found in Appendix C.
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Face image stimulus set. 4,921 near-frontal faces with limited eye occlusions and apparent age > 19 years old were sampled from the CC-BY licensed FFHQ (Karras et al., 2019) dataset. The subset was obtained by (1) splitting FFHQ into 56 partitions based on inferred intersectional group labels; and (2) randomly sampling from each partition with equal probability. Ethnicity was estimated using FairFace model (Karkkainen & Joo, 2021); and binary gender expression and age group labels were obtained from FFHQ-Aging crowdsourced human annotations (Or-El et al., 2020).
3AFC similarity judgments. AVFS contains 638,180 quality-controlled triplets over 4,921 faces, representing 0.003% of all possible triplets. To focus judgments on intrinsic face-varying features, for each presented triplet, annotators were instructed choose the person that looks least similar the two other people, while ignoring differences in pose, expression, lighting, accessories, background, and objects. Each AVFS triplet labeled with a judgment, as well as the identifier of the annotator who made the judgment and the annotator's self-reported age, nationality, ancestry, and gender identity. As in previous non-facial odd-one-out datasets (Josephs et al., 2021; Hebart et al., 2022; Dima et al., 2022), there is a single judgment per triplet. Quality was controlled by excluding judgments from annotators who provided overly fast, deterministic, or incomplete responses. In total, 1,645 annotators contributed to AVES via Amazon Mechanical Turk (AMT) and provided consent to use their study data. Compensation was 15 USD per hour.
3AFC task rationale. Let x X and sim Xx) sim(i.)) R denote face image and similarity function, respectively. Our motivation for collecting AVES is fourfold. Most significantly, the odd-one-out task does not require annotator to explicitly categorize people. Second, for triplet (x,, Xj, Xs), repeatedly varying X permits the identification of the relevant dimensions that contribute to sim(i. That is, w.l.o.g., X provides context for which sim(i, j) is determined, making the task easier than explicit pairwise similarity tasks (i.e., "Is x; similar to x;,?). This is because is not always apparent to an annotator which dimensions are relevant when determining sim(i, j), especially when x; and Xj are perceptually different. Third, there is no need to prespecify attribute lists hypothesized as relevant for comparison (e.g.. "Is x; older than x, ?"). The odd-one-task implicitly encodes salient attributes that are used to determine similarity. Finally, compared to 2AFC judgments for triplets composed of an anchor (i.e., reference point), X., a positive, Xp, and a negative, Xn. 3AFC judgments naturally provide more information. 3AFC triplets require an annotator to determine {sim(t, j), sim(i, k), sim (j, k)), whereas 2AFC triplets with anchors only necessitate the evaluation of {sim a. p). sim(a, n)).
4 MODEL OF CONDITIONAL DECISION-MAKING
Zheng et al. (2019) developed an MDS approach for learning continuous, non-negative, sparse em- beddings from odd-one-out judgments. The approach has been shown to offer an intepretable window into the dimensions in the human mind of object categories (Hebart et al., 2020), human actions (Dima et al., 2022), and reachspace environments (Josephs et al., 2021) such that dimensional values indicate feature magnitude. The method is based on three assumptions: (1) embeddings can be learned solely from odd-one-out judgments; (2) judgments are a function of (sim(i, j). sim(i, k), sim(j, });; and (3) judgments are stochastic, where the probability of selecting Jy
p(k) exp(sim(i,
(1)
Conditional similarity. At high-level, want to apply Zheng et al. (2019)'s MDS approach to learn face embeddings. However, the method (I) cannot embed data outside of the training set, limiting its utility; and (2) pools all judgments, disregarding intra- and anter-annotator stochasticity. Therefore, we instead propose to learn a conditional convolutional neural net (convnet).
Let {((xi, Xye, x kt, )> denote a training set of n (triplet, judgment, annotator) tuples. where a E A. To simplify notation, we assume that judgments always correspond to index kt. Suppose f: x fx) = E Rdd is a convnet, parametrized by O, where w E IRd is an embedding of x. In contrast to Zheng et al. (2019), we model the probability of annotator selecting kt as p(kl| a) x (il,
where (..)) denotes the internal similarity function of a. Given two images x; and Xj, we define their similarity according to a as:
sim, (i = (a()a) ReL.U(..)) (a @ ReI.U(w,))
(3)
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where a--) is the sigmoid function and ( E [0, 1] is mask associated with Each mask plays the role of an element-wise gating function, encoding the importance a places on each of the embedding dimensions when determining similarity. Conditioning prediction that encode each annotator's notion of similarity, permitting us to study whether per-dimensional importance weights differ between E A. Veit et al. (2017) employed similar procedure to learn subspaces which encode different notions of similarity (e.g., font style, character type).
induces subspaces
E R || a trainable weight matrix, where each column vector & corresponds to annotator a's mask prior applying o(-). In our conditional framework, we jointly optimize @ and d by minimizing:
Conditional loss. We denote by = - 8 T
Σ log [p(k| | a)] + a1 > (ReLLU(W))l + a2 Σ ReLU(- wi), + + Σ 10
(4)
where
exp(sim, (i)s)
p(ke| a) exp(sima(1E,j)) + exp(sima
(5)
(if, (if, k()) + exp(sima kf))
is the predicted probability that ke is the odd-one-out conditioned on a. The first term in Equation (4) encourages similar face pairs result in large dot products. The second term (modulated by an R)
promotes sparsity. The third term (modulated by 02 E I)) supports interpretability by penalizing negative values. The last term (modulated by 03 IR) penalizes large weights.
Our conditional decision-making framework is applicable to any task that involves mapping from inputs to decisions made by humans; it only requires record- serdddkeeiing during data collection such that each annotation is associated with the annotator who generated it.
and (Φ.)) [L, ....... 1]. Va, Equation (4) corresponds to the unconditional MDS objective proposed by Zheng et al. (2019). We refer to unconditional and conditional models trained on AVFS as AVFS-U and AVFS-C. respectively, and conditional models whose masks are learned post hoc as AVFS-CPH. An AVFS-CPH model uses a fixed unconditional model (i.e., AVFS-U) as a feature extractor to obtain face embeddings such that only is trainable.
Implementation. When 0g
AVFS models have R (He et al., 2016) architectures and output 128-dimensional embeddings. We use the Adam (Kingma & Ba, 2014) optimizer with default parameters, reserving 10% of AVES for validation. Based on grid search, we empirically set a1 0,00005 and a2 0.01. For AVFS-C and AVFS-CPH, we additionally set ag = 0.00001. Across independent runs, we find that only needed and individual dimensions
fraction of the 128 dimensions optimization, we remove dimensions with maximal values (over the stimulus set) close to zero. For AVTS-U, this results in 22 dimensions (61.9% validation accuracy). Note that we observe that 8/22 dimensions have a Pearson's 's r correlation > 0.9 with another dimension. Refer to Appendix A for further implementation details.
reproducible. Post-
5 EXPERIMENTS
The aim of this work is to learn a face embedding space aligned with human perception. To highlight the value of AVFS, we evaluate the performance of AVFS embeddings and conditional masks for predicting face similarity judgments, as well as whether they reveal (I) a buman-interpretable
decomposition of the dimensions used in the human decision-making process; and (2) the importance distinct annotators place on each dimension. We further assess the practicality of the dimensions for collecting continuous attributes, classification, and comparing dataset attribute disparities.
Embedding methods. Where appropriate, for comparison, we consider embeddings extracted from face identity verification and face attribute recognition models: CASIA--ebFaad (Yi et al., 2014) is face verification model trained with an ArcFace (Deng et al., 2019) loss on images from 11K identities; CelebA (Liu et al., 2015) is a face attribute model trained to predict 40 binary attribute labels (e.g.. "pale skin", "young", "male"); and FairFace (Karkkainen & Joo, 2021) and FFHQ are face attribute models trained to predict gender, age, and ethnicity. All models have ResNet18 architectures and output 128-din 18-dimensional embeddings (prior to the classification layer). All baseline embeddings are normalized to unit-length, where the dot product two embeddings determines their similarity. Additional details the datasets and baselines are presented in Appendix B.
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Figure I: Predicting similarity (same stimuli). Accuracy over 24,060 judgments and Spearman's 's between the entropy in human- and model -goeer-geee triplet odd-one-out probabilities. Even when trained on a fraction of AVFS judgments (i.e., 12.5%), the learned AVFS-U-12.5% model still outperforms the baselines. Additional results are presented in Appendix A.
5.1 SAME STIMULI
Predicting similarity. We analyze whether our model results in stimulus set embeddings such that we are able to predict human 3AFC judgments not observed during learning. Using images from the stimulus set of 4,921 faces, we generate 1,000 novel triplets and collect 22-25 unique judgments per triplet (24,060 judgments) on AMT. As we have 22-25 judgments per triplet, we can reliably estimate odd-one-out probabilities. The Bayes optimal classifier accuracy corresponds to the best possible accuracy any model could achieve given the stochasticity in the judgments. The classifier makes the most probable prediction, i.e., the majority judgment. Thus, its accuracy is equal to the mean majority judgment probability over the 1,000 triplets, corresponding to 65.5+1% (95% CI).
In addition to accuracy, we report Spearman's 's r correlation between the entropy in human- and model-generatedd triplet odd-one-out probabilities. Human--gneraate odd-one-out probabilities are of the form: (n,, ny. /n) where, without loss of generality, nz/n corresponds to the fraction of n odd-one-out votes for k. Results shown in Figure 1. We observe that AVFS models outperform the baselines with respect to accuracy, while better reflecting human perceptual judgment uncertainty (i.e., entropy correlation). This holds to as few as 12.5% (72K) as evidenced by the AVFS-U-12.544 model. Further, our learned annotator- specific dimensional importance weights generalize, resulting a predictive accuracy at above the Bayes classifier's. s. As hypothesized (Section 3), transforming AVFS into 2AFC triplets (i.e., AVFS-2AFC in Figure 1) decreases performance due to reduction in information."
vary the number of AVFS training triplets, for example,
Dimensional interpretability. Following Hebart et al. (2020), we qualitatively assess whether learning on AVFS results in human-interpretable decomposition IR denote stimulus set image and its
the dimensions used in the
human decision-making process. Let x and ReLU(w) AVFS-U model embedding, respectively. We denote by dim, and %tile" the jth dimension and percentile of dim, stimulus set embedding values, respectively. Suppose grid, is a 5 x 100 face grid. Column y E [100, ...., 1] of grid, contains 5 stimulus set faces with the highest dim; values, satisfying %tile?- < ReLU(w); < %tile.. Thus, from left to right, grid, displays stimulus set faces sorted in descending order based on their dim, value. We task annotators with writing 1-3 gridy descriptions. After controlling for quality, we obtain 23-58 labels (708 labels) for each j and categorize the label responses into 35 topics.
Figure 2 evidences clear relationship between the top 3 dimy topics and dimy labels generated using CelebA and FairFace models. Across the 22 dimensions, we note the materialization of individual dimensions coinciding with commonly collected demographic group labels, i.c., "male", "female", "black", "white", "east asian", "south asian", "elderly", and "young". In addition, separate dimensions surface for face and hair morphology, i.e., "wide face", "long face", "smiling expression", "neutral expression", "balding", and "facial hair". Significantly, these dimensions, which largely explain the variation in human judgment behavior, are learned without training on semantic labels.
5.2 NOVEL STIMULI
Predicting similarity. We evaluate whether AVES models transfer to novel stimuli by sampling 56 novel face images from FFHQ (not contained in the stimulus set). We then generate all (5)) = 27,720
AVFS-2AFC trained using triplct margin with distance swap loss (Balntas al., 2016).
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Figure 2: Dimensional interpretability. (a) Our interpretation of an AVES-U dimension with the highest scoring stimulus set images. (b) Highest frequency human- -eenaaeeee topic labels. (c) CelebA and FairFace model-ggeeratee labels, exhibiting a clear correspondence with numan-generann Face images in this figure have been replaced by synthetic StyleGAN3 (Karras et al., 2021) images for privacy reasons. Additional dimensions are presented in the dataset documentation (Appendix C).
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Figure 3: Predicting similarity (novel stimuli). Accuracy over 80,300 judgments and Spearman's 's between the entropy in human- and model-generated similarity matrices. Our amotator-sppeciie masks (i.e., AVFS-C and AVFS-CPH models) generalize to triplets composed entirely of novel faces, resulting in increased predictive accuracy. Additional results are presented in Appendix A.
possible triplets, collecting 2-3 unique judgments per triplet (80,300 judgments) on AMT. We report accuracy, well as Spearman's 'ss r between the strictly upper triangular model- and human- generated similarity matrices. Entry (i.j) in the human-generann generated similarity matrix corresponds to the fraction of triplets containing (), where neither was judged as the odd-one-out; and (i. in model-generated similarity matrix corresponds to the mean ((i, j) over all triplets containing (i.
Results are shown in Figure 3. We see that AVFS embedding spaces and the learned amnotator-specine masks generalize to triplets composed entirely of novel faces. This non-trivially demonstrates that AVFS embedding spaces are more closely aligned with the human mental representational space of faces, than embedding spaces induced by training on semantic labels.
To quantify the number of AVFS-U dimensions required to represent a face while preserving per- formance, we follow Hebart et al. (2020)'s dimension-eliminatio approach. We iteratively zero out the lowest value per face embedding until a single nonzero dimension remains. This is done per embedding, thus the same dimension is not necessarily zeroed out from all embeddings during an iteration. To obtain 95-99% of the predictive accuracy we require 6-13 dimensions, whereas to explain 95-99% of the variance in the similarity matrix require 15-22 dimensions. This shows that (1) humans utilize a larger number of dimensions to represent the global similarity structure of faces than for determining individual 3AFC judgments; and (2) similarity context-dependent (i.e.. dynamic), which is not accounted for when representing faces using categorical feature vectors.
Annotator positionality. As shown via our conditional framework, knowledge of the annotator deternining similarity assists in informing the outcome. However, annotators are often framed as interchangeable (Malevé, 2020; Chancellor et al., 2019). To assess the validity of this assumption, we randomly swap the annotator associated with each of the collected 80,300 judgments and recompute the AVFS-CPH model accuracy. Repeating this process 100 times results in performance drop from 61.7% to 52.8% + 0.02% (95% CI), evidencing that annotator masks, and hence annotators, not arbitrarily interchangeable.
Another interesting question relates to whether annotator's sociocultural background influences their decision making. To evaluate this, we create datasets (aa())) ). y)}. where a ) and y e Y are annotator a's learned mask and self-identified demographic attribute. respectively. For particular annotator attribute (e.g., nationality), we limit the dataset to annotators who contributed > 200 judgments and belong one of the two largest groups with respect to the attribute (e.g..

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Figure 4: (a) Annotator positionality. Average linear SVM AUC when trained to discriminate
between AVFS-CPH annotator-specific masks from different demographic groups. None of the confidence intervals overlap with chance performance (horizontal black line). Note the abbreviations: "europe" ("eu"), "asia" ("as"), "western europe" ("euwe"), and "southern asia" ("assa"). (b) Pro- totypicality. Spearman's r between human ratings of face typicality and model-generate, ratings, evidencing that social category typicality manifests in the AVFS-U model's embedding dimensions.
y = ("american", "indian"")). Using 10-fold cross-validation. we train linear SVMs (Hearst et al., 1998) with balanced class weights to predict from a(8,). Figure 4a shows that for each attribute none of the AUC confidence intervals include chance performance. Significantly, we are able to discriminate between nationality-, regional ancestry-, and subregional ancestry-based annotator group masks with high probability (86-88%) Thus, to mitigate bias, diverse annotator groups are required.
Continuous attribute collection. Following Hebart et al. (2020)'s evaluation protocol, we quan- titatively validate whether grid, dej)edefined in Section 5.1-can be used to collect continuous attribute values for novel faces from human annotators. We task annotators with placing novel face x above a single column q of grid,. Placement is based on the similarity between x and the faces contained in each column q of grid,. As annotators are not primed with the meaning of grid,, accurate placement indicates that the ordering of dim, is visually meaningful.
Let nz denote the mean dim, embedding value over the stimulus set whose dim, value satisfies %tilees < ReLU(w), < %%tileJ. We sample 20 novel faces from FFHQ (not contained in the stimulus set). v(x, gridy) and collect 20 unique judgments per face (8,800 judgments). Yx, we
> 122 E R222, where create genan-generated embedding of the form: [20 Ex 10 ....: 20
4n E [100...., 1] denotes the T2th annotator's column choice q. We then generate all
(
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triplets to create a human-gence generated similarity matrix K E 200020 Entry (i.j) corresponds to the mean p(i, j) over all triplets containing (i,j) with the human human-generated embeddings. Using model-generated embeddings, we create a generatee matrix, K E 200x in the same way.
Spearman's 's r correlation between the strictly upper triangular elements of K and K is 0.83 and 0.86 for AVFS-U and AVFS-CPH embeddings, respectively. This shows that (1) dimensional values correspond to feature magnitude; and (2) image grids can be used to directly collect continuous attribute values, sidestepping the limits of semantic category definitions (Keyes, 2018; Benthall & Haynes, 2019).
Prototypicality. In cognitive science, prototypicality corresponds to the extent to which an entity belongs to a conceptual category (Rosch, 1973). We examine whether the dimensional value of face correlated with its typicality, utilizing prototypicality rated face images from the Chicago Face Database (CFD) (Ma et al., 2015; Lakshmi et al., 2021; Ma et al., 2021). Ratings correspond to the average prototypicality of a face with respect to binary gender category (relative to others of the same race) or race category. For each category, we compute the dimensional embedding value or unnormalized attribute logit of a face from a relevant AVFS-U dimension or attribute recognition model's 's classification layer, respectively.
Figure 4b shows that relevant AVFS-U U dimensions are positively correlated with typicality according to Spearman's r. r. Evidently, category typicality- at least for the investigated social category concepts manifests in the AVES dimensions from learning to predict human 3AFC similarity judgments.
Semantic classification. Given the evidence that AVFS-U dimensional values correlate with feature magnitude, we expect them to be useful for semantic attribute classification. To validate this hypothesis, we perform attribute prediction on the following labeled face datasets: COCO (Lin et al.,
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PPHO: '70+
Calib: "smliing"
GHD: happy
CFD: "east asian"
CFD: "black"
0.94 0.00 3.9
39 0.80 0.07 0:00
09 080
0,5 2.00
3.87 0.00 0.5
0.50 0.80 0.58
"CFD: "hik""
CFD: "indann
CasCont "ight
OOCO: night skin"
CelebA: "balding"
0.97 0.50 0.99
0:50
0.50
0A3 0.98
0:50
0.89
0.3
043
0.58
Figure 5: Semantic classification. AUC results are shown for binary attribute classification using continuous attribute value predictions. Our AVFS-U model competitive with the semantically trained attribute recognition model baselines, despite not having been trained on semantic labels.
2014), OpenImages MIAP (MIAP) (Schumann al., 2021). CFD, FFHQ. CelebA (Liu et al., 2015), and Casual Conversations (CasCon) (Hazirbas et al., 2021). Continuous attribute value predictions are computed the same manner as in the prototypicality experiment. Results are shown Figure 5. We observe that AVFS-U dimensions are competitive with semantically trained recognition models, even on challenging unconstrained data (e.g., COCO, MIAP).
Attribute disparity estimation. Next we investigate the utility of individual embedding dimensions for the task of comparing dataset attribute disparities. Suppose we are given n subsets (Si,-.. ..) sampled from S*, where, Ve E [1..., n). Te E [0, 0.01, .... 0.99, 1] and 1 denote the ground- truth proportion of S. = {(x,, y:) 4: labeled y = 0 and y = 1, respectively, We aim to find c with the lowest ground-truth attribute disparity, i.e., arg min, (c)) = arg min, abs(2r. 1).
We define sim(i. = abs(ū; úÚ) as the similarity between x; and x,. where 9 R is a predicted continuous attribute value for ground- y {0, 1} given manner as in the prototypicality and semantic classification experiments. Let K E mmm denote similarity matrix, where denotes the number of images and K,, sim(i, j). The average similarity of samples in a set S.,. is x E; E3 K, (Leinster & Cobbold, 2012). We estimate e as
computed in the same
follows: arg min, Â(c) = arg min, |S. -S.1-2 E. Ej K.,- In all experiments, we set n 100 and m 100. For S", we use the following labeled face datasets: COCO, MIAP, CFD, FFHQ, CelebA, and CasCon. Averaging over 100 runs, we report (1) A A(arg min, ((c)); and (2) Spearman's between (Â(1). ((n)) and
Results are shown in Figure 6. Despite not being trained semantically labeled data, observe that AVFS-U dimensions are competitive with the baselines, in particular AVFS-U disparity scores highly correlated with ground-truth disparity scores.
6 DISCUSSION
Motivated by legal, technical, and ethical considerations associated with semantic labels, we intro- duced AVFS new dataset of 3AFC face similarity judgments over 4,921 faces. We demonstrated the utility of AVFS for learning a continuous, low-dimensional embedding space aligned with human perception. Our embedding space, induced under novel conditional framework, not only enables the accurate prediction of face similarity, but also provides a human- inter--itterpettabll decomposition of the dimensions used in the human-decision making process, and the importance distinct annotators place on each dimension. We further showed the practicality of the dimensions for collecting continuous attributes, performing classification, and comparing dataset attribute disparities.
Since AVES only contains 0.003% of all possible triplets over 4,921 faces, future triplets could be actively sampled (Lindley, 1956; MacKay, 1992) as learn additional face-varying dimensions
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Sett idtntilied label
MVFIUU
Celes
FairFace
FFHO
Model was trained the test daza
CFD: "mwel""
CasCors "male"
CelebA: "mwle"
COCO:
MWNP: "male"
FFHQ: "maale"
3.05
0.05
0.10
U.S5
0:00
0:10
5.00
0.06
0.00
0.C6
0.89
0.69
0.60
0.60
"Ce '70+"
FFHC: '70*"
Caleb: "smtinng
CFD: "hagoy"
CCFD: "east asian"
GRD: "Bleck"
0.20
9:15
]
0.10
2.05
0.10
10
0.05
A+
0.08
2.00
0.95
0.98
0.30
0.80
0.80
0,88
0.88
068
0:98
1.0
CosCon: stin"
CDCD: 1ihht skin
"alding
0.04
0.20
0.30 ]
1.00
0.10
0.02
010
0.0
]
0.00
0.88
018
0.80
0.80
0000
098
1.00
Figure 6: Attribute disparity estimation. Average attribute disparity (y-axis) in data subsets selected as the most diverse and average Spearman's 's r (æ-axis) between the ground-truth disparity for each subset considered and model-estimated disparity scores. Results highlight that disparity scores derived using AVFS-U model dimensions are highly correlated with ground-truth disparities.
using larger stimulus sets. For example, Roads & Love (2021) selectively sample from an approximate posterior distribution using variational inference and model ensembles. However, similar to Zheng et al. (2019)'s MDS approach, judgments are pooled, which disregards an individual annotator's decision-making process. As we have shown, annotators are (at least our task) not interchangeable and are influenced by their sociocultural backgrounds. Future work could therefore combine our model of conditional decision Docision-making with active sampling, e.g., seeking at each iteration unlabeled triplets that maximize expected information gain conditioned on the annotator (or annotator group) that will be assigned the labeling task.
Our work is not without its limitations. First, we implicitly assumed that the stimulus set was diverse. Therefore, our current proposal is limited to the proof of concept that we can learn face-van face-varying dimensions that are both present within the stimuli and salient to human judgments of face similarity, Our stimulus set was sampled such that the faces were diverse with respect to inferred demographic subgroup labels. Ideally, these labels would have been self-reported. With the principle of data minimization in mind, a potential solution would be to collect stimuli from unique individuals that differ in terms of their country of residence, providing both identity- and geographic- baased label diversity. Notably, country of residence information additionally helps to ensure that each image subject's s data is protected appropriately (Andrews et al., 2023). Second, the number of (near) nonzero dimensions depends on the sparsity penalty weight, i.c., 0ij in Equation (4), which must be carefully chosen. We erred the side of caution (lower penalty) avoid merging distinct factors, which would have reduced interpretability. Third, the emergence of "smiling expression" and "neutral expression" dimension indicate that annotators did not always follow our instruction to ignore differences in facial expression. While this may be considered a limitation, our model was able to reveal the use of these attributes, providing a level of transparency not offered by existing face datasets. Finally, our annotator pool was demographically imbalanced, which is an unfortunate consequence of using AMT.
Despite these limitations, we hope that our work inspires others to pursue unorthodox tasks for learning the continuous dimensions of human diversity, which do not rely on problematic semantic labels. Moreover, we aim increase discourse annotator positionality. As the philosopher Thomas Nagel suggested, it is impossible to take a "view from nowhere" (Nagel, 1989). Therefore, we need to make datasets more inclusive by integrating diverse set of perspectives from their inception.

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ETHICS STATEMENT
AVFS contains annotations from a demographically imbalanced of annotators, which may not generalize to a set of demographically dissimilar annotators. Moreover, while the stimulus set of images for which the annotations pertain to were diverse with respect intersectional demographic subgroup labels, the labels were inferred by combination of algorithms and human annotators. (Refer to the dataset documentation in Appendix for details on the stimulus set and annotator distributions.) Therefore, the stimulus set is not representative of the entire human population and may in actuality demographically biased. Although we provided evidence that models trained on AVFS generalize novel stimuli, it is conceivable that the models could perform poorly faces with attributes that were not salient to human judgment behavior on triplets sampled from, or represented in, the stimulus set of images. Future work could explore active learning as reveal additional attributes that are relevant to human judgments of face similarity and expanding the stimulus set.
Furthermore, the stimulus set (sampled from the permissively licensed FFHQ dataset) collected without image subject consent. Reliance on such data an overarching problem in the computer vision research community, due lack of consensually collected, unconstrained, and diverse human-centric image data. To preserve the privacy of the image subjects, all face images presented in this paper have been replaced by StyleGAN3 generated faces with similar embedding values.
To demonstrate that AVFS judgments encode discriminative information, we validated our learned dimensions in terms of their ability to estimate attribute disparities, as well as predict binary attributes and concept prototypicality. Our results show that discriminative attribute classifiers can be distilled solely from learning predict similarity judgments, underscoring that we do not need to explicitly collect face attribute labels from human annotators. Note however, for social constructs (e.g., gender, race) and emotions, we do not condone classification, as it reifies the idea that an individual's 's social identity internal state can be inferred from images alone.
REPRODUCIBILITY STATEMENT
Data and code are publicly available under Creative Commons license (CC-BY-NC-SA), permitting noncommercial use cases at https: https:///githuae com/ S aaa_e/eewwwweeeeeeeeeeeeeeeeeeee from somewhere.
ACKNOWLEDGMENTS
This work funded by Sony Al.
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A IMPLEMENTATION DETAILS
A.1 FACE ALIGNMENT
For each face dataset used in this paper. faces were detected and aligned following the procedure outlined by Or-El et al. (2020), which utilizes the dlib face detector and 68 landmark shape predic- tor (King. 2009).
A.2 AVFS MODELS
Aligned face images were resized to 128 x 128. For training, standard data augmentation was used, ie.. horizontal mirroring and 112 x 112 random cropping, and image values were normalized to [-1, 1]. Training performed using batch size of 128 on single Tesla T4 GPU.
For the unconditional AVFS models, guided by the validation loss, we empirically set a = 0.00005 and 02 0.01 using grid search. The conditional models used the same hyperparameters with the additional loss term's 's weight set to 0g = 0.0001. All models were optimized for 40 epochs with Adam (Kingma & Ba, 2014) (default parameters) and learning rate 0.001.
Post-optimization, for each trained model, we obtained a core set of dimensions by removing dimensions with maximal value (over the stimulus set) close to zero, resulting in a low-dimensional space. For the AVFS-U trained model, this resulted in 22 dimensions. The threshold for removing dimensions for all models was determined based maximizing accuracy the validation set. For the AVFS-U model, across five independent runs, the 95% confidence interval (CI) for the number of dimensions and validation accuracy were 27.4 and 61. $1.990.11% 1%, respectively. Figure shows that the judgments can be encapsulated using a limited number of the available dimensions and that the 22 dimensions are approximately replicated across the independent runs.
Note that the AVFS-2AFE model was trained using triplet margin with distance swap loss (Balntas al., 2016) for 40 epochs with Adam (Kingma & Ba, 2014) (default parameters) and learning rate 0.001.
A.3 BASELINE MODELS
Aligned face images were resized to 128 x 128. For training, standard data augmentation was used, i... horizontal mirroring and 112 112 random cropping, and image values were normalized to [],, Training was performed using batch size of 512 across 4 Tesla T4 GPUs.
FairFace models. We trained face attribute recognition models using SGD for 45 epochs on the FairFace dataset. The initial learning rate 0.1 was divided by 10 at epoch 15, 30, and 40. L weight decay and SGD momentum were 0.0005 and 0.9, respectively. The FairFace models were trained to predict age, gender, and ethnicity.
CelebA models. We trained face attribute recognition models using SGD for 45 epochs on the CelebA dataset. The initial learning rate 0.1 was divided by 10 at epoch 15, 30, and 40. L weight
Validation accuracy
Pearson's
65
1.0
1.0
Accurasy
65
Peanson p
0.8
Spearman
0.8
45
0.8
0.8
128
22
22
Dimensions retained
Dimension
Dimension
Figure 7: AVFS-U model training across independent runs. (a) Validation accuracy as function of the number of dimensions retained over five independent runs (95 % CI), empirically evidencing that the judgments can be encapsulated using a limited number of dimensions. (b) Pearson and (c) Spearman's r between each of the 22 dimensions and the best matching dimension from each of the other four runs (95 % CT), empirically evidencing the reproducibility of the dimensions.
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decay and SGD momentum were set to 0.0005 and 0.9, respectively. The CelebA models were trained to predict 40 binary attributes.
FFHQ models. As FFHQ is unlabeled dataset, we used age and gender labels inferred by human annotators from the EFHQ-Aging dataset. Ethnicity labels were inferred using the pretrained FairFace model described above. We trained the FFHQ models predict the inferred age, gender, and ethnicity labels using SGD for 45 epochs. The initial learning rate 0.1 was divided by 10 at epoch 15, 30, and 40. L weight decay and SGD momentum were set 0.0005 and 0.9, respectively.
CAblA-WebFace models. We trained several face recognition models on the
dataset. The models differ only in terms of the loss minimized, i.e., softmax, ArcFace (Deng et al., 2019), CasFace (Wang et al., 2018), and SphereFace (Liu et al., 2017). Models were trained using SGD for 55 epochs. The initial learning rate 0.1 was divided by 10 epoch 15, 30, and 40. L weight decay and SGD momentum were set to 0.0005 and 0.9, respectively.
For the additional baseline models used in Appendix B, we obtained publicly available model weights for sulervise and object recognition models. Aligned faces were preprocessed based on the model used.
Self-supervised models. We used the following pretrained self-supervised models:
Self-supervised model trained using the SWAV (Caron et al., 2020) framework on Ima- K (Russakovsky et al., 2015)-an object recognition dataset of 1.3M images over 1K object classes, where an estimated 17% of the images contain at least one human face (Yang et al., 2022). Model weights can be found at https: : /ightb.......gggggggggggg..... com/ facebookreeearcch swav.
Self-supervised models trained using the SwAV framework IG-IB (Goyal al., 2022) dataset of 1B uncurated Instagram images, containing millions of images of humans. Model weights can be found at https: /ttp::///gttiae wtt.:///aecccbboeeseeer....o ma i teeinn/poo ptwpppooeetceeeeee SEER.
Self-supervised models trained using the SwAV, MoCo-v2 (Chen al., 2020), and DINO (Caron et al., 2021) frameworks on PASS (Asano et al., 2021)-22 dataset 1.3M images without people. Model weights can be found at https: / i thub. com com/yuke yuk imasimas sano/PASS.
Object recognition models. We used object recognition model from PyTorch pretrained using softmax loss on ImageNet I (Russakovsky et al., 2015).
B EXTENDED EXPERIMENTAL RESULTS
For FairFace, CelebA, and FFHQ models, we additionally consider unnormalized class logit embed- dings (e.g., denoted CelebA-L), as well logit embeddings converted to binary vectors based on class predictions (e.g., denoted CelebA-BL. -LL). All baseline embeddings are normalized to unit-length, where the dot product of two embeddings determines their similarity. Results are shown Table 1.
C DATASET DOCUMENTATION
The questions answered in this dataset documentation were proposed by Gebru et al. (2018).
C.I MOTIVATION
C. FOR WHAT PURPOSE WAS THE DATASET CREATED?
Few datasets contain self-identified sensitive attributes, inferring attributes risks introducing additional biases, and collecting attributes can carry legal risks. Besides, categorical labels can fail to reflect the continuous nature of human phenotypic diversity, making it difficult to compare the similarity between sabella faces. Motivated by these legal, technical, and ethical considerations, the dataset was created enable research on learning human-centrie face embeddings aligned with human perception, as well as studying annotator bias. The dataset does not contain problematic semantic face attribute labels, rather it consists of face similarity judgments, where each judgment
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Table 1: Predicting similarity. (Same Stimuli) Accuracy over 24,060 judgments and Spearman's between the entropy in human- and model-generated triplet odd-one-out probabilities. (Novel Stimuli) Accuracy over 80,300 judgments and Spearman's 's between the entropy human- and model-generated similarity matrices. Note that #Labels is approximate of the number of labels collected to create dataset accounting for consensus.
Sume Stimali
Novel Stimall
DaneMadell
Los//Pramework
#linages
#labess
Architecture
Acc.
Acc.
Cross-cstropy
1.3M
>1.3
Reghet-32GF
46.6
0.05
41.7
0.32
FairFace
Cross estropy
62K
372K
ResNeti8
55.9
0.41
51.9
0.67
FaitFace
ente-e
62K
372K
ResNeti8
52.9
0.25
18.6
0.51
PairFace-LL.
Cross-catopy
372K
ResNetil
47.7
Celeba
Cross estropy
178K
7.1M
ResNet18
52.1
0.25
48.9
0.56
ColdtA-L
Cross-estopy
17SK
7.1M
ResNer18
53.2
0.15
49.6
0.59
CelebA-BL
Cross-cttopy
175K
7.1M
ResNeti8
FPHQ
estropy
199K
RosNat8
FHHQ-L
Croes-cstoopy
70K
ResNa118
53.5
0.31
50.5
0.62
РHHQ-B BL
Cross-catrop
70K
ResNett8
51.0
0.23
47.3
0.54
CASIA WebFaace
ArcFace
404K
404K
ResNeti8
51.6
46.1
0.40
Croes-catoopy
404K
404K
ResNar8
CASIA WebFace
SphereFaze
404K
404K
ResNet18
48.3
0.25
43.8
0.35
CAStA-WehFiee
CosFiee
404K
404K
ResNar18
44.0
0.12
40.8
0.23
AVIS-C
Conditional Equation (4)
5K
S74K
ResNet18
67.4
0.68
61.7
0.82
AVIS CPH
Conditionll Equation (4)
5K.
574K
ResNeti8
Caconditional Equation (4)
5K.
ReNNa18
57.5
AYES-U-SOS
Laconditional Equation (4)
5K.
287K
ResNert8
61.3
0.61
55.8
0.81
AVES USSS-U-255
Laconditooall Equation (4)
5k.
144K
ResNeti8
60.6
0.56
35.3
0.80
AVES-U --1-50-12.5%
Unconditional Equation (4)
5K.
72K
ResNet18
0.47
55.0
0.79
AVPS-2APC
Triplet margia with swap (Balntas 2016)
5K.
574K
ResNeri8
ImageNNlKK
ResNa150-05
JG-IB
SwAV
IB
RegNat-32GE
47.2
0.18
44.7
0.45
1G-IB
SWAV
IB
0
RegNet-64Gr
48.1
0.16
44,4
0.45
1B
SWAY
18
RegNet 128GF
46.8
0.40
IB
RegNet-256C7
PASS
McCo-v2
1.M
ResNet50
42.3
0.09
40.5
0.27
PASS
SWAY
1.2
0
ResNet30
42.4
0.12
40.4
0.21
PASS
DINO
1.3M
VITS-16
43.2
0.10
41.8
0.32
is accompanied by both the identifier and demographic attributes of the annotator who made the judgment.
C.1.2
WHO CREATED THE DATASET AND ON BEHALF OF WHICH ENTITY?
The dataset was constructed by the authors Jerone T. A. Andrews, Przemyslaw Joniak, and Alice Xiang.
C.1.3
WHO FUNDED THE CREATION OF THE DATASET?
The dataset was funded by Sony AI.
C.2 COMPOSITION
C.2.1
WHAT DO THE INSTANCES THAT COMPRISE THE DATASET REPRESENT?
The instances comprising the dataset represent:
Three-alternative forced choice (3AFC) triplc-judggment tuples, where a judgment corresponds to the least similar (i.c., odd-one-out) face in triplet of face images according to human annotator. Refer to Figure 8 for an illustrative example of the 3AFC task presented to annotators.
Human-generated topic labels that capture the meaning of set of learned embedding dimen- sions. Refer Figure for an illustrative example of dimension labeling task presented to annotators.
Human- image ratings along a of learned embedding dimensions. Refer Figure 10 for an illustrative example of the image rating task presented to annotators.
All instances in the dataset are associated with the unique identifier and demographic attributes of the annotator who performed the annotation. The identifiers were randomly generated (to preserve
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penson other people, vig diferences head positio, flccial expressiion, lighting, accessorries, backgrourd di déjects.
Gontest
Tripkt
Figure 8: Odd-one-out 3AFC task. Annotators were tasked with choosing the person that looks least similar to the two other people. Context makes salient context-related properties and the extent to which faces being compared share these properties. By exchanging a face a triplet (i.e., altering the context), each judgment implicitly encodes the dimensions relevant to pairwise similarity. All face images in the figure were replaced by synthetic StyleGAN3 (Karras et al., 2021) images for privacy reasons; annotators were shown real images.
Which viuua onaractonisiccs do you think the people on the loft hand side of grid compar people right-fand grid?
90
75
50
25
Figure 9: Dimension labeling task. Illustrative example of a face image grid for model dimension j shown to annotators, displaying the stimulus set of 4,921 faces sorted descending order (from left to right) based their dimension embedding value. Annotators were tasked with writing 1-3 descriptions based on the characteristics they thought were changing along the grid. Annotators were instructed view and interact with the entire grid before providing their descriptions. Consistent descriptions from distinct annotators would suggest that the ordering of the grid is visually meaningful. All face images in the figure were replaced by synthetic StyleGAN3 images for privacy reasons; annotators were shown real images.
the privacy of the annotators) by the authors and the demographic attributes correspond to the self-reported age, nationality, ancestry, and gender identity of an annotator.
C.2.2 How MANY INSTANCES ARE THERE IN TOTAL?
The dataset contains:
188,180 quality 3AFC instances for training and validation; and 24,060 80,300 quality-controlled 3AFC instances for testing.
738 quality- embedding dimensions.
topic labels that capture the meaning of a set of learned
8,800 controlled human- gentan-generaee image ratings along a set of learned embedding dimensions.
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Decice
their aimilrit to the poople shown in each colume.
100
99
90
75
50
25
Figure 10: Image rating task. Illustrative example of face image grid for model dimension shown to annotators, displaying the stimulus set of 4,921 faces sorted in descending order (from left right) based on their dimension embedding value. Annotators were tasked with deciding which column of the grid a novel face (shown above the grid) should be placed on based on its similarity to the faces in the column. Annotators were instructed view and interact with the entire grid before making their decision. As annotators were not primed with the meaning of a grid, accurate placement would indicate that the ordering of the grid is visually meaningful. All face images in the figure were replaced by synthetic StyleGAN3 images for privacy reasons; annotators were shown real images.
C.2.3 DOES THE DATASET CONTAIN ALL POSSIBLE INSTANCES OR IS IT A SAMPLE OF INSTANCES FROM A LARGER SET?
Face image stimuli. Starting from the 70,000 FFHQ (Karras et al., 2019) images, the stimulus set of 4,921 face images was constructed by:
1. Removing faces with an absolute head yaw or pitch angle > / left or right eye occlusion score > 20, or eyewear label. This was done to reduce the influence of head pose on human behavior and more easily permit eye comparisons.
2. Removing face images with an age label > 19 years old, so as to exclude images of minors.
3. Categorizing the remaining faces into 56 intersectional demographic subgroups, which were defined based on ethnicity, age, and gender labels.
4. Randomly sampling face images (without replacement) such that each subgroup would be represented in the stimulus set. This was done to ensure that the stimulus set would at least be diverse with respect to demographic subgroup labels. Moreover, face images were randomly sampled from subgroup, as opposed to selecting the most confidently labeled, avoid biasing the stimulus set toward stereotypical faces.
Figure 11 shows the intersectional group counts for the 4,921 face images in the stimulus set.
An additional set of 56 images and set of 20 images were sampled from FFHQ for the test set partition of 80,300 3AFC instances and 8,800 human- generreed image rating instances, respectively. Both of these sets of images were sampled in the same manner as the stimulus set. In addition, they are disjoint from each other and the stimulus set of 4,921 faces. Figures 12 and 13 show the intersectional group counts for the set of 56 images and set of 20 images, respectively.
Head yaw and pitch angles, eyewear, and eye occlusion labels were obtained from the FFHQ- Aging (Or-El et al., 2020) dataset, which Or-El al. inferred using Face++. Note that FFHQ-Aging comprises of labels for each face image contained in FFHQ. Age and gender labels were also obtained from the FFHQ-Aging dataset, which Or-El al. inferred by employing a group of human annotators to annotate the face images. Ethnicity was estimated using a pretrained FairFace model (Appendix A).
3AFC instances. The training and validation set of 638,180 quality--cotrollly tuples contains 638,180 180 unique triplets, i..., a single judgment per unique triplet. The judgments were obtained from 1, 1,645 eligible annotators. Figure 14 shows the demographic group counts of the
3AFC triplet-judgment
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30
10R
111
112
111
86
42
"Back 20-23 fama
"stook 20 26 male"
Tslaok, 30 00--00 mok
"Back 4Q-40 fama
Talook 40-43, make"
BBack 60 1202200 fema
mo
"oast asiin, 20-20, muie
"oust ssian, 30-30 femma
"eaee anter 40-40, man
"wast araan 50 120, feema
"Mads asian 30-1 120, mase
"ndaan 20-20 fema Trndon,
"ndoo 30-31 feem
Trodkn 30 make
"ndain 40-40 fama
fimm
"nslian 50 120 mave
"atto hisaanio 20-20, famake
Tudiro Napanik, 20-20 ma
"east seren 20-211
Toast 30-30
Tindlnn 40
"attno hisparis, 30 30-29
154
8
"atnoo hospanc, 40 45 Semale"
"laino hisponic 60-40 mile"
'inddoo maskum 20-20 famull"
Tiidds "inicee costen 2028. make"
30-29, female"
mmddde eastem, lemale"
tesiell eallen 40-40, mule"
"middi unstem, 50-120 120 femula"
soudheest ssten 20-20 male"
"eouthont oaber, 30 30-50 femmle"
"sothaast usian $0-30, make"
"pouthcast cston, 40 60-20 female"
"souheentt ooran, 50 120, femmle"
"wothaoe weten 50 120, mode"
"Whiko, 20 00-20 fomale"
Whill 20.20 male"
Whith 30-38 male"
"whik 40 40 femmle"
"hite 40-40, mile"
20-29:
Whil, 50 120
"uttoo topaniic
"nidlle
Figure 11: Stimulus set intersectional subgroup counts. Intersectional subgroup counts for the 4,921 face images contained in the stimulus set. There are 56 subgroups represented in the stimulus set.
"blckk, 20-2, femala"
"bicok 20-30 remate"
"back 30-39, male"
Tblrkk 10-40, female
"hoca 40 d0 made"
"blck, 50 120, male"
aslin, Gamall"
"cast oslan, 20 25, hale"
"cast oxtin, 20-30 remale"
"eart 30 hale
"oust ooinn 50-120 make
Trndann 20-23, remale
"indar 20-25 29, male"
Tosten Gemmae
"ndiar 40-40 male"
Tetno tespano, 30-30, ter ate
"laina hispaiii hisparii 50 50 make"
"rndun, 50- 120, fomaa
totoo rappmee 4Q-0, rema
Teeto heppore 20 20,
heparee,
"attoo tepanic, 50 120 male"
"mocde eattern 20 26, mole"
"midde weteen 10,00 bemsee"
Tedodle castom 40-40 male"
"middee exstem, CD- 120, make"
sean temsse
"aalbaus caln 40-40 fomale"
"suuhouzt asian 40-49, make"
"souhroast 60- fomale
"iit 20-20 make
"wnka, 50-35, lomale
Wiil 50-39, make"
"wiie 40-40 male"
"whin, 50 720 temser
"odiks 50- 120 mode"
"Tatno Hopone 40-49,
"eicdle 30-00
sean,
poutteenttoo
"iidda
Figure 12: Novel stimuli test set intersectional subgroup counts. Intersectional subgroup counts for the 56 face images used in the test set partition of 80,300 quality-controlled 3AFC juudmet-jugmmen tuples. There are 48 (from a possible 56) subgroups represented in the 27,720 unique triplets.
annotators as well as the distribution of annotator contributions. 3 The quality-controlled instances
"Note the abbreviations: "africa" ("af"", "north africa" ("afna")) "east africa" ("afea"), "middle africa" ("afmaa") "south africa" ("afsa"), "western africa" ("afwa""), "americas" ("am"), "caribbean" ("ame")," "central
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"aboo, 26, male
"Howk 60-40, femm
"ooss asian, 20-25, ma
"oast do on 30-39 temo
temo
aodees, mas"
"nnd an 40-40 tama
"atno Nupanic, 20 ma
"atnoo teepanic, 30, mam
"midde modemn 40-00 mas
"moddo eastem, 50-1 120 mus"
"souhhosst 80-40, remale
mo
"east 20.30 00-36,
"oast aaa, 20-20,
"slock
"alino hiparic
"alnoo
Figure 13: Rated image set intersectional subgroup counts. Intersectional subgroup counts for the 20 face images used for collecting human-gent suman-generated image ratings along the 22 learned embedding dimensions. There are 18 (from a possible 56) subgroups represented.
Table 2: Annotator exclusion policies and criteria. 3AFC annotations from annotators who provided overly fast, deterministic, incomplete judgments were excluded.
Policy
Criteria
Min. Ajudgments
Fist #1
25% julgments generated in <. 0.8s
100
Foot #2
50% of julgments gesentad in <, 1.1s
100
Deteministic
40% judgments coerespond a single niplet position
200
Incomplete
Submission empty julgment
represent a subset of instances from a larger set of 703,300 3AFC instances. The 703,300 triplets were sampled from (4.91) possible triplets (i.e., over 4,921 face images contained in the stimulus set). Instances were not included in the subset of 638, 180 3AFC instances if they were annotated by any annotator who provided overly fast, deterministic, or incomplete judgments. Refer the annotator exclusion policies outlined in Table 2.
The test set partition of 24,060 quality-controlie 3AFC triplet-judgment tuples contains 1,000 unique triplets with 22-25 25 unique judgments per triplet. The judgments were obtained from 355 eligible annotators. Figure 15 shows the demographic group counts of the annotators as well as the distribution of annotator contributions. The quality-cottrolled instances represent a subset of instances from larger set of 25,000 3AFC instances with 25 unique judgments per triplet. The 1,000 triplets were sampled from (4.221) possible triplets (i.e., over 4,921 face images contained in the stimulus set). Within the 1,000 iriplets. 2.163 face images from the stimulus set of 4,921 face images were used. The 1,000 triplets are disjoint from the 703,300 triplets sampled for the training and validation set. Instances were not included in the subset of 24,060 3AFC instances if they were annotated by any annotator who provided overly fast, deterministic, or incomplete judgments. Refer to the annotator exclusion policies outlined in Table 2.
The test set partition of 80,300 quality-controlled 3AFC triplet-judgment tuples contains (56) 27,720 unique triplets, representing all possible triplets that could be sampled from 56 images. Each triplet has 2-3 judgments from 632 eligible annotators. Figure 16 shows the demographic group counts of the annotators as well as the distribution of annotator contributions. The quality- instances represent subset of instances from larger set of 83,160 3AFC instances with 3 unique judgments per triplet. Instances were not included in the subset of 80,300 3AFC instances if they were annotated by any annotator who provided overly fast, deterministic, or incomplete judgments. Refer to the annotator exclusion policies outlined in Table 2.
Dimension topic label instances. The 738 quality-controlled human-generated topic labels were obtained for 22 learned embedding dimensions, representing a subset from 128 dimensions. Figure 17 shows the learned embeddings with the highest frequency topic labels generated by the annotators.
america" ("amea"), "south america" ("amasa"), "northern america" ("amna"), "asia" ("as"), "central asia" ("asca"), "eastern asia" ""asea"), "south-eastern asia" ("assea"), "southern asia" ("assa")), "western asia" ("aswa"), ),,, "europe" ("eu"), "eastern europe" ("euea"). "northern europe" ("eune"), "southern europe" ("euse"), "westem curope" ("euwe"), "oceania" ("oe"), "australia and new zealand" ("ocanz"), "melanesia" ("ocme"), "micronesia" ("ocmi"") and "polynesia" ("ocp").
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Ancedit
Gender deniity
Nationllty
A48
SYD SE3
15
21
"un""
150-120
Themaa
"ma
"canada"
"Heedd"
"gumme"
"ndda"
an ot ou
"am
110
Subregjions ancestry
"amnn
"amraa EUO0
Temms mITH mowe
"anna cutte
"arrae
"oors"
"saoa"
"ass0"
"ouaa ourd"
"oure, euse
Pumm mane mowd"
"anne Gowd
"ouso,
Distribtion annctuter contributins
Feqquency
1000
Annotations
Figure 14: 3AFC training and validation set annotator demographics. Annotator age, gender identity, nationality, ancestry, and subregional ancestry group counts for the 1,645 eligible annotators who contributed to the training and validation set of 638, 180 quality-controlled 3FFC triplet-judgment tuples. The distribution of per annotator contributions is also shown. Note that for nationality, ancestry, and subregional ancestry the plots only show groups with 2 5 annotators.
The subset of dimensions were selected the basis of their maximal observed value (over the stimulus set of 4,921 faces) being sufficiently larger than zero. The topic labels were obtained from 84 eligible annotators. Figure 18 shows the demographic group counts of the annotators as well as the distribution annotator contributions. Each the 22 dimensions has 23-58 topic labels. The quality-controlled topic labels correspond to words and those that were derogatory or offensive. The filtered responses were then converted into 35 topie labels: "age", "age related", "ancestry", "ear shape", "emotion expression", "eye color", "eye related", "eye shape", "eyebrow related", "eyebrow shape",, "face related", "face shape", "facial expression". "facial hair". "forehead shape". "gender expression", "hair color", "hair length". "hair related", "hair texture", "hairstyle", "head shape". "health related". "lips related", "lips shape", "mouth related", "mouth shape". "neck related", "nose related", "nose shape", "skin color". "skin related", "skin texture". "teeth related", and "weight related".
arrived at by first removing annotator responses that did not
Dimension rating labels. The 8,800 quality-controllen
mener-eeee image ratings were obtained for 22 learned embedding dimensions, representing a subset from 128 dimensions. The subset of dimensions were selected on the basis their maximal observed value (over the stimulus set 4.921 faces) being sufficiently larger than zero. The ratings were obtained from 164 eligible annotators. Figure 19 shows the demographic group counts of the annotators as well as the distribution of annotator contributions. Quality control corresponded to excluding incomplete responses, i.c., empty responses. The 8,800 ratings represent all of the ratings collected from annotators, i.c., all collected ratings were nonempty.
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Age
ideniity
Nationality
Nocestry
7Z 215
15--200
"eemste
hembmary
"ammntaan
Tainuaar
"ondhar""
"om
Bubragion Aabrgginaal ancostry
Distribution of annotator contributions
14
100
0
50
Nnna curo. Cune"
"anno, DUNe
"euto
buno
Annotations per
Figure 15: 3AFC same stimuli test set annotator demographics. Annotator age, gender identity, nationality, ancestry, and subregional ancestry group counts for the 355 eligible annotators who contributed the set partition of 24.060 comtrly-c 3AFC triplet-judgment tuples. The distribution of per annotator contributions is also shown. Note that for nationality, ancestry, and subregional ancestry the plots only show groups with 2 annotators.
Age
Gender ddanity
Nationalty
Ancestry
250
350
231
120
141
"Samale"
"maee"
hoar banay
Samencae
"ongloo"
Trencn
'al am our
Tal our
"tmm
150-120
Subregional
144
Diemibution annotator budions
Frequeccy
80
resporne
Tand
"amiaa
Tomia, 0l0e
"ass0a""
Teuten erme mime
"baaoo ouae
"o000, Baae"
"oune"
1000
"suee""
"ou00,
2000
400
000
800
Apmotations per annorator
Figure 16: 3AFC novel stimuli test set annotator demographics. Annotator age, gender identity, nationality, ancestry, and subregional ancestry group counts for the 632 eligible annotators who contributed to the test set partition of 80,300 quality-controlle 3AFC triplet-judgment tuples. The distribution of per annotator contributions is also shown. Note that for nationality, ancestry, and subregional ancestry the plots only show groups with 2 5 annotators.
C.2.4 WHAT DATA DOES EACH INSTANCE CONSIST OF?
Each 3AFC instance consists of a triplet of face image identifiers, 3AFC (i.e., odd-one-out) judgment, and annotator identifier (corresponding the annotator who generated the topic label).
Each human-gennne generated topic label instance consists of a dimension identifier, topic label, and annotator identifier (corresponding the annotator who generated the topic label).
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Diiss 1/22: "south asiar"
Tong lace"
Dim. 3122: "wnkke""
Dim, 4:22 "leminine"
"smilng exp
Dim. 6/22; "massollin"
Their
"lacill eep..
esp..
cill exp
"nose atape"
"age reltted
Wineeetry
remotion
"anceelty"
Indien: "busshy rysbroows tatino hisparic
"polnty neee". "wiraative' "middle caston""
"white" hair
"brond heir. brown
Temmw:
"weering ipatide: Theasy makeup"
"amlligg bones, "asy chicks"
Trigh
ghoek:
"mmlle "sideburne"; Ibo
7122: "nuural
11/22
asian"
Trose related" Macial exp... 1a anncestyy
"ancestry" Skkin related
"anoestry". "skin Thair longth
"age relatee", hair cofor
"anosstry',
Tade shape? "weight Intedt Tosall pop.
"akin color". "anoestryy "N dial exp.
wwaziing necitte" "al". beard"
"gra
Teast osbn
asion
soouheast
"blakk; 'oig lppi. Tolg nose"
Trihh checkbores: "double "smiling"
70w 190-69r, 'gray hair"
"east asaan "southeast asann, "Straight
13/22: "amiiina"
Dim. 14/22: bakcing"
Dim 1512: "young""
16/22 "biack"
17:20: "adial hail"
Dim. "baldingg
gender osp.. "nair color" eop
"annder exp.. "asial hal"" lenght
"age rolated esp
"anoostry",
skn shape"
color"
"anoestry',
ponder exo".
Taclal exp..
Thail Bergh"" "pender pap.. "ancwetry"
Temmate:: wworinn lipstick makeup"
"bake" gray hair", "weering
"slond hair".
10-a",
"pale
track Tegg kost, "big nose"
wideruum"" "gnatee""
Spaa halr" "weering nockide"
Dim, 19/22: "east asian"
20/22: widerly"
Dim, 21/22: "iide face"
Dim, 2022 White
Nakin color"
"age
relered.
"ancasty";
shape",
"anoestry":
"skincooo" "wrcesky",
"yney
"chubby""
"Monnd
osion".
pale skin"
trigh chezkoones"
har
Figure 17: Dimensional interpretability. (a) Our interpretation an AVES-U dimension with the highest scoring stimulus set images. (b) Highest frequency human-generated topic labels. (c) CelebA and FairFace generaggeerrated labels, exhibiting a clear correspondence with human-gan -eman-generated topics. Face images in this figure have been replaced by synthetic StyleGAN3 images for privacy reasons.
Nationally
Age
Gender derliyy
Ancsoliry
"50-300 40040
Teemae
"amettcan
"howrloo""
"chnoge
"eng
"ehoopor"
"pemann
"oale
"nndan
Iistinn
1thuamann
"sponte"
"Meenames""
rant
Samaa
"ssa"
"sune"
tam
cont butions
Frequnncy
8
88
Annotationa per annotator
Figure 18: Topic labeling annotator demographics. Annotator age, gender identity, nationality, ancestry, and subregional ancestry group counts for the 84 eligible annotators who contributed 738 topic labels that capture the meaning of the 22 learned embedding dimensions. The distribution of per annotator contributions is also shown. Note that for nationality, ancestry, and subregional ancestry the plots only show groups with 2 5 annotators.
Each human- generated image rating instance consists of dimension identifier, image identifier, image rating, and annotator identifier (corresponding to the annotator who generated the topic label).
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Age
Identhy
Natonality
Wnocotryy
"ammliian"
"brazlinn"
tanddar
"ongiton"
Subregional ancestry
Disonbution of annotator commibutions
20
Frequescy
100
TO noregpanie
"Auni, ButOW
"sus""
"utoo"
Annotations
Figure 19: Image rating annotator demographies. Annotator age, gender identity, nationality, ancestry, and subregional ancestry group counts for the 164 eligible annotators who contributed 8,800 human-generated image ratings along the 22 learned embedding dimensions. The distribution of per annotator contributions is also shown. Note that for nationality, ancestry, and subregional ancestry the plots only show groups with annotators.
Note that each annotator's ' identifier is associated with their demographic attributes (i.e., age, gender identity, nationality, and ancestry).
C.2.5 IS THERE A LABEL OR TARGET ASSOCIATED WITH EACH INSTANCE?
Each 3AFC instance's target is a 3AFC judgment.
Each human- gennaateeee topic label instance's target is a topic label.
Each human-generated image rating instance's is target is an image rating.
C.2.6 Is ANY INFORMATION MISSING FROM INDIVIDUAL INSTANCES?
No.
C.2.7
ARE RELATIONSHIPS BETWEEN INDIVIDUAL INSTANCES MADE EXPLICIT?
Yes. Each instance can be mapped to the annotator who labeled it.
C.2.8
ARE THERE RECOMMENDED DATA SPLITS?
The 3AFC instances are split into training, validation, and testing partitions. All other instances are for evaluating learned model dimensions, i.e., for the model introduced in the paper.
C.2.9
ARE THERE ANY ERRORS, SOURCES OF NOISE, OR REDUNDANCIES THE DATASET?
No.
C.2.10 IS THE DATASET SELF-CONTAINED, OR DOES LINK TO OTHERWISE RELY ON EXTERNAL RESOURCES?
The dataset does not include any images, it instead references to image identifiers. The image identi- fiers can be used to obtain the relevant FFHQ images, which are hosted on NVIDIA Corporation's Google Drive: https: //g thub. com/
datteet..et......
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C.2.11 DOES THE DATASET CONTAIN DATA THAT MIGHT BE CONSIDERED CONFIDENTIAL?
The dataset includes the demographic information (i.e., age, nationality, ancestry, and gender identity) of each annotator; however, all annotators consented to the collection, use, and publication of their data.
C.2.12 DOES THE DATASET CONTAIN DATA THAT, IF VIEWED DIRECTLY, MIGHT BE OFFENSIVE, INSULTING, THREATENING, OR MIGHT OTHERWISE CAUSE ANXIETY?
No.
C.2.13 DOES THE DATASET RELATE TO PEOPLE?
The dataset contains annotations corresponding to human judgments of face similarity, as well as demographic information about each annotator.
C.2.14 DOES DATASET IDENTIFY ANY SUBPOPULATIONS?
The dataset includes the demographic information (i.e., age, nationality, ancestry, and gender identity) of each annotator.
C.2.15 IT POSSIBLE TO IDENTIFY INDIVIDUALS, EITHER DIRECTLY OR INDIRECTLY FROM THE DATASET?
Each annotator's Amazon Mechnical Turk (AMT) identifier was replaced by an identifier randomly generated by the authors. The dataset therefore contains de-identified data.
C.2.16 DOES THE DATASET CONTAIN DATA THAT MIGIIT BE CONSIDERED SENSITIVE WAY?
The dataset includes the demographic information (i.e., age, nationality, ancestry, and gender identity) of each annotator.
C.3 COLLECTION PROCESS
C.3.1
HOW WAS THE DATA ASSOCIATED WITH EACH INSTANCE ACQUIRED?
All data associated with each instance was acquired via AMT, except for the image identifiers which were obtained from the FFHQ dataset: https: //:::///iii/ hgthhub...... com/ NV1abs/ / ffhq- dataset.
C.3.2 WHAT MECHANISMS OR PROCEDURES WERE USED TO COLLECT THE DATA?
The AMT API was used to collect the data associated with each instance.
C.3.3
IF THE DATASET SAMPLE FROM LARGER SET, WHAT THE SAMPLING STRATEGY?
Refer Appendix C.2.3.
C.3.4 WIO WAS INVOLVED IN THE DATA COLLECTION PROCESS AND HOW WERE THEY COMPENSATED?
The authors collected the annotations directly from annotators via AMT. All annotators were required to have previously completed > 100 human intelligence tasks (HITs) AMT with > 95% approval rating. Eligibility was further determined through a prescreening survey, which included a short multiple-choice English language proficiency test. Since we placed restriction annotator location, in order to be eligible, we required annotators to answer at least two out of three multiple- choice English language proficiency questions correctly.
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Annotators were paid nominal fee of 0.01 USD for completing a prescreening survey. For all subsequent tasks, annotators were compensated at rate of 15 USD per hour, regardless of whether their work passed quality control checks.
C.3.5 OVER WHAT TIMEFRAME WAS THE DATA COLLECTED?
From 6 November 2021 to 20 February 2022.
WERE ANY ETHICAL REVIEW PROCESSES CONDUCTED? No.
C.3.6
C.3.7 DID YOU COLLECT THE DATA FROM THE INDIVIDUALS IN QUESTION DIRECTLY, OR OBTAIN IT VIA THIRD PARTIES OR OTHER SOURCES?
All data was collected directly from the annotators via AMT.
C.3.8 WERE THE INDIVIDUALS IN QUESTION NOTIFIED ABOUT THE DATA COLLECTION?
Yes. Annotators were provided with (1) an information sheet and consent form; and (2) notification and consent for collection and use of study data form.
C.3.9 DID THE INDIVIDUALS IN QUESTION CONSENT TO THE COLLECTION AND THEIR DATA?
Annotators consented the collection, use, and publication of their age. gender identity, nationality, ancestry, and task responses.
C.3.10
IF CONSENT WAS OBTAINED, WERE THE CONSENTING INDIVIDUALS PROVIDED A MECHANISM TO REVOKE THEIR CONSENT IN THE FUTURE OR FOR CERTAIN USES?
Annotators were instructed to contact Sony Europe B. V. Taurusavenue 16, 2132LS Hoofddorp. Netherlands or privacyoffice SEu3sony com to revoke their consent in the future or for certain uses.
C.3.11 HAS AN ANALYSIS OF THE POTENTIAL IMPACT OF THE DATASET AND ITS USE ON DATA SUBJECTS BEEN CONDUCTED?
A data privacy impact assessment was conducted.
C.4 PREPOOCESINGCCLEANNNIIIEEEEE
C.4.1 WAS ANY PHEROOCESING/CCEENNNGG/AAA
OF THE DATA DONE?
To control for quality, any annotator who provided overly fast, deterministic, incomplete responses were not included in the dataset. In addition, labels for model dimensions collected from human annotators were cleaned and converted topic labels.
C.4.2
WAS THE "RAW" DATA SAVED IN ADDITION TO THE PRIPROCCSEED/CLLEAR////AAAAE
DATA?
The authors retain a copy of the raw data.
C.4.3 IS THE SOFTWARE THAT WAS USED TO PEEROOCES//CLAA/LLERRR THE DATA AVAILABLE?
No.
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C.5 USES
C.5.1 HAS THE DATASET BEEN USED FOR ANY TASKS ALREADY?
In the paper, the dataset was used to concurrently learn a face embedding space aligned with human perception well annotator-specific subspaces. The combination of the learned embedding space and annotator-specifie subspaces not only enabled the accurate prediction of face similarity, but also provided
intereeatteeeee decomposition of the dimensions used in the human decision- making process, as well as the importance distinct annotators placed on each dimension. The paper demonstrated that the individual embedding dimensions (I) are related concepts of gender, ethnicity, age, well as face and hair morphology; and (2) can be used to collect continuous attributes, perform classification, and compare dataset attribute disparities. The paper further showed that annotators are influenced by their sociocultural backgrounds, underscoring the need for diverse annotator groups to mitigate bias.
C.5.2
Is THERE REPOSITORY THAT LINKS TO ANY ALL PAPERS OR SYSTEMS THAT USE THE DATASET?
Refer https: wwt.ht.t......
C.5.3 WHAT (OTHER) TASKS COULD THE DATASET BE USED FOR?
The dataset could used for any task related to human perception, human decision- manoing processes, metric learning, active learning, bias mitigation, or disentangled representations.
C.5.4 IS THERE ANYTHING ABOUT THE COMPOSITION OF THE DATASET OR THE WAY IT WAS COLLECTED AND PREROCCESED/CLEANEDLLL.EEE THAT MIGHT IMPACT FUTURE USES?
The dataset contains annotations for images contained in the FFHQ dataset of Flickr images. The images currently have permissive licenses that allow free use, redistribution and adaptation for noncommercial purposes, however Flickr users are free to request the removal of any of their images contained in the FFHQ dataset by visiting https: /github..... com/ www/cwwm//c//eeeee NVlabs/ dath-datasst.
Moreover, the dataset contains annotations from demographically imbalanced set of annotators, which may not generalize to set of demographically dissimilar annotators. Furthermore, while the stimulus set of images for which the annotations pertain to were diverse with respect intersectional demographic subgroup labels, the labels were inferred by a combination of algorithms and human annotators. Therefore, the stimulus set is not representative the entire human population and may in actuality be demographically biased.
C.5.5 ARE THERE TASKS FOR WHICH THE DATASET SHOULD NOT BE USED?
The dataset created for noncommercial research purposes only. The dataset intended for, and should not be used for, development or improvement of facial recognition technologies.
C.6
DISTRIBUTION
C.6.1 WILL THE DATASET BE DISTRIBUTED TO THIRD PARTIES OUTSIDE OF THE ENTITY ON BEHALF OF WHICH THE DATASET WAS CREATED?
The dataset can be accessed by scmewhere.
from
C.6.2 How WILL THE DATASET DISTRIBUTED?
The dataset can be accessed by visiting https: // /ighhb....... com.SonyAATa..vveee..n.. somewhere.
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C.6.3
WHEN WILL THE DATASET BE DISTRIBUTED?
May 2023.
C.6.4 WILL THE DATASET BE DISTRIBUTED UNDER A COPYRIGHT OR OTHER INTELLECTUAL. PROPERTY (IP) LICENSE, AND/OR UNDER APPLICABLE TERMS OF USE (ToU)?
The dataset is distributed under a Creative Commons BY-NC-SA license: creativecommont org/ cccees/by-ccaaaaa
https://
C.6.5 HAVE ANY THIRD PARTIES IMPOSED IP-BASED OR OTHER RESTRICTIONS ON THE DATA ASSOCIATED WITH THE INSTANCES?
No.
C.6.6 Do ANY EXPORT CONTROLS OTHER REGULATORY RESTRICTIONS APPLY TO THE DATASET OR TO INDIVIDUAL INSTANCES?
The authors are not aware of any controls or restrictions.
C.7 MAINTENANCE
C.7.1
WHO WILL BE SUPPORTINGHOSTTNGGAANNAAANN
THE DATASET?
The dataset is supported by the authors and can be accessed by visiting ht com/ SenyAI/a_view____ httpa//a_wiee__froo_oooeeeeeeee
C.7.2
How CAN THE CURATOR OF THE DATASET BE CONTACTED (E.G., EMAIL ADDRESS)?
Correspondence jerone andrews@sony. com.
C.7.3 Is THERE AN ERRATUM?
If errors found, an erratum file will be added the dataset.
C.7.4 WILL THE DATASET BE UPDATED?
The dataset will be versioned.
C.7.5 IF THE DATASET RELATES TO PEOPLE, ARE THERE APPLICABLE LIMITS ON THE RETENTION OF THE DATA ASSOCIATED WITH THE INSTANCES?
No. However, annotators free withdraw their consent at any time and for any reason.
C.7.6 WILL OLDER VERSIONS OF THE DATASET CONTINUE TO BE SUPOORTEDHHOSTEEMMAAAAAAEEEE
Older versions of the dataset will continue to be hosted, unless they contain information about, or obtained from, annotator who has subsequently withdrawn their consent.
C.7.7 IF OTHERS WANT TO EXTEND/AUGMENT/EUILA ON/CONTRIBUTE TO THE DATASET, IS THERE A MECHANISM FOR THEM TO SO?
Others should contact the authors they wish to contribute to the dataset.
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Reviews:
[{"review_id": "eNOmJL2YnZ", "reviewer": "Reviewer_pZsn", "summary": "", "questions": "", "limitations": "", "rating": 6, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "main_review": "", "summary_of_the_paper": "This paper introduces a way to implicitly learn a set of continuous face-varying dimension with weak human annotations. By introducing a novel large-scale novel dataset, authors have shown the learned implicit face embeddings are human-interpretable and related to gender, race, age, as well as face and hair morphology categories. ", "summary_of_the_review": "This paper introduces a way to learn human-centric face representations. By introducing a novel dataset with weak human annotation, the learned embeddings are proved to be human-interpretable and related to gender, race, age, as well as face and hair morphology categories. The proposed topic of will contribute to the evaluation of data diversity and the dataset introduced in this paper will contribute to the community. Therefore, I recommend this paper to be accepted. ", "strength_and_weaknesses": "Strength:\nThe proposed topic of human-centric face representation is interesting, and the dataset introduced in this paper will contribute to the community.\nWeakness:\nIn this paper, the proposed embedding is based on a weak human annotation (choose the person that looks least similar to the two other people, a.k.a, odd-one-out). How is the proposed embedding different from the self-supervised learning-based face embedding?\n", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {"correctness": "4: All of the claims and statements are well-supported and correct.", "technical_novelty_and_significance": "4: The contributions are significant, and do not exist in prior works.", "empirical_novelty_and_significance": "4: The contributions are significant, and do not exist in prior works."}, "extra_text": {}}, {"review_id": "PTNiRlKyjp", "reviewer": "Reviewer_L52N", "summary": "", "questions": "", "limitations": "", "rating": 5, "confidence": 5, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "main_review": "", "summary_of_the_paper": "This paper proposes a new face representation learned from similarity judgments and contributes a novel annotation dataset with 4900 faces  (a subset of FFHQ dataset). The main purpose of this representation is to allow us to measure sample diversity without explicit annotations. In this context, the diversity is defined mainly on demographic basis, i.e. race, gender, and so on. The annotation was made on sample triplets where the annotators were asked to choose a least-similar example out of 3 samples. The representation is learned by maximizing the similarity between the other two examples, with an annotator-specific weight vector, which is also learnable. Experiments show that the representation captures important demographic dimensions such as race, gender and also other ones such as smile, which the authors tried to avoid to learn. ", "summary_of_the_review": "I think this paper has a good potential and value. In my opinion, it's not fully demonstrated. In particular, Sec 3.6 needs to be expanded further. Competing baselines for this paper are not classifiers. I think they should be clustering, scaling, diversity sampling methods, etc. \n\nIn addition, I disagree with some statements like \"we do not encode, reify, or propagate stereotypes\". Even though the authors didn't ask the annotators to \"use\" stereotypes, there is no way to enforce it. The paper already shows that the annotators heavily relied on gender and race to define similarity. We categorize faces based on those dimensions. It's a known fact. (it may be interesting to show the amount of variance in annotations that can explained by gender age race) \n\nAlso, \"Implicit in these annotations is the assumption that human annotators can consistently and objectively map observable characteristics to categorical labels.\" -> there's no assumption like this in prior work. It is well understood that annotators are biased and subjective. That's why these annotations are almost always obtained from multiple annotators. In any case, I don't clearly see how this proposed method can be safe from these issues because again, the evaluation shows the annotators did use the same signals. This can be clarified better or toned down. On the other hand, there's a clear benefit to not annotate these attributes (legal, privacy issues). I think it may be better to focus on that direction in motivation. \n\n", "strength_and_weaknesses": "(+) Overall it's on an interesting and important topic.\n(+) The paper is relatively well written and lots of details are provided in appendix. \n(-) But I suggest to move important details to the main body, for example, how you estimate attributes to make intersectional groups to sample from FFHQ. It was not clear that the sample subset was balanced, which would be problematic because FFHQ is unbalanced. \n(+) New dataset and applicable model\n\n(-) My main reservation is validation. The paper shows many experiments, but everything from 3.1 - 3.5 is more of basic verification. Only 3.6 is about the demonstration of the utility of this dataset. As the primary contribution of this paper is a new dataset, I expect the authors show a lot more justifications on its usefulness than what was done in 3.6. \n\nMost experiments (especially 3.6) focus on showing that the learned representation (each dimension) corresponds to demographic attributes such as race and gender. This is not surprising. This doesn't make the proposed dataset useful. If we just do k-means clustering, you will also discover these groupings automatically. So why do we need these new annotations? Is the primary goal of this paper to discover gender, race, age without annotating them directly on target data? If so, I think there are much cheaper ways. Why don't we just use a classifier to estimate these labels? While the paper argues that they want to avoid this, but the way the proposed representation is used and validated is pretty much the same (i.e. it has separate dimensions for these attributes). In other words, this demographic information is not \"distributed\", so I don't know if this is very different from other classifiers. \n\nAlso, 3.6 only uses CC and CFD. Both are very clean datasets. Will this work for in-the-wild dataset such as fairface? \n\nAnother question is that the representation still captures non-sensitive attributes such as smiling. The authors explicitly tried to avoid this. But it's still learned, which means the annotators still used expression. \n\nOverall, I think the paper will benefit significantly from (1) having a clear discussion about how humans perceive and define similarity and (2) designing and performing evaluations aligned with that discussion. The current paper focuses only on gender, race, etc, and I don't see much benefit of this paper over explicit classifiers. \n", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {"correctness": "3: Some of the paper’s claims have minor issues. A few statements are not well-supported, or require small changes to be made correct.", "technical_novelty_and_significance": "3: The contributions are significant and somewhat new. Aspects of the contributions exist in prior work.", "empirical_novelty_and_significance": "2: The contributions are only marginally significant or novel."}, "extra_text": {}}, {"review_id": "3tcLXbw0-c", "reviewer": "Reviewer_r72x", "summary": "", "questions": "", "limitations": "", "rating": 8, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "main_review": "", "summary_of_the_paper": "In this paper, the authors proposed a method for implicitly learning continuous face-varying dimensions, without the need of asking an annotator to explicitly categorize a person. The result can be used in auditing datasets for diversity. The authors also proposed FAX, a novel dataset of 638,180 face similarity judgments over 4,921 faces. This dataset can be of interest for researchers from multiple disciplines.", "summary_of_the_review": "The paper has good contributions, though its actual efficacy in real application is not very clear. ", "strength_and_weaknesses": "Strength: \n\nThe presentation of this paper is clear. The design of the dataset and the experiment is introduced in great details. \n\nThe authors have provided detailed supplementary materials to support the main paper. \n\n\nWeakness:\n\nIt is not very clear to me how the proposed method is used for auditing dataset diversity, and how well/effective such audition will be, in terms of boosting the performance of related computer models. I wonder the correlation between human perceptual face similarity and objective machine classifications. \n\nThe previous studies on face similarity should be mentioned and discussed, for example, the following papers:\n\n\nSomai, Rosyl S., and Peter JB Hancock. \"Exploring perceived face similarity and its relation to image-based spaces: an effect of familiarity.\" Journal of Vision 21.9 (2021): 2149-2149.\n\nSadovnik, Amir, et al. \"Finding your lookalike: Measuring face similarity rather than face identity.\" Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops. 2018.\n\nThe authors are honest in pointing out several limitations. The efficacy of this work is based on the hypothesis that the proposed stimuli set is sufficiently diverse and there is no ideal way to prove this. Perhaps the authors can tune down their claim a bit. \n\n It is not clear to me why the authors chose the “odd-one-out” in triple group paradigm, though the authors have mentioned earlier work using the similar method. Why not use ranking, or “odd-one-out” in a quadruple group?\n", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {"correctness": "3: Some of the paper’s claims have minor issues. A few statements are not well-supported, or require small changes to be made correct.", "technical_novelty_and_significance": "3: The contributions are significant and somewhat new. Aspects of the contributions exist in prior work.", "empirical_novelty_and_significance": "3: The contributions are significant and somewhat new. Aspects of the contributions exist in prior work."}, "extra_text": {}}]

Author rebuttal:

Decision: Accept: poster