Personalized Feature Translation for Expression Recognition: An Efficient Source-Free Domain Adaptation Method
Authors: Masoumeh Sharafi
Soufiane Belharbi
Muhammad Osama Zeeshan
HOUSSEM Ben Salem
Ali Etemad
Alessandro Lameiras Koerich
Marco Pedersoli
Simon L Bacon
Eric Granger
Venue: ICLR 2026 Poster (2026)
Keywords: unsupervised domain adaptation
source free
personalization
videos
facial expression recognition
domain translation
Primary area: unsupervised, self-supervised, semi-supervised, and supervised representation learning
TL;DR: we propose a new efficient source free domain adaptation method for personalization using feature-based translation for facial expression recognition in videos.

Abstract: Facial expression recognition (FER) models are employed in many video-based affective computing applications, such as human-computer interaction and healthcare monitoring. However, deep FER models often struggle with subtle expressions and high inter-subject variability, limiting their performance in real-world applications. To improve performance, source-free domain adaptation (SFDA) methods have been proposed to personalize a pretrained source model using only unlabeled target domain data, thereby avoiding data privacy, storage, and trans- mission constraints. This paper addresses a common challenging scenario where source data is unavailable for adaptation, and only unlabeled target data consisting solely of neutral expressions is available. SFDA methods are not typically designed to adapt using target data from only a single class. Further, using models to generate facial images with non-neutral expressions can be unstable and computationally intensive. In this paper, the Source-Free Domain Adaptation with Personalized Feature Translation (SFDA-PFT) method is proposed for SFDA. Unlike current image translation methods for SFDA, our lightweight method op-
erates in the latent space. We first pre-train the translator on source domain data to transform the subject-specific style features from one source subject into another. Expression information is preserved by optimizing a combination of expression consistency and style-aware objectives. Then, the translator is adapted to neutral target data, without using source data or image synthesis. By translating in the latent space, SFDA-PFT avoids the complexity and noise of face expression generation, producing discriminative embeddings optimized for classification. Using SFDA-PFT eliminates the need for image synthesis, reduces computational overhead, and only adapts a lightweight translator, making the method efficient compared to image-based translation. Our extensive experiments on four challenging video FER benchmark datasets, BioVid, stressID, BAH, and Af-Wild2, show that PFT consistently outperforms state-of-the-art SFDA methods, providing a cost-effective approach that is suitable for real-world, privacy-sensitive FER applications. 
Our code is publicly available at: github.com/MasoumehSharafi/SFDA-PFT.

Published as a conference paper at ICLR 2026
PERSONALIZED FEATURE TRANSLATION FOR EXPRESSION RECOGNITION: AN EFFICIENT SOURCE- FREE DOMAIN ADAPTATION METHOD
Masoumeh Sharafi', Soufiane Belharbi', Muhammad Osama Zeeshan', Houssem Ben Salem', Ali Etemad Alessandro Lameiras Koerich², Marco Pedersoli', Simon L Bacon 3.4 & Eric Granger*
LIVIA, Dept. of Systems Engineering, ETSS Montreal, Canada
LIVIA,, Dept. of Software and IT Engineering, ETS Montreal, Canada
8 Dept. of Health, Kinesiology, & Applied Physiology, Concordia University, Montreal, Canada
Montreal Behavioural Medicine Centre, CIUSSS Lel--ee-''l-de--Moonee
Canada
5 Deptt. of Electrical and Computer Engineering, Queen's 's University, Kingston, Canada
Imasoumeh. sharefi. 1, muhemmad-csame zeeshan. sta.mmmmmm.mmmm...es............ houssem, seteem.be-ssaleee allem..1 Bens ctamtl.ca
ssoufiane beiherbi,, marco. marco.pederee pedersled,,,,,, alessandro. korraidh..koerichh koerich, eric. grangege detteerr.)etttt....m al attemadgqueensu.co ca, somon, bow.oencccooordia.ccc.
ABSTRACT
Facial expression recognition (FER) models are employed in many video- based affective computing applications, such as human-computer interaction and health- care monitoring. However, deep FER models often struggle with subtle expres- sions and high inter-subject variability, limiting their performance in real-worke applications. To improve performance, source-free domain adaptation (SFDA) methods have been proposed to personalize a pretrained source model using only unlabeled target domain data, thereby avoiding data privacy, storage, and trans- mission constraints. This paper addresses a common challenging scenario where source data is unavailable for adaptation, and only unlabeled target data consist- ing solely of neutral expressions is available. SFDA methods are not typically designed adapt using target data from only single class. Further, using models to generate facial images with non-neutral expressions can be unstable and com- putationally intensive. In this paper, the Source-Free Domain Adaptation with Personalized Feature Translation (SFDA-PFT) method is proposed for SFDA. Unlike current image translation methods for SFDA, our lightweight method op- erates in the latent space. We first pre-train the translator on source domain data transform the subject-specific style features from one source subject into an- other. Expression information is preserved by optimizing a combination of ex- pression consistency and style-aware objectives. Then, the translator is adapted neutral target data, without using source data image synthesis. By trans- lating in the latent space, SFDA- PFT avoids the complexity and noise of face expression generation, producing discriminative embeddings optimized for clas- sification. Using SFDA-2FT eliminates the need for image synthesis, reduces computational overhead, and only adapts lightweight translator, making the method efficient compared to image-based translation. Our extensive experiments on four challenging video FER benchmark datasets, BioVid, Stress ID, BAH, and Afff-Wildd show that SFDA SFDA-PFT consistently outperforms state-of-the-at SFDA methods, providing a cost-effective approach that suitable for roal-world, FER applications. Our code is publicly available at: hullub.cco/MaaommmeeSSSaiiSAAPPP
INTRODUCTION
FER plays an important role in video-based affective computing, enabling systems to interpret the emotional or health states of humans through non-verbal cues (Calvo & D MeMloo 2010 Ko

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Source feature
Source Feature Space
Target Feature Space
Torgel Translator
Source classifier
SFDA-II
SEDA-PFT (Ours)
Target subject
Imuge Translation
image style
100
82.46
75.54
> Prediction
80
57.2
60
(a) Personalization of Image Translation (SEDA-II)
40
Target subject
Feature Translaionn
Source dumein feanire style
20
5.76
3.6
0
Prediction
Accuracy (%)
Params (M)
FL.Pps (G)
(b) Our Personalizatonn of Feature Translation (SFDA-PFT)
comparison between standard image translation, SFDA-IT (Hou & Zheng 2021a against SFDA-PFT BioVid data.
Figure I:
(a) Image translation methods operate at the pixel level and require complex mappings to align the target and source styles. (b) Our SFDA-PET method di- rectly translates in the source feature space, allowing for efficient personalization. (right) Accuracy, parameter counts, and FLOPs at inference highlight the trade-offs between the two approaches, with models implemented using a ResNet- -18 backbone.
2018). Its applications range from tuman-computer interaction (Pu & Nie 2023). to health moni-
toring GGay--Morey et al., 2025), and clinical assessment of pain, depression and stress (Calvo &
D D'Mello 2010). Despite recent advances in deep learning (Barros et al. 2019 Sharafi et al. [022]
2023) and the availability of large annotated datasets for training (Walter et al. 2013) Kollias & Zafeiriou 2019), deep FER models may perform poorly when deployed on data from new users and operational environments. This is due to the mismatch between distributions of the training (source domain) data and the testing (target operational domain) data. Beyond variations in capture condi- tions, data distributions may differ significantly across subjects. Inter-subject variability (Zeng et al., 2018 Martinez, 2003) can degrade the accuracy and robustness of deep FER models in real-worll applications (l.i & Deng. 2020a Zhao et al. 2016).
To improve performance, this paper focuses on subject-based adaptation or personalization of deep FER models to video data from target subjects. Various unsupervised domain adaptation (UDA) methods have been proposed to address the distribution shifts by aligning feature distributions (Li & Deng 2018 Zhu et al., 2016; Chen al. 2021 Li & Deng 2020b). However, they typically re- quire access to labeled source data during adaptation, constraint that is often infeasible in privacy- sensitive application areas like healthcare due to concerns for data privacy, data storage, and com- putation costs. This has led to the emergence of source-free domain adaptation (SFDA). where adaptation of a pretrained source model is performed using only unlabeled target data (Liang al., 2020 Tang et al. 2024 Guichemerre et al. (024).. These methods (Fang et al. 2024 [i et al. 2024) can broadly categorized into (I) model-based approaches, which adapt the model parameters, using target domain statistics or pseudo-labels, and (2) data-based approaches (focus of this paper), which instead operate the data level by translating target images into the source domain style, enabling inference through the frozen source model without modifying its parameters.
Sfate-of-the-an SFDA methods assume access data from all target classes, which is not practical in real-world FER applications. Indeed, person-specific data representing non-neutral expressions is typically costly or unavailable. A short neutral control video may, however, collected for target individuals and used to personalize a model to the variability of an individual's 's diverse expressions. In practice, collecting and annotating neutral target data for adaptation generally easier and less subjective than gathering non-neutral emotional data. Recent work has employed GANs generate expressions based on neutral inputs but relies on image-level disentanglement of identity and expres- sion, which is often unstable and computationally expensive (Sharafi al.) 2025). This limitation reduces the effectiveness of model-based adaptation or fine-tuning strategics, particularly those re- lying on pseudo-labeling, since labels for neutral data available during adaptation. However,
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data-based strategies translate target data into the source domain style. This avoids adapting param- eters of the source classifier and enables direct inference with the frozen source model, improving stability, efficiency, and privacy. Following this direction, some SFDA methods (e.g., SFDA-IT) leverage generative models to translate target inputs into source-style images, guided by the source model (Hou & Zheng 20)1a/b). However, these methods not adapted for subject-specific adap- lation of FER models, They consider the source as single domain and often suppress important subject-specific cues for personalized FER. They also depend on expressive target data, which rarely available in practice, making generative training infeasible in limited-data settings.
To address the limitations of image translation methods for SFDA, we introduce the Source-Free Do- main Adaptation with Personalized Feature Translation (SFDA-PFT) method that explicitly models subject-specifer variation within the source domain. SFDA-PFT a conceptually simple yet effec- tive feature translation method for source-free personalization in FER. The key idea is to pre-train a translator network that maps features from one source subject to another while preserving the un- derlying expression. This subject-swapping objective encourages the model to capture intra-class, inter-subject variability, learning the structural relationship between expression and identity-peccific features within the source domain. During adaptation, only small subset of the translator's 's pa- rameters is fine-tuned to translate the style of the target subject, enabling stable and cost-effective personalization. Figure | (left) illustrates the difference between image-based and feature- -baed translation. Image-level methods (Figure [a)) generate target images in the source style, relying on complex generative models that introduce instability and high computational overhead. In contrast, Figure[[b shows that SFDA-PFT translates target features directly toward the closest source sub- ject, preserving expression without pixel-level synthesis. The complexity comparison in Figure [] (right) shows that SFDA-PFT achieves higher accuracy while requiring up to 100x fewer parame- ters and 17x fewer FLOPs than SFDA-IT, highlighting its efficiency and suitability for deployment.
Our contributions. (1) We propose a personalized feature translation (SFDA-PFT) method for SFDA in FER using only target images with neutral expressions. Unlike image translation methods that require expressive target data and generative models, our approach translates features across subjects while preserving expression semantics. Adaptation is performed in the feature space with only a small subset of parameters and significant reduction in computational complexity. (2) Style-aware and expression consistency losses are proposed to guide the translation process with- out requiring expressive target data. Our method only requires few neutral target samples for lightweight adaptation, introduces no additional parameters at inference time, and ensures stable and cost-effective deployment. (3) An extensive set of experiments is provided on four video FER bench- marks, Bivii (pain estimation), St ressID (stress recognition). BAH (ambivalence--hesitany recognition), and Aff-Wil 1 (basic expression classification). Results show that our SFDA- achieves performance that is comparable to or higher than state-of-the-an SFDA (pseudo-labeling and image translation) methods, with lower computational complexity.
2 RELATED WORK
2.1 FACIAL EXPRESSION RECOGNITION
FER aims to identify human emotional states from facial images or video sequences. To enhance generalization, UDA methods (Feng al. 2023 Cao al., 2018 Chen et al., 2021: Ji al. 2019
[] & Deng. 20205) and multi-source domain adaptation (MSDA) techniques (Zhou et al. 2024h
align distributions between source and target domains using unlabeled target data. While effec- tive, these approaches typically require access to source data during adaptation. Personalized FER methods (Yao al. 202T; Kollias al. 2020) adapt models to individual users but rely on labeled data per user. More recent subject-
adaptation frameworks (Zeeshan et al., 2024; 2025b ab treat each subject as domain and adapt across users, yet still depend on source data. These con- straints motivate the need for SFDA, which enables model personalization without accessing source samples, offering a more practical solution for privacy-sensitive FER applications.
2.2 SOURCE-FRES DOMAIN ADAPTATION AND PERSONALIZATION
SFDA addresses privacy, computational and storage concerns by adapting a pre-trained source model to unlabeled target domain without access source data. Common model-based strate-
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gies include self-supervised learning
Yang et al. 202 Litrico et al.
2023 pseudo-labeling (Liang
eet al. 2020). entropy minimization Liang et al., 2020). and feature alignment via normalization
auxilaary modules (Li et al. 2016
Liang et al. 2022 Kim et al. 2021b). SHOT (Liang al.
2020) and DINE (Ling et al. 2022) exemplify efficient adaptation via classifier tuning or Batch- Norm statistics. However, these methods often assume confident predictions and smooth domain shifts, which are frequently violated in FER due to high inter-subject variability and subtle expres- sion differences. FER-specific adaptations such as CluP (Conti et al. 2022) and FAL (Zheng al., 2025) address label noise and pseudo-labe instability, yet challenges remain when only neutral tar- get expressions are available. DSFDA (Sharafi al. 2025) tackles this by disentangling identity and expression using generative models, but its reliance adversarial training and multi-stage pipelines limits scalability and robustness practical deployment.
2.3 FEATURE TRANSLATION FOR SFDA
Image translation a common data-based strategy for SFDA that maps target images into the source style using generative models, allowing frozen source models to generalize without access to source data (Hou & Zheng 2021a Kurmi et al. 202T, Qiu et al.) 202T] Tian et al. 202T; Ding et al.] 2022). While effective in general tasks, these methods face critical limitations in FER and per- sonalization. FER requires preserving subtle expression cues and identity-speciff features, which often distorted by image synthesis. Moreover, generative models computationally intensive, prone to instability, and assume access to expressive target samples, an unrealistic assumption in neutral-only personalization settings. To address these challenges, propose translating features instead of images, using a compact, self-supervised translator that maps target features into the aource-aligned space without requiring adversarial training, source data, expressive target inputs, offering a stable and efficient solution for source-free FER personalization.
3 PROPOSED PERSONALIZES FEATURE TRANSLATION METHOD
Figure 2 illustrates the overall framework of our SFDA-PP PFT method. The source model is com- prised feature extractor backbone and classifier head, both frozen during adaptation. To adapt this model to a new target subject with only few neutral images extracted from a video, we intro- duce a translator network, copy of the source encoder equipped with lightweight adaptation layers after the feature extractor. The translator is pretrained on source data using a subject-swapping
Source Pre Training
Target Adaptation
Source Sub-i
Target
Doge
Cox
Subo
Tirget
Targer
Target Inference
Expression
Feature Translator
Style Loss
Source Classifier
CaossEntaay
Frorm:
Figure 2: Overview the proposed SFDA-PFT method. (a) During pre-training, the translator T is trained to map Sub-i features into the distribution of Sub-j from the source dataset, using combination of style alignment and expression consistency losses. (b) During adaptation, only the feature translator T is updated using expression-consisteds predictions from two different images (Imagel and Image2) of the same target subject. (c) At inference time, the trained translator and the fixed source classifier C are used to predict expressions for target-domain inputs.
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translating features between source subjects while maintaining expression labels. This enables the model to capture subject-specifie information and preserve expression.
objective:
Architecture: Let Ds {(xs, ya)) be labeled source dataset, where x; is source subject and y, E y its corresponding expression label. Let D = (x, } denote the unlabeled dataset for a target subject. We denote by F the source feature extractor and by C the classifier head. The translator network is defined as the composition of followed by a set of lightweight, subject-adaptive layers T. Thus, the translator Tiall T F takes an image input and outputs a translated feature representation. The source classifier (F, C) is trained on Ds and remains frozen during adaptation. The translator is first pretrained on Ds to learn identity transformation while preserving expression, and then adapted to each target subject individually using only few samples.
3.1 SOURCE
The objective of source pre-training is twofold: first, to train a reliable expression classifier on labeled source data, and second, to initialize the translator network that can disentangle and recompose identity and expression in the feature space. This initialization is crucial because the translator will later be adapted to new subjects using only a few unlabeled samples.
Formally, the source classifier consists of a feature extractor F and a classifier head C, which are optimized on the source dataset Ds {(x.: } by minimizing the standard cross-entropy loss:
LCE Us) log (C(F(x)))
To pre-train the translator T, construct pairs of source images (x,,x) from distinct subjects. The first image X1 carries the expression that should be preserved, while the second x, provides the target identity to which the representation should adapt. Extracted features are denoted as f, = f, = FX = T(f). The translated representation f, is optimized with two comple- mentary criteria. First, expression semantics are preserved by minimizing the divergence between classifier predictions on the original and translated features:
Lax DxI. (C(f.) II
Second, the translated feature is explicitly encouraged to adopt the identity statistics of the reference subject xj. Rather than relying pixel-level synthesis adversarial identity matching, we achieve this alignment directly in feature space by matching low-order statistics of early-layer activations. Concretely, for each selected layer C, we compute the per-channel mean p(-) and standard deviation (-) of both the translated representation f! and the reference identity feature rj. and minimize their squared differences. The resulting objective:
Layle Σ ((IA())) p(f)))2 + +loo(?) -o (F)))
(3)
LEC
forces the translator reshape the distribution of f so that it reflects the identity- style of X2 while leaving expression semantics intact.
This formulation is inspired by the observation that per-channel statistics encode subject-dependent appearance cues (e.g., facial geometry, texture, or lighting) that are orthogonal to expression dy- namics. By matching only the first two moments, the translator adapts identity without overfitting to sample-specific details, thus avoiding artifacts that commonly arise in image-level translation. Crucially, this lightweight alignment in feature space both efficient and robust to noise, making a key ingredient of our method. The final source pre-training objective combines these components:
Lyource Lce + >espe Lespe Astyee Layke
where Aeepe and Aatll weight the preserving expression semantics and aligning subject identity.
3.2 TARGET ADAPTATION AND INFERENCE
Given a small set of unlabeled frames from a new target subject, the goal is to personalize the trans- lator Tha while keeping the source classifier (F, C) fixed. Adaptation is performed independently
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for each subject and updates only the lightweight adaptive layers T, ensuring efficiency and avoid- ing catastrophic interference with previously learned knowledge. Since all target samples originate from the same identity, explicit identity alignment is unnecessary; the adaptation stage thus focuses exclusively on preserving expression semantics. For each target frame x,, features are first extracted by the frozen source encoder as f. F(x.) and then transformed by the translator as fr = T(f.).
To maintain expression fidelity, we enforce consistency between classifier predictions before and after translation by minimizing the KL divergence:
Lexps = DKL (C(E)) ( C(.)..
(5)
This self-distillation objective anchors the adapted translator to the original classifier's decision boundary, ensuring that the expression information present in f; is preserved after subject-speciffc transformation. Since labels are not required, even few neutral frames are sufficient for adaptation. In practice, this enables efficient and data-light personalization that can be performed at test time without revisiting the source dataset.
Inference. After adaptation, the personalized translator Tian T F is used for recognition. For new frame x, of the same target subject, the translator maps its features into a source-aligned representation while maintaining the expression. The frozen classifier C then predicts the expres- sion from adapted features. This design allows test-time subject personalization without labels, avoids storing accessing source data during deployment, and eliminates the overhead of pixel- level translation. As result, the method provides a lightweight yet effective strategy for SFDA in FER, combining the stability of frozen discriminative models with the flexibility of subject-adaptive translation.
4 RESULTS AND DISCUSSION
4.1 EXPERIMENTAL METHODOLOGY
Datasets: In our experiments, we evaluate on four diverse facial expression datasets: Biovid (Watter et al. 2013), which contains controlled laboratory recordings of pain stim- uli: StressID (Chaptoukaev
al. 2023), which captures stress levels based on self-reports; BAH Gonzááez-Gonzálee et al. 2025). large-scale dataset for recognizing ambivalence and hesi- tancy expressions in naturalistic recordings; and A Aff-Wi 1d2 (Kollias & Zafeiriou, 2019), a widely used in-the-will benchmark for basic expression recognition. These datasets collectively cover range of domains, from controlled lab settings to real-world scenarios, and from binary (pain, stress, anbivalence/nesitanny to multi-class (seven basic emotions) classification tasks. Full dataset de- scriptions are provided in the Appendix.
Protocol: In experiments, each subject is viewed an independent target domain. In the BioVid, BAH, AfffWild2, and Stress ID datasets. Following prior works on personalization FER (Zeeshan et al. 0024:20255 Sharafi et al. 2025), evaluate the standard 10 fixed target subjects used in the literature, which span both genders and cover a range of ages, with each subject contributing hundreds to thousands of frames. This protocol enables fair comparison while ensuring that per-subject metrics are computed over large sample sizes. This subject-specific setup reflects real-world personalization scenarios and enables assessment under inter-subject variability. During adaptation, we assume access only to neutral expression data from the target subjects. No source data are available this stage, consistent with the SFDA setting. We evaluate performance under the following four settings. Source-Only. The model is trained on labeled source- source-domain data and directly evaluated on target subjects without adaptation. This serves as a lower-bound baseline, high- lighting the impact of domain shift. SFDA (msdel- The model is adapted using only neutral data from the target domain. We compare our proposed SFDA- SFDA-PFT PFT method with recent state-of- the-art SFDA methods, including SHOT (Liang et al. 2020), TPDS (Tang al.) 2024). NRC (Yang) et al. 2021. ). SEIT Hou & Zheng 2021b), SFDA-IT (Hou& & Zheng 2021a). and DSFDA (Sharafi al. 2025 SFDA (data-based). This variant incorporates our spbject-specific translation module, which aligns target features to the source domain through subject-specific adaptation. Oracle. The model is fine-tuned using labeled target-domain data, including neutral and non-neutral expressions.

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Table 1: Comparison of our proposed SFDA-PPR PFT method against state-of-the-ar SFDA methods on the Biovid dataset (10 target subjects, 77 source subjects). Bold numbers indicate the best F1.
Serting
Methods
Sub-1
Sub-2
Sub-3
Sub-4
Sub-5
Sub-6
Sub-7
Sub-8
Sub-9
Suh-10
Average
Source-ooly
Source model (no adaptation)
62.78
52.76
82.02
80.83
82.73
56.03
71.85
66.90
50.01
45.79
65.17
SHOT [iang 2020
$2.97
45.35
38.98
51.92
46,43
51.72
46. 74
42.20
47.82
SFDA
NRC
Yang et al.
2021
48.45
52.16
68.60
59.52
65.06
34.85
52.20
44,06
44.82
34.68
48.44
(mudel-basod)
TPDS
Tang di
62.26
53.16
75:23
64.79
87.06
56.14
58.20
65.84
54.34
45.79
62.27
DSPDA Sharah
1125
65.72
64.10
77:57
75.12
75.20
57.59
76.15
74.75
59.08
61.54
68.48
SFTT Hog & Zheng
1021b
76,85
65.33
78.70
80,44
87.01
$4,44
57.54
70.81
57.66
75.92
70.47
(dora based)
SRDA
SFDACTT (Hoa Zheng
X121a
71.64
63.89
84.53
80.30
86.24
59.18
77.66
72.08
54.97
67.01
71.74
SPDA-PIT (ours)
80.65
71.75
90.26
81.54
92.68
70,06
84.26
79.29
74.53
58.08
78.31
Oraole
Supervisnd fine-tuning
92.22
86.83
91. 89
92.96
91.27
87.65
85.48
90.30
93.
92.12
90.40
Table 2: Comparison of our proposed SFDA-PFT method against state-of-the-ar SFDA methods on the StressID dataset (10 target subjects, 44 source subjects). Bold numbers indicate the best F1.
Setting
Methods
Sub-1
Sub-2
Sub-3
Sub-4
Sub-5
Sub-6
Sub-7
Sub-8
Sub-9
Sub-10
Average
Source-only
Source model (no adaptation)
44.44
45.54
45.34
44.89
45.79
43.99
45.34
44.89
44.44
45.34
44.30
SHOT
[Liang et al
2020
12.66
41.79
43.52
43.09
43.95
42.22
43.52
43.09
42.66
43,52
-33.00
SFDA
NRC
Yang 1021
40.67
39.85
41.49
41.08
41.90
40.26
41.49
41.08
40.67
41.49
(mnsee-busod)
TPDS
Tuny
2024
50,10
49.08
51.11
50,60
51.61
49.59
51.11
50.60
50,10
51,11
50.50
DSPDA
Shirnii ot 5025
65.47
64.15
66.79
66.13
67.45
64.81
66.79
66.13
47
66.79
66.00
SFIT
(oy
Zhang
102
62.00
60.75
63.25
63.88
62.63
62.50
(dhia- basol)
SFDA
SFDA (Hou Zheng
63.19
61.91
64.47
63.83
65.10
62,55
64.47
63.83
63.19
64.47
63:70
SFDA-PFT (ours)
69.36
67.96
70.76
70.06
71.46
68.66
70.76
70.06
69.36
70.76
69.92
Orsele
Supervisod line-tuning
76
99.65
95.74
98.61
97.70
96.72
97.50
Table 3: Comparison of our proposed SFDA-PFT method against state-of-the-art SFDA methods on the BAH dataset (10 target subjects, 214 source subjects). Bold numbers indicate the best Fl.
Setting
Methods
Sub-1
Sub-2
Sub-3
Sub-4
Sub-5
Sub-6
Sub-7
Sub-8
Sub-9
Sub-10
Average
Source only
Source model
11.20
17.84
12.60
18.50
14.10
16.92
10.30
13:40
16.00
15.31
14.62
SHOT [Liang
20200
40.53
47.91
42.14
46.20
39.81
48.52
41.02
45.70
44.23
45.13
44.10
SPDA
NRC Ying et al 2021
48.72
42,50
46.00
44.10
41.81
47.5K
43.71
44.65
47.93
44.12
45 45.00
(model basec)
TPDS
41.22
46.30
44.01
47.82
40.95
45.53
43.29
47.18
42.23
DSFDA Sharal 2025
49.10
44.70
47.51
42,92
50.23
45,30
46.70
47,90
41.82
49.84
46.10
SFTT Hon & Zhenp
b021h
56.81
50.91
54.72
52,10
$7.54
49.82
55.91
51.23
58.12
$1.40
$2,90
SFDA
(dasa: based)
SFDA
Zheng 2021a
48.50
55.71
50.81
47.21
$4,12
49.03
52.64
51.50
SPDA-PFI (ours)
61.52
55.10
60.42
53.81
59.73
56.05
61.91
54.25
62.84
54,70
57.40
Oracle
Supervisod line- tuning
96,20
92.81
95.70
94,25
96.53
93.91
95.14
94.72
92.53
97.01
91.58
Our SFDA-PFT model was implemented using PyTorch and conducts all experiments on a single NVIDIA A A100-SXM4-40GB GPU. The source classifier built on a ResNet-15 backbone, followed by a classifier trained for binary expression recognition. Results obtained with other backbone architectures are provided in the Appendix. ResNet-15 8 was selected as the feature extractor due to its widespread adoption in prior FER and domain adaptation works. During target adaptation, only the subject- backbone and classifier remain fixed. We train the model using the Adam optimizer with learning rate of x 10-3 and a batch size of 64. A learning rate scheduler (Reducel (Reducel.ROnnlateeee RROPPlateau) was used, which monitors the validation loss and reduces the learning rate by a factor of 0.5 if no improvement is observed for 3 consecutive epochs. We set despe = 1.0 and Aayle = 0.3, giving expression preservation higher priority while allowing the style loss to act as a regularizer for identity alignment.
Implementation Details:
layers of the translator are updated. The source
4.2 COMPARISON WITH -FATE-OF-THE ART METHODS
For the lab-controlled datasets BioVid and StressID, SFDA-PFT achieves the highest F1 among all methods (Table 0 and Table 2) On BioVid, which is relatively balanced across classes, SFDA-PFT obtains an average F1 of 78.31, outprrooringg DSFDA by almost 10 points. More-
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Table 4: Comparison of our proposed SFDA-PFF method against state-of-the-ar SFDA methods on the Aff-Wild2 dataset (10 target subjects, 282 source subjects). Bold numbers indicate the best F1.
Setting
Methods
Sub-1
Sub-2
Sub-3
Sub-4
Sub-5
Sub-6
Sub-7
Sub-8
Sub-9
Sub-10
Average
Source only
Source model (no adaptation)
18.70
19.60
20.50
20.00
21.00
20.50
21.40
22.30
20.00
21.00
20.50
SHOT
long et al
2020)
33.77
34.67
35:57
35.07
36.07
35:57
36.47
57.37
35.07
36.07
35:57
SFDA
NRC
Yana
1020
35.14
36.04
36.54
36.94
57.84
36.04
(mudee-bused))
TPDS Jung
1014
36.69
37.59
38.49
37,99
38.99
38.49
39.39
60.29
37.99
38,99
38.49
DSPDA
Sharah of 2025
37.26
38.16
39.06
38.56
39.56
39.06
39.96
40.86
38.56
39.56
39.06
SFII
(Bo Zheng
1b2
50.23
50.73
51.13
52.03
(dita-hised))
SFDA
SFDA--IT (Hou Zheng 2221
49,30
50.20
51.10
50,60
51.60
51.10
52.00
52.90
50.00
51.60
51.10
BFDA-PF2 (ours)
$2.66
53.56
54.46
53.96
$4.96
54.46
55.36
56.26
53.96
$4.96
54.46
Oraete
Supervised finc-miing
91.93
92.83
93.73
95.23
94.23
93.73
94.63
95.53
93.23
94.23
93.73
over, to assess robustness across runs, we repeated SFDA-PFT training on BioVid with three independent seeds and obtained a stable average F1 of 78.43 + 0.25%, confirming low variance and consistent performance across random initializations. The main failure case is Sub-10, 10, where SFDA-PFT drops to 58.08. A closer look shows that this subject is from older age group, where pain-related facial reactions tend to be weaker and more varied. Because of this, the model strug- gles with recall, even though precision remains high. This indicates that age differences represent a challenge for personalization, consistent with prior FER studies reporting systematically lower recognition accuracy for older adults (Guo al. 2013 SÖNMEZ 2019 Kim et al. 2021a), and points to the value of age-aware or group-based adaptation strategies. On Stress TD, which is strongly imbalanced, SFDA-PFT reaches 69.92%. over 7 points higher than the best competing method, showing that it can handle skewed class distributions while still capturing subject-specific patterns. On the in-the-wild datasets BAH and Aff-Wi Wi d22 (Table 3 and Table class imbalance, noisy annotations, and uncontrolled acquisition conditions make F1 more reliable evaluation met- ric than accuracy. Here, SFDA-PFT again delivers the strongest performance, with 57.40% on BAH and 54.46% on Aff-Wilds 122, outperforming all alternatives. A key factor behind this improve- ment is that SFDA-PE operates directly in the feature space, leveraging the robust representations already extracted by the backbone. In contrast, get samples into a synthetic source domain, often introducing artifacts, blurring, or distortions that suppress subtle but critical expression cues such micro-expressions tions. These imperfections propagate downstream and degrade classifier performance. By avoiding pixel-level synthesis, SFDA-PFT preserves discriminative structures in the feature space and pro- vides more stable adaptation under the severe class imbalance and noise characteristic of real-world settings. Full accuracy results are reported in the Appendix, but we emphasize that F1 is a more informative criterion in these imbalanced scenarios.
tragge-traslationn-- based methods attempt to map tar-
localized muscle activa-
Table 5 compares SFDA-PFT with repre- sentative SFDA baselines in terms of ac- curacy and adaptation efficiency. We se- lect baselines that, like our setting, up- date only a subset of parameters instead of fully fine-tuning the network. Here, iter- ations are the number of gradient update steps during test-time adaptation, and time is the average wall-clock adaptation time per batch. Despite requiring many iterations and longer runtimes per hatch, these baselines achieve lower accuracy than SFDA-PFT. In contrast, SFDA-PFT achieves the best accuracy with far fewer updates and sub-second adaptation per batch, showing that feature-space adaptation is markedly more efficient and scalable.
Table 5: Comparison of SFDA models on BioVid d in terms of accuracy, number of iterations, and conver- gence time per batch (B=64).
Method
ACC (%)
Iters
Time (s)
SFDA DE Liung
2020
62.88
1400
65,5
TPDS
Tank onneeta ctal
2024
65.57
900
60.0
SHOT
1155
$4.0
NRC
Yang el al A121
60.31
705
75.0
SFDA-PFT (ours)
135
0.95
5 ABLATION STUDIES
Impact of Source Subject Pairing Strategies. To study the effect of source subject pairing during translator pretraining, we evaluate three strategies: random, cosine-based, and landmark-basee In the cosine-based strategy, well-classified source samples are paired based on feature similarity in the
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07.41
Random
Cosine
Landwrrk
(4)
(b)
Figure 3: Source subject pairing on the BioVid dataset. (a) Examples of random, cosine-based, and landmark-based pairs. (b) Average ACC, with landmark-based pairing performing best.
Table Average ACC (%) on BioVid us- ing SFDA-PFT, ablating expression and style losses.
Table 7: Similarity of expression and identity branches on BioVid. Fixed expr. means the same expression, different subjects, while Fixed subj. means same subject, different expressions.
Setting
A
A
Acc. (%)
No Losses
X
X
68.62
Branch
Fixed expr.
Fixed subj.
Style Loss
X
/
70.10
Expression Loss
V
X
71.60
Expression Identity
0.75
0.40
0.53
0.85
Full Loss
V
V
82.46
embedding space via cosine distance. The landmark-based strategy leverages facial geometry and pose: landmarks are aligned using Procrustes analysis, and head-pose vectors from OpenFace (Amos) et al. 2016) provide orientation cues. As shown in Figure 3 cosine- and landmark- based pairing outperform random selection, with landmark-base achieving the highest average accuracy. For the elderly (60+) subject, age-aware variant selecting younger expression-matched references improves the score by about +7%. Detailed per-subject results are provided in the Appendix.
Expression and Identity Specialization in Embeddings. We evaluate the specialization of identity and expression by computing cosine similarities for pairs with (i) the same emotion but different subjects, and (ii) the same subject but different emotions. As shown in Table the expression branch maintains higher similarity for the same emotion (0.75) compared to different emotionss (0.40), while the identity branch shows stronger similarity for the same subject (0.85) than for different subjects with the same emotion (0.53). These results show that the expression and identity branches capture primarily emotion and identity features, respectively, with some overlap between them.
Target Sample Distribution Across Source Subjects. To quantify the distribution of target sam- ples and ensure the model doesn't overfit to single source identity, use the Nearest Source Prototype Histogram. This visualization shows the cosine similarity between target embeddings and source prototypes, assigning each target sample to the closest source subject. As shown in Fig- 4 the histogram reveals diverse distribution of target samples across multiple source subjects, rather than concentrating on one. This confirms that our model avoids overfitting, promoting better generalization while preserving the variation in identities and expressions across the source domain.
Qualitative Analysis via t-SNE Visualization. Figure 5 displays t-SNE plots for two comple- mentary views: an image-based based translation where target samples are first translated in pixel space and then embedded by the frozen backbone, and feature-based translation of SFDA-PFT that operates directly latent features. In the image-based plots, translated target clusters remain no- ticeably shifted away from the source clusters, revealing larger residual domain gap. In contrast, the SFDA-PFT plots show target points tightly overlapping the source manifolds, indicating that latent-space translation induces a smaller domain shift while preserving the expression structure.
Impact of Feature Vector Size Performance We conducted ablation study to investigate the impact of feature dimensionality on the performance of feature translation across four FER datasets: BioVid, Stress ID. BAH, and Aff-Wild2. For each dataset, varied the dimen- sionality of the translated feature vector from to 512 and observed consistent improvements in accuracy with increasing dimensionality. Notably, the performance gains saturated around 256 or

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Number of Target Samples
91
81
Source Subjacts
Figure 4: Distribution of target samples for sub-1 in BioVis id dataset across source subjects
64-D
128-D
256-D
512-D
100
Neutral
90
Accunccy
Image-Based
BioVid
StressID
BAH
ARWWil22
Figure 5: T-SNE of source vs. translated fea- tures for Sub-1 in BioVid comparing feature- based (left) and image-based (right) translation.
Figure 6: SFDA-PFT accuracy across feature dimensions (64-512) on four datasets, showing performance gains with higher dimensions.
512 dimensions, suggesting that higher-dimennsonal features provide richer identity and expression information. However, the marginal gains beyond 256 dimensions diminish, indicating trade-off between representational power and computational efficiency. These trends are illustrated in Fig- ure 6 highlighting the importance of selecting appropriate feature size for effective and efficient.
6 CONCLUSION
This paper introduces SFDA -FDA-PFT, an efficient SFDA method tailored for personalization FER us- ing only image data with neutral expressions from target subjects. Unlike traditional image-based approaches that depend on expressive target data and computationally expensive generative mod- els. SFDA-PFT operates entirely in the feature space. translates features from one subject to another in the source domain by aligning subject-specific features while preserving the expression of the original subject. This allows the model maintain the expression of the input while adapting to the source subject, and to provide cost-effective personalization without requiring target expres- sion data. The SFDA-PI PFT adaptation process involves adapting only a few layers of the translator module the target subject's neutral data. SFDA PFT is computationally efficient, stable during training, and well-suited for deployment in privacy-sensitiee real-world scenarios such as healthcare mobile applications. Experiments four video FER datasets show that SFDA-PP a higher level performance with lower complexity, generalizing well across both controlled and in-the-wit wild conditions.
achieve
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ACKNOWLEDGMENTS
This work was supported in part by the Fonds de recherche du Québec Santé (FRQS), the Natural Sciences and Engineering Research Council of Canada (NSERC), Canada Foundation for Innovation (CFI), and the Digital Research Alliance of Canada.
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Appendix
This supplementary material provides additional insights and evidence supporting the main paper. includes detailed descriptions of baseline methods, algorithmic procedures, extended experiments additional datasets, ablation studies analyzing key components, and a summary of hyperparameter configurations used in our evaluations.
Algorithm Details
Source Pre-training
Target Adaptation
Baseline Methods
Extended Experimental Results
Datasets
Quantitative Comparison with SFDA Baselines
Qualitative Examples of SFDA-IT Translations
Additional Ablation Studies
Distance Closest Source Prototypes
Qualitative Analysis via t-SNE visualization
Impact of Different Backbones
Source Layer Selection for Style Transfer
Effect of Expression Loss Type
Sensitivity Analysis of Loss Weights.
Hyperparameter Details
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Algorithm Source Pre-training
1: procedure PRETRAINSOURCEEDS, C,
2: Initialize F, F.
3: for each epoch do for all (x., ya) E Ds
4:
5:
f, f Fx.)
6:
Fpred f- Cf..)
7:
LCE +- CrossEntropy Us)
8:
Update F, C using LCE
9:
end for end for
10:
Freeze F
12:
for each epoch do
for all paired (x1, (x1, (x2, Ds do
14:
fl < F(x1)
15:
f2 - F(x2)
16:
f - T(f))
17:
Leepr < DKL (C(())
Layl 5
19:
for all / E L do
20:
11, 01 -- MeanStd(f))
21:
μ2; 02 t- McanStd (22
22:
Layle + Layle + || p2ll? + ||o1 -22l
23:
end for
24:
LCE + C-rsssEntroyyCC/ff
Lioaal f- LCB + Aexpr Lexpr Aayle- Latyle
26:
Update T using Liotal
end for
28:
end for
end procedure
A ALGORITHM DETAILS
This section outlines the procedures of proposed Personalized Feature Translation (SFDA-PFT) framework for SFDA. The method comprises two stages: (1) source pre-training, and (2) target-domain. The full pseudocode is provided in Algorithms 1 and and define the main notations below.
Architecture: Let Dz ((x,, } be labeled source dataset, where x, is source subject and y. E y its corresponding expression label. Let Dy = {xx} denote the unlabeled dataset for target subject. We denote by F the source feature extractor and by C the classifier head. The translator network is defined as the composition of F followed by set of lightweight, subject-adaptive layers T. Thus, the translator Tial T F takes an image as input and outputs a translated feature representation. The source classifier (F, C) is trained on Dy and remains frozen during adaptation. The translator is first pretrained on Ds to learn identity transformation while preserving expression, and then adapted to each target subject individually using only few samples.
B BASELINE METHOD DESCRIPTIONS
We compare our proposed method against seven representative SFDA baselines. These methods span both feature-space and image-space adaptation strategies, enabling comprehensive evaluation of our approach. For fairness. all baselines are implemented using fixed ResNet-1 - backbone and evaluated under consistent experimental protocol.
SHOT [Liang et al. 2020) freezes the source feature extractor and adapts only the classifier using pseudo-labeling and information maximization, encouraging discriminative cluster- ing in the target domain without accessing source data.
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Algorithm 2 Target Adaptation
1: procedure ADAPTOTAAGETTD,,E.C.I
2: Freeze F, C
3: for each epoch do
4:
for all x₁ E Dr do
5:
f. +- F(x,)
6:
f, T(f)
7:
Lexpr < DxL (C()) C())) Update TT using Loxpr
8:
9:
end for
10: end for
11: end procedure
TPDS (Tang al. 2024
introduces a progressive adaptation framework that bridges source and target domains via series of proxy distributions, aligning predictions using category consistency and mutual information objectives.
NRC (Yang al. 2021 exploits the intrinsic neighborhood structure of the target data by enforcing label consistency among reciprocal neighbors, using memory bank for efficient retrieval.
DSFDA (Sharafi al. 2025) adapts FER models using only neutral target videos by disen- tangling identity and expression features, generating synthetic expressive data, and jointly training in one-stage framework.
SFTT (Hou & Zheng 2021b) visualizes the knowledge gap between source and target models by translating target images into source-style images using only the two model checkpoints. It employs generator guided by knowledge distillation and relationship- preserving loss, enabling adaptation and fine-tuning without source data.
SFDA-IT (Hou & Zheng. 2021a) formulates domain adaptation as an image translation problem where a generator maps target images into source-style images without paired supervision. The translated images are then classified by the fixed source model, improving performance through batch-wise style alignment and entropy regularization.
C EXTENDED EXPERIMENTAL RESULTS
C.I DATASETS
BioVid: Heat and Pain (Part A): This dataset (Waterr et al. 2013) consists video recordings of 87 subjects experiencing thermal pain stimuli in controlled laboratory setting. Each subject is assigned to one of five pain categories: "no pain" and four increasing pain levels (PA1-PA4), with PA4 representing the highest intensity. Consistent with prior work, which reports minimal facial activity at lower intensities, we focus on binary classification between "no pain" and PA4. For each subject, 20 videos per class are used, each lasting 5.5 seconds. Following recommendations in (Werner et al, (017). the first 2 seconds of each PA4 video are discarded to eliminate frames where facial expressions are typically absent, retaining only the segments that capture stronger pain- related facial activity.
This dataset (Chaptoukaev al.) 2023) focuses on assessing stress through facial expressions. It comprises facial video recordings from 54 individuals, totaling around 918 minutes of annotated visual content. our work, we use only the visual modality. Each frame labeled as either "neutral" "stressed," based participants' self-reported stress scores. Specifically, frames corresponding to scores below are labeled as neutral (label 0), while those with scores or higher considered stressed (label 1).
StressID:
BAH: The BAH dataset (González- Gonzálezz et al. 2025). which is designed for recognizing am- bivalence and hesitancy (A/H) expressions in real-world video ceoordings.ooprrisn facial record- ings from 224 participants across Canada, designed to reflect a diverse demographic distribution in terms of sex, ethnicity, and province. Each participant contributes up to seven videos, with total
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Table 8: Comparison of the proposed SFDA-PFT with state-of-the-art SFDA methods on the BioVid dataset (10 target subjects, 77 source subjects). All models use ResNet- MeNNett1 8. Bold num- bers indicate the best ACC.
Setting
Methods
Sub-1
Sub-2
Sub-3
Sub-4
Sub-5
Sub-6
Sub-7
Sub-8
Sub-9
Sub-10
Average
Source only
Source model (no adaptation)
66,11
55.55
86.36
85.11
87.11
59.00
75.66
70.44
52.66
48.22
68
SHOT
long et al
2020)
55.78
47.76
41.05
52.44
54.67
48.89
54.46
49.22
54.86
44,44
50.35
SFDA
Yang 1020
39.38
84.00
79.67
42,67
53.95
42.47
(mudee-bused))
TPDS Jung
1014
65.56
55.98
79.22
68.22
91.67
59.11
61.28
69.33
57.11
48.22
65.57
DSPIA Sharah of
2025
77.00
75.11
90.89
85.67
88.11
67.48
89.22
87.56
09:22
72.11
80.24
SEII
(Bo Zheng
102
82.87
91.62
60.59
14.56
(dita-hised)
SFDA
SFDA-IT (Hou Zheng
2221
75.33
67.27
89.00
84.55
90.80
62.31
81.77
75.89
57.88
70.56
75.54
BFDA-PF2 (ours)
84.93
75.56
95.05
85.86
97.59
73.78
88.73
83.49
78.48
61.16
$2.46
Oraete
Fine-Tuning
97.11
91.43
96.76
97.89
96.11
92.30
95.09
98
97.00
95.19
of 1,118 videos ( 86.2 hours), Among these, 638 videos contain at least one A/H segment, resulting in total of 1,274 annotated A/H segments. The dataset includes 143,103 frames labeled with A/H, out of 7 714,005 total frames. In our setup, frames with A/H annotations are assigned a label of (indicating the presence of A or H), while all other frames are considered neutral and assigned a label of O.
large-scale in-the-wild dataset for affeet recognition, consisting of 318 videos with available annotations. In our study, use subset of 292 videos that each represent a single subject, which is essential for our subject- based setting, where each individual is treated as a separate domain. We focus exclusively expression categories for discrete expression classification. Specifically, we use the following seven classes: neutral (0), anger (1), disgust (2), fear (3), happiness (4), sadness (5), and surprise (6). We consider only the visual modality in our experiments.
Aff-Wi d2: The Aff-Wi d2 dataset (Kollias & Zafeiriou 2019)
basic
C.2 QUANTITATIVE COMPARISON WITH SFDA BASELINES
Across the lab-controlled datasets BioVid and StressID, our proposed SFDA-PFT achieves the best performance compared to all baselines (Table and Table 9. On Biovid, SFDA-PFT improves the average accuracy to 82.46%, surpassing the best baseline (SFDA-TT) by more than percentage points. Similarly, on Stress SFDA-FFT reaches 71.49% again outperforming all competing methods. These gains highlight the advantage of SFDA PFT in settings where ac- quisition conditions are stable, allowing feature-level adaptation to capture subtle subject-specific differences with reduced variance across individuals. While overall performance is strong, two notable failure cases appear on BioVid for Sub-8 and Sub-10, where SFDA-PFT achieves only 83.49% and 61.16%, respectively. Both subjects belong to the older age group, where pain-related facial responses are less pronounced and more variable, reducing the discriminability of features. This suggests that subject age can act as confounding factor in personalized adaptation and points to the potential benefit of future age-aware or stratified domain adaptation strategies.
On the more in-the-wild datasets BAH and Aff-Wild2, SFDA-PFT remains highly competi- tive (Table 10 and Table III. On BAH, SFDA-PFT, clearly outperforms all alternatives, achiev- ing 62.09% average accuracy, over 4 points higher than the best image-translation baseline. On Aff-Wildt Wii which involves 7 classes and severe real-world noise. SFDA-PFT performs on par with the strongest baseline, trailing by less than percentage point. The remaining gap arises from multi-class confusion and extreme conditions such as pose variation, motion blur, and class imbal- ance. Notably, SFDA-PFR surpasses image-trans sangeettrassaaion-baseeee methods because adapts directly in the feature space rather than the image space: image translation often introduces artifacts or loses discriminative details (e.g., subtle muscle activations or nicro-expressions) which weakens down- stream classification. By preserving the discriminative structure already extracted by the backbone, SFDA-PFT avoids error accumulation from imperfect translations and provides more stable, reli- able adaptation across subjects.
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Table 9: Comparison of the proposed SFDA-PFT with state-of-the-art SFDA methods on the StressID ID dataset (I0 target subjects, 44 source subjects). All models use ResNet-18 18. Bold numbers indicate the best ACC.
Setting
Methods
Sub-1
Sub-2
SSb-3
Sub-4
Sub-5
Sul-6
Sub-7
Sub-8
Sub-9
Sub-10
Average
Source only
Source model (no adaptation)
38,96
41.21
65.53
42,04
55.16
65.51
69.43
60.78
53.62
55.63
$4.79
SHOT lang et al
2020)
68.31
$1.95
45.83
39.26
53.67
61.38
99.76
45.25
51.42
$2.05
$2.88
SFDA
Yaoa X020
52:25
31.83
59.67
41.25
$4.20
51 51.07
(mudee-busod))
TPDS
1014
65.56
54,98
64.22
58.22
51.67
63.11
69.28
59.33
50.11
51,96
59.17
DSPI DSPDDt Sharah et al.
73.47
69.39
87.12
69.74
79.87
87.39
82.80
83.89
75.03
77.39
78.61
SFII
(Bog Zheng
1b2
69.67
71.92
67.48
70.76
75.21
(dita-hised))
SFDA
SFDA-IT (Hou Zheng
2021
73:47
69.50
69.90
73,02
66.54
78.62
71.30
76.67
65.42
67.32
71.18
BEDA-PF2 (ours)
78.33
74.87
78.17
73.32
79.96
89.00
84.76
84.14
74.42
77.95
79.49
Oraete
Fine-Tuning
98.89
1001
99.53
09.22
97.57
96.02
99.
99.56
98.83
Table 10: Comparison between the proposed SFDA-PFT and several sfate-of-thh-aa methods on the BAH dataset (10 target subjects, 214 source subjects). All models use ResNet- Bold numbers indicate the best ACC.
Setting
Methods
Sub-1
Sub-2
Sub-3
Sub-4
Sub-5
Sub-6
Sub-7
Sub-8
Sub-9
Sub-10
Average
Source-ooly
Source model
49.71
50.00
54.67
48.43
51.51
50.30
48.65
SHOT (Linn al
2020
19.73
$4,17
60.49
49.55
45.83
51,22
46.62
52.76
19:09
47.92
50.74
SFDA
NRC
Yang clal M22
49.46
$4.05
55.002
49.11
46.52
49.91
44.83
52.26
48.63
47.24
49.70
(model-basee
2024
55.91
47.67
53,20
DSFDA Sharali
3125
61.24
56,02
60.31
58.77
$4.19
59.88
$7.40
53.16
62.15
60.49
58.36
SFIT
(ou Zhang
102
60.15
56.84
60.01
$1,91
56.15
55.73
56.02
56.49
56.22
58.39
57.09
hosed)
SFDA
SHDA-II Zheng
2021a
60.12
56.48
56.15
56.80
$7.05
SEDA-PFT (ours)
69.46
64.17
60.49
62.11
59.83
61.91
54.62
57.76
62.63
67.92
62.09
Oracle
Finc-lone
93.35
96.61
99.22
95.58
99.17
97.89
92.45 48
96.14
93.07
93.38
95.69
Table 11: Comparison between the proposed SFDA-PFT and several sfate-of-the-ar methods on the Aff Aff-willd Wild2 dataset (10 target subjects, 282 source subjects), All models use ResNet-11 Bold numbers indicate the best ACC.
Setting
Methods
Sub-2
Sub-3
Sub-5
Sub-7
Sub-8
Average
Sourc--only
Source model
24.50
23.97
21.94
30.17
32.41
40.63
16.92
37.67
18:77
19.98
26.70
SHOT Liang et al.
2020
45.00
41.57
40.42
43.91
39.88
41.34
42.18
39.00
49.16
50.84
42.
SFDA
NRC Ying 2021
40.98
43.29
(model based)
TPDS lang
2014
47.62
44.18
43.75
46.03
42,87
41.59
44.22
41.07
49.08
50.49
45.09
DSPDA Sharlli el
58.31
56.78
$7.96
59.24
55.63
58,09
56.41
57.18
57.82
56.18
57.42
SHTT (on
Zhenp
2021b
56.37
54.79
$5.03
$2.98
56.85
57.12
49.56
55.93
(data: basod)
SFDA
SFDA (Hou & Zhong
2021
39.63
$7.88
54.42
58.07
53.61
55.94
50.83
55.76
58.23
56.83
56. 56.12
SFDA-DFI (murs)
59.47
61.72
57.39
60.91
59.64
56.75
59.20
Oracle
Finc-tune
98.90
98.71
98.03
98.37
91.66
83.33
99.87
81,48
91.51
97.88
91.58
C.3 QUALITATIVE EXAMPLES OF SFDA-IT IT TRANSLATIONS
In addition to quantitative results, we also provide qualitative examples of translated images gener- ated by SFDA-IT (Hou & Zheng 2021a). As an masee-based adaptation method, SFDA-IT (Hou & Zheng 2021a maps target-domain samples into source-style visual space before classification. Figure illstrates representative examples from Biovid, showing target input frames and their translated counterparts. While SFDA-IT (Hou & Zheng, 2021a) effectively alters low-level style features, it may fail to preserve fine-grained facial expressions essential for accurate classification, particularly in subtle affective states.
D ADDITIONAL ABLATION STUDIES
D.1 ISTANCE TO CLOSEST SOURCE PROTOTYPES
To assess the effectiveness of our subject-aware translation module, we plot the L2 distances be- tween translated target samples and the closest source subject prototype in the feature space. As
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Original Tagel Subject
Translardd Targe Subject
Figure 7: Translated images of two target subjects from the BioVid dataset using landmark pairs at test time with the SFDA-IT (Hou & Zheng 202 method. Left column shows the original target image. Right columns display the corresponding translated images used for classification.
Neutral
1,500
Frequency
1,000
Source-ooly
(b) SEIT
500
0
0.2
0.4
0.6
0.8
Distance Closest Source Subject Prototype
(c) SFDA-IT
(c) PFT (ocrs)
Figure 8:
Histogram of distances between translated target frames and their closest source prototype.
Figure 9: t-SNE visualizations before and after SFDA-PFT adaptation for Sub-1 in BioVid dataset.
shown in Figure the distribution is asymmetric, with a strong concentration of distances around 0.2 and long tail toward higher values. This indicates that most translated target features are successfully aligned close to their corresponding source subject representations, validating the role of the translation mechanism in enhancing subject-level alignment. The sharp peak and reduced spread reflect improved intra-class compactness and inter-domain consistency, which are critical for minimizing domain shift in source-free adaptation settings.
D.2 QUALITATIVE ANALYSIS VIA T-SNE VISUALIZATION.
Target embeddings are visualized using t-SNE for a representative subject (Sub-1) across four models, source-only, SFTT (Hou & Zheng 202 (b), SFDA-IT (Hou & Zheng 2021a), and our SFDA-PFT (Figure. 19. Initially, the source-only model (a) yields overlapping neutral/pain clus- ters. After adaptation, SFIT (b) adds mild structure but remains mixed; SFDA-IT (c) shows clearer yet diffuse boundaries; SFDA-PFT (d) forms compact, well-separated clusters, indicating better expression preservation and domain alignment.
D.3 IMPACT OF DIFFERENT BACKBONES
To assess whether the gains of SFDA-PI PFT depend particular backbone, repeat all experi- ments with both a transformer encoder (ViT-B/32 and a convolutional encoder (ResNet-50) across BioVid, Stress ID, and BAH. In all settings, SFDA-PFT achieves the best average Fll and yields the highest or negh-highest subject-wise performance (Tables [7]. outperforming SHOT, DSFDA, SFIT, and SFDA-IT. These results indicate that the proposed feature-space translation is
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Table 12: Comparison of the proposed SFDA-PFT with state-of-the-art SFDA methods the
BioVid dataset (10 target subjects, 77 source subjects) with ViT-B/32 Bold numbers indi-
cate the best
Methods
Sub-1
Sub-2
Sub-3
Sub-4
Sub-3
Sub-6
Sub-7
Sub-8
Sub-9
Sub-10
Average
Src_only
68.89
52.00
85.00
74.56
75.00
56.69
65.89
56.00
64.55
59.55
65.81
SHOT
75.69
79.69
74.69
69.00
70.48
DSFDA
70.16
66.52
85.99
81.11
85.66
64.36
78.22
75.00
67.23
63.00
73.72
SETT
70.16
69.00
86.55
80.69
81.45
63.60
80.00
74.66
65.36
61.00
73.24
SFDA-IT
69.99
71.00
88.00
80,90
86.97
66.00
79.65
64.56
66.47
60.98
73.45
SFDA-PPT (ours)
77.00
71.00
90.20
81.50
88.10
84.60
75.20
68.00
63.40
76.65
Table 13: Comparison of the proposed SFDA-PFT with state-of-the-art SFDA methods the
StressID ID dataset (10 target subjects, 44 source subjects) with ViT-B/32 Bold numbers indi- cate the best
Methods
Sub-1
Sub-2
Sub-3
Sub-4
Sub-3
Sub-6
Sub-7
Sub-8
Sub-9
Sub-10
Average
Src_only
47.10
35.56
58.12
50.98
51.28
38.76
45.05
38.29
44.14
40.72
45.00
SHOT
52.08
47.99
50.53
43.75
39.09
44.69
DSFDA
62.28
59.07
76.36
72.00
76.05
57.16
69.43
66.61
61.52
55.95
65.66
SETT
59.35
58.40
73.29
68.28
68.95
53.84
67.72
63.15
55.29
51.63
62.00
SFDA-IT
61.18
62.58
76.87
70.63
78.07
57.65
69.57
56.39
58.06
53.27
64.43
SFDA-PPT (ours)
68.23
79.93
72.22
78.07
74.96
66.64
60.26
56.18
67.92
Table 14: Comparison of the proposed PFT with state-of-the-art SFDA methods the BAH
dataset (10 target subjects, 214 source subjects) with ViT-B/32 Bold numbers indicate the best F1.
Methods
Sub-1
Sub-2
Sub-3
Sub-4
Sub-5
Sub-6
Sub-7
Sub-8
Sub-9
Sub-10
Average
Sre_only
17.09
12.90
21.09
18.50
18.61
14.07
16.35
13.90
16.02
14.78
16.33
SHOT
50.09
45.55
42.08
37.56
42.98
DSFDA
45.23
42.89
55.44
52.29
55.23
41.49
50.43
48.35
44.63
40.62
47.66
SETT
47.89
47.10
59.08
55.08
$5.60
43.41
54.61
50.96
44.62
41.64
50.00
SEDA-IT
48.22
49.39
60.62
55.73
59.92
45.47
54.87
44.48
45.79
42.01
50.65
SFDA-PFT (ours)
55.75
51.41
65.31
59.01
63.79
48.87
61.26
54.45
49.24
45.91
55.50
Table 15: Comparison of the proposed SFDA-PFT with state-of-the-art SFDA methods on the BioVid dataset (10 target subjects, 77 source subjects) with BesNet-50. Bold numbers indi- cate the best
Methods
Sub-1
Sub-2
Sub-3
Sub-4
Sub-5
Sub-6
Sub-7
Sub-8
Sub-9
Sub-10
Average
Sre_only
65.00
53.23
79.56
68.88
72.66
53.00
65.00
55.66
63.22
54.69
63.09
SHOT
70.00
87.50
79.11
73.56
67.77
64.55
70.38
DSFDA
70.89
68.90
87.00
80.00
79.76
62.55
75.47
70.00
64.76
55.50
71.48
SEIT
75.02
69.00
88.65
80.00
80.14
62.50
81.95
72.45
64,80
60.00
73.45
SFDA-IT
76.25
69.00
88.65
80.69
80.00
62.00
81.50
72.45
65.80
60.40
73.67
SFDA-PFT (ours)
80.00
71.50
90.20
81.50
88.10
67.50
84.60
75.20
68.00
63.40
77.00
Table 16: Comparison of the proposed SFDA-PFT with state-of-the-art SFDA methods on the
StressID dataset (10 target subjects, 44 source subjects) with ResNet- -00. Bold numbers indi- cate the best
Methods
Sub-1
Sub-2
Sub-3
Sub-4
Sub-5
Sub-6
Sub-7
Sub-8
Sub-9
Sub-10
Average
Sre_only
49.22
37.00
56.54
51.11
51,28
40.69
50.89
40.18
42.56
45.44
46.49
SHOT
50.69
42.89
58.00
52.00
$1.99
46.14
51.26
45.99
44.05
47.04
49.01
DSEDA
63,00
57.00
75.00
71.97
75.99
$2.78
57.77
69.00
$3.66
46.44
62.26
SEIT
58.69
57.46
73.00
69.78
76.00
57.36
69.69
65.89
$7.69
52.00
63.76
SFDA-IT
61.00
60.55
75.75
70.00
77.11
56.65
70.07
66.30
58.66
55.00
65.11
SFDA-PFT (ours)
67.22
63.91
77.06
72.22
79.00
60.89
76.66
67.60
59,99
58,00
68.26
robust to the choice of backbone and can be used as a plug-and-plaa SFDA module for both CNN- and ViT-based FER models.
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Table 17: Comparison of the proposed PPDA-PFT with state-of-the-art SFDA methods the BAH dataset (10 target subjects, 214 source subjects) with ResNet -50. Bold numbers indicate the best F1.
Methods
Sub-1
Sub-2
Sub-3
Sub-4
Sub-5
Sub-6
Sub-7
Sub-8
Sub-9
Sub-10
Average
Src_only
16.00
12.07
23.11
18.01
18.96
14.99
17.22
13.44
16.09
15.02
16.49
SHOT
51.78
44.50
43.88
39,99
44.62
DSFDA
45.44
44,00
58.97
54.01
$4.00
43.33
51.12
48.99
45,00
40.19
48.51
SETT
45,76
46.66
59.28
56.01
55.33
44.98
54.00
49.15
44.25
41.14
49.66
SFDA-IT
45,95
47.00
61.11
55.22
55.58
45.45
58.11
49.88
46.08
43,00
50.74
SFDA-PPT (ours)
53.00
65.33
57.77
64.77
52.14
54.68
48.76
45.00
55.64
Table 18: Impact of source layer selection for style transfer classification accuracy (%) using proposed method for B BioVid d dataset.
Layer Configuration
Accuracy (%)
Layer
74.13
Layers 1-2
76.37
Layers 1-3
82.46
Last Layers
69.25
D.4 SOURCE LAYER SELECTION FOR STYLE TRANSFER
To assess the impact of style extraction depth, experiment with using mean and variance statistics from different layers of the source model to transfer identity-peccffi information. As shown in Table utilizing only early layers (e.g., Layer 1) yields moderate performance, while progressively including Layers 2 and 3 leads to significant improvements. This suggests that intermediate layers better capture subject-specific style without entangling high-level semantic content. In contrast, using the last layers results in a drop in accuracy. likely due to the abstraction of expression- features. Overall, these findings highlight the importance of selecting appropriate layers for effective style modeling in source-free FER.
D.5 EFFECT OF EXPRESSION LOss TYPE
To evaluate the impact of different expression loss formulations, we compare mean squared error (MSE), cross-entropy (CE), and Kullback-Leibler (KL) divergence in both image-based and feature- based settings. As shown Table T9 the feature-based model consistently outperforms its image- based counterpart across all loss types. further validating the advantages of operating in the latent feature space. Among the expression loss variants, KL divergence achieves the highest accuracy in both models, suggesting its strength in aligning soft expression distributions more effectively than point-wise (MSE) or hard-target (CE) alternatives. Notably, feature-based model with Ki. divergence reaches 80.54% accuracy, outperforming the best image-based counterpart by over 5%, while also benefiting from reduced training cost and model complexity.
D.6 SENSITIVITY ANALYSIS OF LOSS WEIGHTS.
We analyze the impact of the expression and style losses during source training and their effect on average target classification performance on BioVid dataset. As shown in Figure T0 turning off either loss (i.e., setting -e or -0yle=0) leads to a substantial drop in accuracy compared to the joint setting, confirming that both components are important for effective translation. The degradation is more pronounced when the style loss is removed, highlighting the dominant role of identity alignment for subject-speciffe adaptation. At the same time, varying Aeser (with A0y3e=0.3) or Aayle (with >esp==1.0 over broad range yields only modest changes in performance, indicating that SFDA-PER PFT is not overly sensitive to moderate perturbations these hyperparameters around the chosen setting (Aesp==1.0 0.33.
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82
Accuracy (%)
Acouracy (%6)
78
78
76
74
72
70
0.0
0.2
0.4
0.6
0.8
1.0
Apple
Acae
(a)
(b)
Figure 10:
Sensitivity of our SFDA-PFT method to the expression and style loss weights on BioVid: (a) varying Aeses with 0.3; (b) varying Xayle with A.spr=1.0.
Table 19: and our proposed SFDA-PFT methods using different expression loss functions on the BioVid dataset.
Average target-domain classification accuracy (%) of SFDA-IT (Hou & Zheng, 2021a
expression Type Image-
Feature-based
MSE
73.15
77.91
Cross-Entropy
74.20
79.83
KL Divergence
75.54
82.46
Table 20: Syper-parameters for source training and target adaptation.
Hyper-parameter
Source Training
Target Adaptation
Backbone
RecNet -18
ResWet- 18
Optimizer
SGD Nesterov
Adam
Momentum
0..4
NA
Weight Decay
Learning Rate
(0.001, 0.011 0.02,
(0.000), 0.001,0.002)
1.R Decny Schedule
Step decay at ( 1500, 250, 250)
Reducel ROnPlateau (patience=:) ?)
Mini-hatel Size
(32,64)
Epochs
(30, 50, 100)
[20, 50)
Random Flip
Horizonal/Verricl
Horizonral/Vrrian
Coler Jitter
Crightess/Contraa//Sae
Hue
0.05
Same
Image Size
225 crop 224
E HYPERPARAMETER DETAILS
This section summarizes the hyperparameters used for both source pre-training and target adapta- tion, as presented Table 20, We report settings for optimizer types, learning rate schedules, and batch sizes. All experiments use a fixed ResNet-18 18 backbone to ensure fair comparison across methods. The chosen values follow standard SFDA practices and are selected based on source- domain validation performance.
22


Reviews:
[{"review_id": "Fs9PYeWnJR", "reviewer": "Reviewer_nYCY", "summary": ": This paper proposes a personalized feature translation method named PFT, which efficiently achieves domain adaptation for facial expression recognition models using only target user's neutral expression data. It outperforms existing methods across multiple datasets while significantly reducing computational costs.", "questions": "（1） Compared to models like SHOT and NRC that also update only partial parameters, how does PFT perform in terms of the number of iterations and total time required to reach convergence?\n（2） The core method involves using style loss to align identity features while employing expression loss to preserve facial emotion information. Is there any quantitative evidence demonstrating that the translator network indeed learns decoupled representations of these two factors? If the reference image itself carries strong non-neutral expressions, could this contaminate the translation process, leading to loss or confusion of expression information?\n（3） Was comparing PFT against such lighter-weight baselines considered to more clearly demonstrate the added value of feature translation over simple normalization or alignment strategies?", "limitations": "", "rating": 4, "confidence": 4, "soundness": 2, "presentation": 2, "contribution": 2, "strengths": "（1） Structure: The paper features a clear structure, with algorithms presented in easily understandable diagrams. The problem definition is precise, and the motivation is well-articulated.\n（2） Innovation: The experiments introduce a personalized translation approach within the feature space, circumventing the instability inherent in traditional image-based methods.\n（3） Quality: The experiment design is rigorous, validating the method's effectiveness across four distinct datasets. Detailed ablation studies are provided to demonstrate the contribution of each component.", "weaknesses": "（1） Although comparisons are made with multiple SFDA methods, the paper does not include more recently proposed personalized approaches based on generative models or meta-learning.\n（2） The paper notes performance degradation on elderly subjects but does not propose adaptive strategies for age differences. Further exploration of stratified or age-aware adaptation methods is recommended.", "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": "", "extra_scores": {}}, {"review_id": "ICHcmxy0UA", "reviewer": "Reviewer_7pCo", "summary": "This paper proposes Personalized Feature Translation (PFT), a source-free domain adaptation method for facial expression recognition that operates entirely in the feature space. By translating target subject features toward source-style prototypes using only neutral expressions, PFT achieves higher accuracy with significantly lower computational cost than image-based SFDA methods.", "questions": "review the Weaknesses", "limitations": "", "rating": 4, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 2, "strengths": "This paper introduces a highly original and impactful approach to source-free domain adaptation for facial expression recognition. By formulating a novel feature-level translation method that operates using only neutral target data, it achieves state-of-the-art performance while being dramatically more efficient than image-based alternatives. The work is exceptionally well-supported through rigorous experiments on four diverse benchmarks and presents a practical solution to key real-world constraints like data privacy and computational cost.", "weaknesses": "1．\tWhile the proposed PFT method is well-motivated, the paper lacks a clear theoretical or intuitive explanation of why feature translation in latent space is inherently more robust than image-level translation for expression preservation.\n\n2．\tThe paper lacks a rigorous explanation or analysis of how the proposed losses ensure that the translator modifies only identity-related features while preserving expression-related ones.\n\n3．\tThe proposed PFT method relies on pre-training a feature-space translator, but there is no analysis of training stability or overfitting risks. Given the challenge of disentangling expression and identity, is there a risk of overfitting to identity features? Are there issues with convergence or conflicting objectives (style vs. expression)?\n\n4．\tThe experiments are conducted on only 10 target subjects per dataset. This small sample size raises concerns about the statistical significance and generalizability of the results. The authors should include confidence intervals or perform cross-validation across multiple random splits of target subjects.\n\n5．\tThe paper mentions hyperparameters such as λexpr and λstyle, but does not discuss their sensitivity or how they were tuned.", "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": "", "extra_scores": {}}, {"review_id": "82rsZu8vFK", "reviewer": "Reviewer_2fSs", "summary": "This paper presents Personalized Feature Translation (PFT), a novel source-free domain adaptation method for facial expression recognition. PFT performs personalized adaptation by translating features in the latent space, requiring only neutral expression data from target subjects. This approach eliminates the need for complex image synthesis and source data access during adaptation, while maintaining high computational efficiency. Extensive experiments demonstrate that PFT consistently outperforms state-of-the-art methods across multiple datasets。", "questions": "NA", "limitations": "", "rating": 6, "confidence": 2, "soundness": 3, "presentation": 2, "contribution": 3, "strengths": "The key strength of the proposed PFT method lies in its innovative feature-space translation paradigm. This method avoids the need for complex image synthesis and achieves computational efficiency through lightweight parameter adaptation. It demonstrated superior performance over state-of-the-art methods across four FER benchmarks.", "weaknesses": "1 While the empirical results are strong, the paper lacks an analysis explaining the reasons why feature-space translation is more effective.\n\n2 The experimental validation is centered primarily on FER. It would be valuable to discuss the potential of PFT for other tasks that require subject-specific adaptation, such as face recognition or person re-identification.", "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": "", "extra_scores": {}}]

Author rebuttal:

Decision: Accept (Poster)