nils-herrmann/clean_scirepeval huggingface dataset:
Linear shift-invariant maximum margin SVM correlation filter

Advanced correlation filters are effective for recognizing distorted images of a particular class. Most correlation filter designs are based on optimization criteria that lead to a closed form filter solution. We remove this restriction of a closed form solution and introduce a new filter design approach, based on a margin of separation maximization formulated as a linear support vector machine (SVM). The resulting SVM classifier is of the form of a correlation filter which has some attractive attributes, such as linearity and shift-invariance (properties that traditional SVM classifiers lack). We also show that our proposed SVM correlation filter offers built-in noise tolerance, which is valuable for any recognition task where noise can be present. More importantly, we demonstrate that we can achieve good generalization using only a single image for training. We compare our proposed filter design to popular advanced correlation filter designs and show the increase in performance of our proposed method by testing on two well known face databases (CMU-AMP lab facial expression database and the CMU-PIE illumination dataset consisting of faces of 65 people).
Temporal Non-volume Preserving Approach to Facial Age-Progression and Age-Invariant Face Recognition

Modeling the long-term facial aging process is extremely challenging due to the presence of large and non-linear variations during the face development stages. In order to efficiently address the problem, this work first decomposes the aging process into multiple short-term stages. Then, a novel generative probabilistic model, named Temporal Non-Volume Preserving (TNVP) transformation, is presented to model the facial aging process at each stage. Unlike Generative Adversarial Networks (GANs), which requires an empirical balance threshold, and Restricted Boltzmann Machines (RBM), an intractable model, our proposed TNVP approach guarantees a tractable density function, exact inference and evaluation for embedding the feature transformations between faces in consecutive stages. Our model shows its advantages not only in capturing the non-linear age related variance in each stage but also producing a smooth synthesis in age progression across faces. Our approach can model any face in the wild provided with only four basic landmark points. Moreover, the structure can be transformed into a deep convolutional network while keeping the advantages of probabilistic models with tractable log-likelihood density estimation. Our method is evaluated in both terms of synthesizing age-progressed faces and cross-age face verification and consistently shows the state-of-the-art results in various face aging databases, i.e. FG-NET, MORPH, AginG Faces in the Wild (AGFW), and Cross-Age Celebrity Dataset (CACD). A large-scale face verification on Megaface challenge 1 is also performed to further show the advantages of our proposed approach.
Faster than Real-Time Facial Alignment: A 3D Spatial Transformer Network Approach in Unconstrained Poses

Facial alignment involves finding a set of landmark points on an image with a known semantic meaning. However, this semantic meaning of landmark points is often lost in 2D approaches where landmarks are either moved to visible boundaries or ignored as the pose of the face changes. In order to extract consistent alignment points across large poses, the 3D structure of the face must be considered in the alignment step. However, extracting a 3D structure from a single 2D image usually requires alignment in the first place. We present our novel approach to simultaneously extract the 3D shape of the face and the semantically consistent 2D alignment through a 3D Spatial Transformer Network (3DSTN) to model both the camera projection matrix and the warping parameters of a 3D model. By utilizing a generic 3D model and a Thin Plate Spline (TPS) warping function, we are able to generate subject specific 3D shapes without the need for a large 3D shape basis. In addition, our proposed network can be trained in an end-to-end frame-work on entirely synthetic data from the 300W-LP dataset. Unlike other 3D methods, our approach only requires one pass through the network resulting in a faster than real-time alignment. Evaluations of our model on the Annotated Facial Landmarks in the Wild (AFLW) and AFLW2000-3D datasets show our method achieves state-of-the-art performance over other 3D approaches to alignment.
"Hallucinating Irises" - Dealing with Partial & Occluded Iris Regions

In this paper, we examine a novel image-filling algorithm to fill the occluded and partial regions of the iris. In iris recognition, these occlusions naturally appear due to eye-lids being partially closed, eye movements and eye-lashes. Additionally, specular reflections arising from the iris-camera illuminator are present and produce occlusion artifacts on the iris region. In the polar representation of iris images, additional 'nuisance' parameters include image regions containing the pupil and sclera due to inaccurate iris segmentations. Thus, for accurate iris recognition, it is important to reduce the effect of these nuisance parameters so that the performance of the iris recognition system can be maximized. Typically, a mask region (automatic or manual) is defined to ignore occluded or affected iris regions from such nuisance artifacts. However, in the feature extraction stage, especially using Gabor type extraction, the features near the edge of these artifacts are also affected due to the tail supports of these filters that overflow on occluded irises. We propose a novel method for filling in the occluded regions by synthesizing the iris pattern from this current exemplar image and show that a significant improvement on the Iris Challenge Evaluation (ICE) dataset can be made.
Unconstrained Pose-Invariant Face Recognition Using 3D Generic Elastic Models

Classical face recognition techniques have been successful at operating under well-controlled conditions; however, they have difficulty in robustly performing recognition in uncontrolled real-world scenarios where variations in pose, illumination, and expression are encountered. In this paper, we propose a new method for real-world unconstrained pose-invariant face recognition. We first construct a 3D model for each subject in our database using only a single 2D image by applying the 3D Generic Elastic Model (3D GEM) approach. These 3D models comprise an intermediate gallery database from which novel 2D pose views are synthesized for matching. Before matching, an initial estimate of the pose of the test query is obtained using a linear regression approach based on automatic facial landmark annotation. Each 3D model is subsequently rendered at different poses within a limited search space about the estimated pose, and the resulting images are matched against the test query. Finally, we compute the distances between the synthesized images and test query by using a simple normalized correlation matcher to show the effectiveness of our pose synthesis method to real-world data. We present convincing results on challenging data sets and video sequences demonstrating high recognition accuracy under controlled as well as unseen, uncontrolled real-world scenarios using a fast implementation.
Classification of cardiosynchronous waveforms by projection to a Legendre Polynomial sub-space

The use of Radio Frequency Impedance Interrogation (RFII) is being investigated for use as a noninvasive hemodynamic monitoring system and in the capacity of a biometric identifier. Biometric identification of subjects by cardiosynchronous waveform generated through RFII technology could allow the identification of subjects in operational and potentially hostile environments. Here, the filtering methods for extracting a unique biometric signature from the RFII signal are examined, including the use of Cepstral analysis for dynamically estimating the filter parameters.  The projection of that signature to a Legendre Polynomial sub-space is proposed for increased class separability in a low dimensional space. Support Vector Machine (SVM) and k-Nearest Neighbor (k=3) classification are performed in the Legendre Polynomial sub-space on a small dataset.  Both the k-Nearest Neighbor and linear SVM methods demonstrated highly successful classification accuracy, with 93-100% accuracy demonstrated by various classification methods. The results are highly encouraging despite the small sample size. Further analysis with a larger dataset will help to refine this process for the eventual application of RFII as a robust biometric identifier.
CUDA accelerated iris template matching on Graphics Processing Units (GPUs)

In this paper we develop a parallelized iris template matching implementation on inexpensive Graphics Processing Units (GPUs) with Nvidia's CUDA programming model to achieve matching rates of 44 million iris template comparisons per second without rotation invariance. With tolerance to head tilt, we achieve 4.2 million matches per second and compare our implementation to state of the art prior work performed on GPU and FPGA, emphasizing our improvements. Additionally a comparison to highly optimized CPU implementations of iris template matching is performed, showing a 14X speedup using our approach. In contrast to other published work, we develop an implementation for parallel iris template matching that incorporates iris code shifting for rotation invariance and provide timing data showing our proposed architecture is efficiently implemented, capitalizing on shared and texture memory to speedup the bit shifting process beyond current prior art.
Palmprint Classification Using Multiple Advanced Correlation Filters and Palm-Specific Segmentation

We propose a palmprint classification algorithm with the use of multiple correlation filters per class. Correlation filters are two-class classifiers that produce a sharp peak when filtering a sample of their class and a noisy output otherwise. For every class, we train the filters for a palm at different locations, where the palmprint region has a high degree of line content. With the use of a line detection procedure and a simple line energy measure, any region of the palm can be scored and the top-ranked regions are used to train the filters for each class. Using an enhanced palmprint segmentation algorithm, our proposed classifier achieves an average equal error rate of 1.12 times10-4% on a large database of 385 classes using multiple filters of size 64 times 64 pixels. The average false acceptance rate when the false rejection rate is zero is 2.25 times10-4%.
Unconstrained periocular biometric acquisition and recognition using COTS PTZ camera for uncooperative and non-cooperative subjects

We propose an acquisition and recognition system based only on periocular biometric using the COTS PTZ camera to tackle the difficulty that the full face recognition approach has encountered in highly unconstrained real-world scenario, especially for capturing and recognizing uncooperative and non-cooperative subjects with expression, closed eyes, and facial occlusions. We evaluate our algorithm on the periocular region and compare that to the performance of the full face on the Compass database we have collected. The results have shown that the periocular region, when tackling unconstrained matching, is a much better choice than the full face for face recognition even with less than 2/5 the size of the full face. To be more specific, the periocular matching across all facial manners, i.e., neutral expression, smiling expression, closed eyes, and facial occlusion, is able to achieve 60.7% verification rate at 0.1% false accept rate, a 16.9% performance boost over the full face.
Multifactor analysis based on factor-dependent geometry

This paper proposes a novel method that preserves the geometrical structure created by variation of multiple factors in analysis of multiple factor models, i.e., multifactor analysis. We use factor-dependent submanifolds as constituent elements of the factor-dependent geometry in a multiple factor framework. In this paper, a submanifold is defined as some subset of a manifold in the data space, and factor-dependent submanifolds are defined as the submani-folds created for each factor by varying only this factor. In this paper, we show that MPCA is formulated using factor-dependent submanifolds, as is our proposed method. We show, however, that MPCA loses the original shapes of these submanifolds because MPCA's parameterization is based on averaging the shapes of factor-dependent subman-ifolds for each factor. On the other hand, our proposed multifactor analysis preserves the shapes of individual factor-dependent submanifolds in low-dimensional spaces. Because the parameters obtained by our method do not lose their structures, our method, unlike MPCA, sufficiently covers original factor-dependent submanifolds. As a result of sufficient coverage, our method is appropriate for accurate classification of each sample.
3D face econstruction from a single 2D face image

T3D face reconstruction from a single 2D image is mathematically ill-posed. However, to solve ill-posed problems in the area of computer vision, a variety of methods has been proposed; some of the solutions are to estimate latent information or to apply model based approaches. In this paper, we propose a novel method to reconstruct a 3D face from a single 2D face image based on pose estimation and a deformable model of 3D face shape. For 3D face reconstruction from a single 2D face image, it is the first task to estimate the depth lost by 2D projection of 3D faces. Applying the EM algorithm to facial landmarks in a 2D image, we propose a pose estimation algorithm to infer the pose parameters of rotation, scaling, and translation. After estimating the pose, much denser points are interpolated between the landmark points by a 3D deformable model and barycentric coordinates. As opposed to previous literature, our method can locate facial feature points automatically in a 2D facial image. Moreover, we also show that the proposed method for pose estimation can be successfully applied to 3D face reconstruction. Experiments demonstrate that our approach can produce reliable results for reconstructing photorealistic 3D faces.
An Evaluation of Iris Pattern Representations

The success of an iris recognition algorithm is partially dependent upon the iris pattern representation computed during feature extraction. Some algorithms in the literature represent iris texture by applying bandpass Alter banks to the segmented iris region. However, the selection of a bandpass filter form, as well as the particular instantiations of that form, are often presented as arbitrary choices. In this paper, we evaluate multiple filter candidates and compare the discriminative information provided by their responses. Discrimination is measured on a set of reference iris images drawn from multiple datasets. After preliminary analysis of the best filter type, we conduct iterative optimization over the parameters of a filter bank to search for the best possible iris pattern representation. Finally, we give some sample recognition results using the selected filter bank.
Sparse Feature Extraction for Pose-Tolerant Face Recognition

Automatic face recognition performance has been steadily improving over years of research, however it remains significantly affected by a number of factors such as illumination, pose, expression, resolution and other factors that can impact matching scores. The focus of this paper is the pose problem which remains largely overlooked in most real-world applications. Specifically, we focus on one-to-one matching scenarios where a query face image of a random pose is matched against a set of gallery images. We propose a method that relies on two fundamental components: (a) A 3D modeling step to geometrically correct the viewpoint of the face. For this purpose, we extend a recent technique for efficient synthesis of 3D face models called 3D Generic Elastic Model. (b) A sparse feature extraction step using subspace modeling and ℓ1-minimization to induce pose-tolerance in coefficient space. This in return enables the synthesis of an equivalent frontal-looking face, which can be used towards recognition. We show significant performance improvements in verification rates compared to commercial matchers, and also demonstrate the resilience of the proposed method with respect to degrading input quality. We find that the proposed technique is able to match non-frontal images to other non-frontal images of varying angles.
Investigating the feasibility of image-based nose biometrics

The search for new biometrics is never ending. In this work, we investigate the use of image based nasal features as a biometric. In many real-world recognition scenarios, partial occlusions on the face leave the nose region visible (e.g. sunglasses). Face recognition systems often fail or perform poorly in such settings. Furthermore, the nose region naturally contain more invariance to expression than features extracted from other parts of the face. In this study, we extract discriminative nasal features using Kernel Class-Dependence Feature Analysis (KCFA) based on Optimal Trade-off Synthetic Discriminant Function (OTSDF) filters. We evaluate this technique on the FRGC ver2.0 database and the AR Face database, training and testing exclusively on nasal features and have compared the results to the full face recognition using KCFA features. We find that the between-subject discriminability in nasal features is comparable to that found in facial features. This shows that nose biometrics have a potential to support and boost biometric identification, that has largely been under utilized. Moreover, our extracted KCFA nose features have significantly outperformed the PittPatt face matcher which works with the original JPEG images on the AR facial occlusion database. This shows that nose biometrics can be used as a stand-alone biometric trait when the subjects are under occlusions.
Graphical Model Approach to Iris Matching Under Deformation and Occlusion

Template matching of iris images for biometric recognition typically suffers from both local deformations between the template and query images and large occlusions from the eyelid. In this work, we model deformation and occlusion as a set of hidden variables for each iris comparison. We use afield of directional vectors to represent deformation and a field of binary variables to represent occlusion. We impose a probability distribution on these fields using a lattice-type undirected graphical model, in which the graph edges represent interdependencies between neighboring iris regions. Gabor wavelet-based similarity scores and intensity statistics are used as observations in the model. Loopy belief propagation is applied to estimate the conditional distributions on the hidden variables, which are in turn used to compute final match scores. We present underlying theory as well as experimental results from both the CASIA iris database and the database provided for the iris challenge evaluation (ICE). We show that our proposed method significantly improves recognition accuracy on these datasets over existing methods.
Electromyograph and keystroke dynamics for spoof-resistant biometric authentication

Biometrics has come a long way over the past decade in terms of technologies and devices that are used to verify user identities. Three of the more well studied modalities in this field are the face, iris and fingerprint, with the latter two reporting very high user identification/verification rates. In the biometric community there has been little work in studying biomedical signals for user recognition purposes. In this paper, we propose using electromyograph (EMG) signals as a person's biometric signature. The EMG records the motor unit action potentials (MUAP) during any physical motion. Our study is done within the context of a person using a keyboard to type a password or any other fixed phrase. Along with EMG signals, we log key press times for the user and study the feasibility of using this data too as a biometric feature. Keypress timings alone if used as a biometric, are very easy to spoof and hence we fuse this modality with EMG signals. In order to classify these features, we use subspace modeling as well as Bayesian classifiers. The experiments have been performed within the context of a user typing a fixed pass phrase at a workstation. The idea is to monitor both biometric modalities when this action is performed and study user verification across data capture sessions and within capture sessions. Our approach yields high values of verification rates, which shows the promise of using these modalities as user specific biometric signatures.
Robust local binary pattern feature sets for periocular biometric identification

In this paper, we perform a detailed investigation of various features that can be extracted from the periocular region of human faces for biometric identification. The emphasis of this study is to explore the BEST feature extraction approach used in stand-alone mode without any generative or discriminative subspace training. Simple distance measures are used to determine the verification rate (VR) on a very large dataset. Several filter-based techniques and local feature extraction methods are explored in this study, where we show an increase of 15% verification performance at 0.1% false accept rate (FAR) compared to raw pixels with the proposed Local Walsh-Transform Binary Pattern encoding. Additionally, when fusing our best feature extraction method with Kernel Correlation Feature Analysis (KCFA) , we were able to obtain VR of 61.2%. Our experiments are carried out on the large validation set of the NIST FRGC database [6], which contains facial images from environments with uncontrolled illumination. Verification experiments based on a pure 1–1 similarity matrix of 16028×8014 (~128 million comparisons) carried out on the entire database, where we find that we can achieve a raw VR of 17.0% at 0.1% FAR using our proposed Local Walsh-Transform Binary Pattern approach. This result, while may seem low, is more than the NIST reported baseline VR on the same dataset (12% at 0.1% FAR), when PCA was trained on the entire facial features for recognition .