nils-herrmann/clean_scirepeval huggingface dataset:
PCA vs. automatically pruned wavelet-packet PCA for illumination tolerant face recognition

Facial recognition/verification R. Chellappa et al., (1995), is a continuing and growing area of research in the field of biometrics. One of the first approaches to this challenge was principal component analysis (PCA) [M. A. Turk et al., (1991), T. Chen et al., (2002)]. Typically PCA is performed in the original spatial domain. However, PCA has a high sensitivity to illumination effects in the original spatial domain. We propose that by using wavelet packet decomposition M. Vetterli et al., (1995), to create localized space-frequency subspaces of the original data, we can perform PCA in these subspaces which can generalize better across illumination variations. We report results on the CMU PIE database T. Sim et al., (2003), by comparing reconstruction error in the original spatial domain to that of the reconstruction error in the spatial subspaces (keeping same number of eigenvectors). It is seen that the total reconstruction error of the space-frequency subspaces is smaller than that of the original space and the automatically pruned wavelet packet PCA produced better face recognition performance across illumination.
Real-time face verification system on a cell-phone using advanced correlation filters

We present a real-time face recognition system that we implemented on a cell-phone using computationally efficient minimum average correlation energy filters. We examine the challenges faced in realizing such a practical system that can perform both enrollment and verification in real-time on the cell-phone using its limited memory and computational resources. Due to the speed and memory constraints of the cellphone, we optimize the correlation filters using a fast fixed-point implementation of 2D Fourier transform optimal for the cell-phone platform. We also detail an efficient algorithm for synthesizing these filters efficiently in order to minimize computation load and making the system practical with real-time enrollment of users.
Gender and Ethnicity Specific Generic Elastic Models from a Single 2D Image for Novel 2D Pose Face Synthesis and Recognition

In this paper, we propose a novel method for generating a realistic 3D human face from a single 2D face image for the purpose of synthesizing new 2D face images at arbitrary poses using gender and ethnicity specific models. We employ the Generic Elastic Model (GEM) approach, which elastically deforms a generic 3D depth-map based on the sparse observations of an input face image in order to estimate the depth of the face image. Particularly, we show that Gender and Ethnicity specific GEMs (GE-GEMs) can approximate the 3D shape of the input face image more accurately, achieving a better generalization of 3D face modeling and reconstruction compared to the original GEM approach. We qualitatively validate our method using publicly available databases by showing each reconstructed 3D shape generated from a single image and new synthesized poses of the same person at arbitrary angles. For quantitative comparisons, we compare our synthesized results against 3D scanned data and also perform face recognition using synthesized images generated from a single enrollment frontal image. We obtain promising results for handling pose and expression changes based on the proposed method.
Correlation Pattern Recognition for Face Recognition

Two-dimensional (2-D) face recognition (FR) is of interest in many verification (1:1 matching) and identification (1:N matching) applications because of its nonintrusive nature and because digital cameras are becoming ubiquitous. However, the performance of 2-D FR systems can be degraded by natural factors such as expressions, illuminations, pose, and aging. Several FR algorithms have been proposed to deal with the resulting appearance variability. However, most of these methods employ features derived in the image or the space domain whereas there are benefits to working in the spatial frequency domain (i.e., the 2-D Fourier transforms of the images). These benefits include shift-invariance, graceful degradation, and closed-form solutions. We discuss the use of spatial frequency domain methods (also known as correlation filters or correlation pattern recognition) for FR and illustrate the advantages. However, correlation filters can be computationally demanding due to the need for computing 2-D Fourier transforms and may not match well for large-scale FR problems such as in the Face Recognition Grand Challenge (FRGC) phase-II experiments that require the computation of millions of similarity metrics. We will discuss a new method [called the class-dependence feature analysis (CFA)] that reduces the computational complexity of correlation pattern recognition and show the results of applying CFA to the FRGC phase-II data
SparCLeS: Dynamic $\ell_{1}$ Sparse Classifiers With Level Sets for Robust Beard/Moustache Detection and Segmentation

Robust facial hair detection and segmentation is a highly valued soft biometric attribute for carrying out forensic facial analysis. In this paper, we propose a novel and fully automatic system, called SparCLeS, for beard/moustache detection and segmentation in challenging facial images. SparCLeS uses the multiscale self-quotient (MSQ) algorithm to preprocess facial images and deal with illumination variation. Histogram of oriented gradients (HOG) features are extracted from the preprocessed images and a dynamic sparse classifier is built using these features to classify a facial region as either containing skin or facial hair. A level set based approach, which makes use of the advantages of both global and local information, is then used to segment the regions of a face containing facial hair. Experimental results demonstrate the effectiveness of our proposed system in detecting and segmenting facial hair regions in images drawn from three databases, i.e., the NIST Multiple Biometric Grand Challenge (MBGC) still face database, the NIST Color Facial Recognition Technology FERET database, and the Labeled Faces in the Wild (LFW) database.
Robust Super-Resolution of Face Images by Iterative Compensating Neighborhood Relationships

In this paper, we propose a novel method for performing robust super-resolution of face images. Face super-resolution is to recover a high-resolution face image from a given low-resolution face image by modeling a face image space in view of multiple resolutions. The proposed method is based on the assumption that a low-resolution image space and a high-resolution image space have similar local geometries but also have partial distortions of neighborhood relationships between facial images. In this paper, local geometry is analyzed by an idea inspired by locally linear embedding (LLE), the state-of-the art manifold learning method. Using the analyzed neighborhood relationships, two sets of neighborhoods in the low-and high-resolution image spaces become more similar in an iterative way. In this paper, we show that changing resolution causes the partial distortions of neighborhood embeddings obtained by a manifold learning method. Experimental results show that the proposed method produces more reliable results of face super-resolution than the traditional way using neighbor embedding.
Radio Frequency cardiopulmonary waveform for subject identification

Radio Frequency Impedance Interrogation (RFII) measures hemodynamic function via resonance frequency coupling to a hydrophilic protein molecule and the resonant return signal phase change. The RFII device generates a cardiosynchronous waveform from the identification of blood movement in the time, frequency, and voltage domains. While the application of this technology to hemodynamic monitoring has demonstrated initial success, this small pilot study examined the use of RFII for subject identification by cardiosynchronous waveform signal analysis, which would allow confirmation of the identity of a subject in an operational setting. Preliminary results demonstrate 86% successful subject identification using the RFII cardiosynchronous waveform signature and simple pattern recognition classification, with as high as 100% identification with improvements to phase-shift recognition. Each individual appears to have a unique waveform morphology that is visually distinct from the other individuals in the data set. These results suggest that RFII may hold great potential as a biometric identifier and the rapid identification and authentication of subjects in the operational setting.
A robust contour sampling and tensor-based approach to facial beard and mustache shape segmentation and matching

In this paper, we propose a novel system for beard and mustache segmentation and matching in facial images. We first segment out facial hair contours from the image by utilizing a sparse dictionary on self-quotient images to classify regions as either skin or facial hair. We then landmark the shape contour to obtain points around the contour of the image using a combination of two algorithms, a novel non-uniform sampling algorithm, and points obtained from SIFT. We utilize these landmark points to extract inner distance-based shape context features. Finally, these features are used as inputs for a tensor product graph-based matching system. We run experiments on the Multiple Biometric Grand Challenge (MBGC) and the PINELLAS mugshot databases. Our pipeline achieves 90.3% matching accuracy on a subset of the PINELLAS database when divided into four types of facial hair.
Statistical Performance Evaluation of Biometric Authentication Systems Using Random Effects Models

As biometric authentication systems become more prevalent, it is becoming increasingly important to evaluate their performance. This paper introduces a novel statistical method of performance evaluation for these systems. Given a database of authentication results from an existing system, the method uses a hierarchical random effects model, along with Bayesian inference techniques yielding posterior predictive distributions, to predict performance in terms of error rates using various explanatory variables. By incorporating explanatory variables as well as random effects, the method allows for prediction of error rates when the authentication system is applied to potentially larger and/or different groups of subjects than those originally documented in the database. We also extend the model to allow for prediction of the probability of a false alarm on a "watch-list" as a function of the list size. We consider application of our methodology to three different face authentication systems: a filter-based system, a Gaussian mixture model (GMM)-based system, and a system based on frequency domain representation of facial asymmetry
Beard and mustache segmentation using sparse classifiers on self-quotient images

In this paper, we propose a novel system for beard and mustache detection and segmentation in challenging facial images. Our system first eliminates illumination artifacts using the self-quotient algorithm. A sparse classifier is then used on these self-quotient images to classify a region as either containing skin or facial hair. We conduct experiments on the MBGC and color FERET databases to demonstrate the effectiveness of our proposed system.
Preliminary feasibility analysis of remote subject identification during hemodynamic monitoring by Radio Frequency Impedance

Non-contact, non-invasive monitoring of hemodynamic parameters would be ideal for medical monitoring in a variety of environments. Radio Frequency Impedance Interrogation (RFII) measures hemodynamic function via resonance frequency coupling to a hydrophilic protein molecule. While the application of this technology to hemodynamic monitoring has demonstrated initial success, this preliminary study examined the use of RFII for subject identification by waveform signal analysis, which would allow confirmation of the identity of a subject in an operational setting prior to rescue efforts. Preliminary results demonstrate an excellent recognition rate using the RFII signature and pattern classification. Each individual has a consistent pattern during the initial waveform identification period that is visually distinct from the other individuals in the data set. These results suggest that RFII may be of great utility in the pre-hospital triage setting for patient monitoring and for the rapid identification of subjects in the operational setting.
Redundant Class-Dependence Feature Analysis Based on Correlation Filters Using FRGC2.0 Data

In this paper we propose a new method called redundant class-dependence feature analysis (CFA) based on the advanced correlation filters to perform robust face recognition on the Face Recognition Grand Challenge (FRGC) data set. The FRGC contains a large corpus of data and a set of challenge problems. The data is divided into training and validation partitions, with the standard still-image training partition consisting of 12,800 images, and the validation partition consisting of 16,028 controlled still images, 8,014 uncontrolled stills, and 4,007 3D scans. We have tested the proposed CFA method and compared it with the PCA and LDA methods in a recognition scenario on the FRGC2.0 data. The preliminary results show that the CFA outperforms the other two compared methods in our experiments. We also show the improved performance of the CFA method on the FRGC experiments #1 and #4.
An Automatic Iris Occlusion Estimation Method Based on High-Dimensional Density Estimation

Iris masks play an important role in iris recognition. They indicate which part of the iris texture map is useful and which part is occluded or contaminated by noisy image artifacts such as eyelashes, eyelids, eyeglasses frames, and specular reflections. The accuracy of the iris mask is extremely important. The performance of the iris recognition system will decrease dramatically when the iris mask is inaccurate, even when the best recognition algorithm is used. Traditionally, people used the rule-based algorithms to estimate iris masks from iris images. However, the accuracy of the iris masks generated this way is questionable. In this work, we propose to use Figueiredo and Jain's Gaussian Mixture Models (FJ-GMMs) to model the underlying probabilistic distributions of both valid and invalid regions on iris images. We also explored possible features and found that Gabor Filter Bank (GFB) provides the most discriminative information for our goal. Finally, we applied Simulated Annealing (SA) technique to optimize the parameters of GFB in order to achieve the best recognition rate. Experimental results show that the masks generated by the proposed algorithm increase the iris recognition rate on both ICE2 and UBIRIS dataset, verifying the effectiveness and importance of our proposed method for iris occlusion estimation.
Enhancing Interior and Exterior Deep Facial Features for Face Detection in the Wild

Although face detection has been intensely studied for decades, it is still a challenging topic due to numerous conditions, e.g. heavy occlusions, low resolutions, extreme poses, non-face patterns that look like human faces, etc. This paper proposes a novel region-based ConvNet to address these issues. Our approach enhances the interior deep facial features and explicitly incorporates the exterior deep features. The enhanced interior features provide fine details for small faces. The exterior features capture the local information surrounding the face, supporting the detection under challenging conditions. Experiments show that our proposed components improve the baseline method significantly. Additionally, our approach consistently achieves competitive performance in four challenging databases, i.e. Wider Face, AFW, PASCAL Faces, and FDDB. We also introduce a new challenging non-face dataset 1 of 6,000 images to benchmark false positive rates for future research.
Automatic eye-level height system for face and iris recognition systems

In this paper we present a fully automated mobile camera device platform that will automatically adjust to the eye-levels of the persons in front or approaching the system. This system serves as a front-end to aid the face recognition and iris recognition systems during both the enrollment and verification of different people of variable heights. Currently most systems are positioned at fixed heights and require subjects who are very tall or short to adjust themselves for enrollment. In some cases this requires the enrollment system operator (or officer) to adjust the camera system accordingly to the user's height. This leads to much failure to acquire errors. The current U.S. VISIT system, Immigration officers manually move the camera goose-necks to adjust to the height of the visitors for photographing. The system presented in this paper can fully automate this process by automatically adjusting the height of the camera (or other biometric device such as iris acquisition systems) to provide a good enrollment/verification photo image for matching using the automated face detection to drive the biometric sensors to the appropriate height level. The need for such a system can help the results obtained by the Independent Testing of Iris Recognition Technology (ITIRT) and face recognition systems that can lead to minimal user co-operation.
Fusion of region-based representations for gender identification

Much of the current work on gender identification relies on legacy datasets of heavily controlled images with minimal facial appearance variations. As studies explore the effects of adding elements of variation into the data, they have met challenges in achieving granular statistical significance due to the limited size of their datasets. In this study, we aim to create a classification framework that is robust to non-studio, uncontrolled, real-world images. We show that the fusion of separate linear classifiers trained on smart-selected local patches achieves 90% accuracy, which is a 5% improvement over a baseline linear classifier on a straightforward pixel representation. These results are reported on our own uncontrolled database of ∼26; 700 images collected from the Web.
A Bayesian Approach to Deformed Pattern Matching of Iris Images

We describe a general probabilistic framework for matching patterns that experience in-plane nonlinear deformations, such as iris patterns. Given a pair of images, we derive a maximum a posteriori probability (MAP) estimate of the parameters of the relative deformation between them. Our estimation process accomplishes two things simultaneously: it normalizes for pattern warping and it returns a distortion-tolerant similarity metric which can be used for matching two nonlinearly deformed image patterns. The prior probability of the deformation parameters is specific to the pattern-type and, therefore, should result in more accurate matching than an arbitrary general distribution. We show that the proposed method is very well suited for handling iris biometrics, applying it to two databases of iris images which contain real instances of warped patterns. We demonstrate a significant improvement in matching accuracy using the proposed deformed Bayesian matching methodology. We also show that the additional computation required to estimate the deformation is relatively inexpensive, making it suitable for real-time applications
Performance Evaluation of Face Recognition using Visual and Thermal Imagery with Advanced Correlation Filters

This paper presents the face recognition performance evaluation using visual and thermal infrared (IR) face images with correlation filter methods. New correlation filter designs have shown to be distortion invariant and the advantages of using thermal IR images are due to their invariance to visible illumination variations. A combined use of thermal IR image data and correlation filters makes a viable means for improving the performance of face recognition techniques, especially beyond visual spectrum. Subset of Equinox databases are used for the performance evaluation. Among various advanced correlation filters, minimum average correlation energy (MACE) filters and optimum trade-off synthetic discriminant function (OTSDF) filters are used in our experiments. We show that correlation filters perform well when the size of face is of significantly low resolution (e.g. 20x20 pixels). Performing robust face recognition using low resolution images has many applications including performing human identification at a distance (HID). The eyeglass detection and removal in thermal images are processed to increase the performance in thermal face recognition. We show that we can outperform commercial face recognition algorithms such as FaceIt® based on Local Feature Analysis (LFA).
Face Recognition with Kernel Correlation Filters on a Large Scale Database

Recently, direct linear discriminant analysis (D-LDA) and Gram-Schmidt LDA methods have been proposed for face recognition. By also utilizing some of the null-space of the within-class scatter matrix, they exhibit better performance compared to Fisherfaces and eigenfaces. However, these linear subspace methods may not discriminate faces well due to large nonlinear distortions in the face images. Redundant class dependence feature analysis (CFA) method exhibits superior performance compared to other methods by representing nonlinear features well. We show that with a proper choice of kernel parameters used with the proposed kernel correlation filters within the CFA framework, the overall face recognition performance is significantly improved. We present results of this proposed approach on a large scale database from the face recognition grand challenge (FRGC) which contains over 36,000 images
Subspace-Based Discrete Transform Encoded Local Binary Patterns Representations for Robust Periocular Matching on NIST’s Face Recognition Grand Challenge

In this paper, we employ several subspace representations (principal component analysis, unsupervised discriminant projection, kernel class-dependence feature analysis, and kernel discriminant analysis) on our proposd discrete transform encoded local binary patterns (DT-LBP) to match periocular region on a large data set such as NIST's face recognition grand challenge (FRGC) ver2 database. We strictly follow FRGC Experiment 4 protocol, which involves 1-to-1 matching of 8014 uncontrolled probe periocular images to 16 028 controlled target periocular images (~128 million pairwise face match comparisons). The performance of the periocular region is compared with that of full face with different illumination preprocessing schemes. The verification results on periocular region show that subspace representation on DT-LBP outperforms LBP significantly and gains a giant leap from traditional subspace representation on raw pixel intensity. Additionally, our proposed approach using only the periocular region is almost as good as full face with only 2.5% reduction in verification rate at 0.1% false accept rate, yet we gain tolerance to expression, occlusion, and capability of matching partial faces in crowds. In addition, we have compared the best standalone DT-LBP descriptor with eight other state-of-the-art descriptors for facial recognition and achieved the best performance. The two general frameworks are our major contribution: 1) a general framework that employs various generative and discriminative subspace modeling techniques for DT-LBP representation and 2) a general framework that encodes discrete transforms with local binary patterns for the creation of robust descriptors.