nils-herrmann/clean_scirepeval huggingface dataset:
The effect of low-bit rate JPEG2000 wavelet images on face authentication using advanced correlation filters

One approach in performing face recognition on mobile devices, due to its limited computational resources is to transmit the captured face images using the mobile phone connection and run the verification on a remote station. It may be necessary to transmit a compressed version of the image due to limitations in the communication bandwidth. This paper proposes the use of JPEG 2000 wavelet based image compression standard to compress the face images to very low bit-rates for transmission over the low-bandwidth communication channel. At the receiver end, the face images are reconstructed using a JPEG2000 decoder and fed into the verification algorithm. We explore the performance of advanced correlation filters like the minimum average correlation energy (MACE) filter and its variants using face images captured in different illumination conditions and encoded using different bit-rates under the JPEG2000 wavelet encoding standard. Evaluation of the performance of such filters is done using illumination variations from the CMU pose, illumination, and expression (PIE) face database. We demonstrate the tolerance of such filters to noisy versions of such images with variations in illumination condition.
Pareto-optimal discriminant analysis

In this work, we have proposed the Pareto-optimal discriminant analysis (PDA), an optimally designed linear subspace learning method that harnesses advantages across many well-known methods such as PCA, LDA, UDP and LPP. By optimizing over the joint objective function and carrying out an alternative coefficients updating scheme, we are able to obtain a linear subspace which is optimized to truly maximize the objective function in discriminant analysis. The proposed method also provides flexibility for formulating the linear transformation matrix in an overcomplete fashion, allowing for a sparse representation. We have shown, in the context of large scale unconstrained face recognition and illumination invariant face recognition, that our proposed PDA significantly outperforms other linear subspace methods.
An Augmented Linear Discriminant Analysis Approach for Identifying Identical Twins with the Aid of Facial Asymmetry Features

In this work, we have proposed an Augmented Linear Discriminant Analysis (ALDA) approach to identify identical twins. It learns a common subspace that not only can identify from which family the individual comes, but also can distinguish between individuals within the same family. We evaluate the ALDA against the traditional LDA approach for subspace learning on the Notre Dame twin database. We have shown that the proposed ALDA method with the aid of facial asymmetry features significantly outperforms other well-established facial descriptors (LBP, LTP, LTrP), and the ALDA subspace method does a much better job in distinguishing identical twins than LDA. We are able to achieve 48.50% VR at 0.1% FAR for identifying family membership of identical twin individuals in the crowd and an averaged 82.58% VR at 0.1% FAR for verifying identical twin individuals within the same family, a significant improvement over traditional descriptors and traditional LDA method.
Illumination Tolerant Face Recognition Using a Novel Face From Sketch Synthesis Approach and Advanced Correlation Filters

Current state-of-the-art approach for performing face sketch recognition transforms all the test face images into sketches, and then performs recognition on sketch domain using the sketch composite. In our approach we propose the opposite; which has advantages in a real-time system; we propose to generate a realistic face image from the composite sketch using a hybrid subspace method and then build an illumination tolerant correlation filter which can recognize the person under different illumination variations from a surveillance video footage. We show how effective proposed algorithm works on the CMU PIE (pose illumination and expression) database
Breaking the Limitation of Manifold Analysis for Super-Resolution of Facial Images

A novel method for robust super-resolution of face images is proposed in this paper. Face super-resolution is a particular interest in video surveillance where face images have typically very low-resolution quality and there is a need to apply face enhancement or super-resolution algorithms. In this paper, we apply a manifold learning method which has hardly been used for super-resolution. A manifold is a natural generalization of a Euclidean space to a locally Euclidean space. Manifold learning algorithms are more powerful than other pattern recognition methods which analyze a Euclidean space because they can reveal the underlying nonlinear distribution of the face space; however, there are some practical problems which prevent these algorithms from being applied to super-resolution. Almost all of the manifold learning methods cannot generate mapping functions for new test images which are absent from a training set. Another factor is that super-resolution seeks to recover a high-dimensional image from a lower-dimensional one while manifold learning methods perform the exact opposite as they are applied to dimensionality reduction. In this paper, we break the limitation of applying manifold learning methods for face super-resolution by proposing a novel method using locality preserving projections (LPP).
Facecut - a robust approach for facial feature segmentation

Segmentation of facial features is a key pre-processing step in enabling facial recognition, building of 3D facial models, expression analysis, and pose estimation. Recently, graph cuts based algorithms have been adapted to carry out this task but many of these methods require manual initialization of points in the foreground and background. In this paper, we propose a novel and fully automatic approach, named Face-Cut, to perform accurate facial feature segmentation. FaceCut combines the positive features of the Modified Active Shape Model (MASM) and GrowCut algorithms to ensure highly accurate and completely automatic segmentation of facial features. We demonstrate the effectiveness of FaceCut on images from two challenging databases.
Hallucinating faces in the dark

Correction of uneven illumination in face images has been a task of intense research with its applicability being extensive. Most algorithms look to improve recognition accuracy failing to produce visually good results or viceversa. In this work, we present a data-driven illumination correction algorithm which simultaneously produces visually good results and improves recognition accuracy among faces. The illumination behavior on the generic structure of faces is learnt by training pyramid sub-spaces on illumination images. An algorithm is proposed to iteratively project illumination information onto these sub-spaces and filter out other information projected onto them. Results are presented on the CMU-PIE and Extended Yale-B databases. Performance is measured using PSNR measures for relighting images and improvement in the performance of face recognition rate in comparison to other contemporary performers.
Local Binary Convolutional Neural Networks

We propose local binary convolution (LBC), an efficient alternative to convolutional layers in standard convolutional neural networks (CNN). The design principles of LBC are motivated by local binary patterns (LBP). The LBC layer comprises of a set of fixed sparse pre-defined binary convolutional filters that are not updated during the training process, a non-linear activation function and a set of learnable linear weights. The linear weights combine the activated filter responses to approximate the corresponding activated filter responses of a standard convolutional layer. The LBC layer affords significant parameter savings, 9x to 169x in the number of learnable parameters compared to a standard convolutional layer. Furthermore, the sparse and binary nature of the weights also results in up to 9x to 169x savings in model size compared to a standard convolutional layer. We demonstrate both theoretically and experimentally that our local binary convolution layer is a good approximation of a standard convolutional layer. Empirically, CNNs with LBC layers, called local binary convolutional neural networks (LBCNN), achieves performance parity with regular CNNs on a range of visual datasets (MNIST, SVHN, CIFAR-10, and ImageNet) while enjoying significant computational savings.
A pixel-wise, learning-based approach for occlusion estimation of iris images in polar domain

On normalized iris images, there are many kinds of noises, such as eyelids, eyelashes, shadows or specular reflections, that often occlude the true iris texture. If high recognition rate is desired, those occluded areas must be estimated accurately in order for them to be excluded during the matching stage. In this paper, we propose a unified, probabilistic and learning-based approach to estimate all kinds of occlusions within one unified model. Experiments have shown that our method not only estimates occlusion very accurately, but also does it with high speed, which makes it useful for practical iris recognition systems.
Individual Kernel Tensor-Subspaces for Robust Face Recognition: A Computationally Efficient Tensor Framework Without Requiring Mode Factorization

Facial images change appearance due to multiple factors such as different poses, lighting variations, and facial expressions. Tensors are higher order extensions of vectors and matrices, which make it possible to analyze different appearance factors of facial variation. Using higher order tensors, we can construct a multilinear structure and model the multiple factors of face variation. In particular, among the appearance factors, the factor of a person's identity modeled by a tensor structure can be used for face recognition. However, this tensor-based face recognition creates difficulty in factorizing the unknown parameters of a new test image and solving for the person-identity parameter. In this paper, to break this limitation of applying the tensor-based methods to face recognition, we propose a novel tensor approach based on an individual-modeling method and nonlinear mappings. The proposed method does not require the problematic tensor factorization and is more efficient than the traditional TensorFaces method with respect to computation and memory. We set up the problem of solving for the unknown factors as a least squares problem with a quadratic equality constraint and solve it using numerical optimization techniques. We show that an individual-multilinear approach reduces the order of the tensor so that it makes face-recognition tasks computationally efficient as well as analytically simpler. We also show that nonlinear kernel mappings can be applied to this optimization problem and provide more accuracy to face-recognition systems than linear mappings. In this paper, we show that the proposed method, individual kernel TensorFaces, produces the better discrimination power for classification. The novelty in our approach as compared to previous work is that the Individual Kernel TensorFaces method does not require estimating any factor of a new test image for face recognition. In addition, we do not need to have any a priori knowledge of or assumption about the factors of a test image when using the proposed method. We can apply individual kernel TensorFaces even if the factors of a test image are absent from the training set. Based on various experiments on the Carnegie Mellon University Pose, Illumination, and Expression database, we demonstrate that the proposed method produces reliable results for face recognition.
Analysis of Low-Dimensional Radio-Frequency Impedance-Based Cardio-Synchronous Waveforms for Biometric Authentication

Over the past two decades, there have been a lot of advances in the field of pattern analyses for biomedical signals, which have helped in both medical diagnoses and in furthering our understanding of the human body. A relatively recent area of interest is the utility of biomedical signals in the field of biometrics, i.e., for user identification. Seminal work in this domain has already been done using electrocardiograph (ECG) signals. In this paper, we discuss our ongoing work in using a relatively recent modality of biomedical signals-a cardio-synchronous waveform measured using a Radio-Frequency Impedance-Interrogation (RFII) device for the purpose of user identification. Compared to an ECG setup, this device is noninvasive and measurements can be obtained easily and quickly. Here, we discuss the feasibility of reducing the dimensions of these signals by projecting onto various subspaces while still preserving interuser discriminating information. We compare the classification performance using classical dimensionality reduction methods such as principal component analysis (PCA), independent component analysis (ICA), random projections, with more recent techniques such as K-SVD-based dictionary learning. We also report the reconstruction accuracies in these subspaces. Our results show that the dimensionality of the measured signals can be reduced by 60 fold while maintaining high user identification rates.
Pokerface: Partial order keeping and energy repressing method for extreme face illumination normalization

We propose a new method called the Pokerface for extreme face illumination normalization. The Pokerface is a two-phase approach. It first aims at maximizing the minimum gap between adjacently-valued pixels while keeping the partial ordering of the pixels in the face image under extreme illumination condition, an intuitive effort based on order theory to unveil the underlying structure of a dark image. This optimization can be formulated as a feasibility search problem and can be efficiently solved by linear programming. It then smooths the intermediate representation by repressing the energy of the gradient map. The smoothing step is carried out by total variation minimization and sparse approximation. The illumination normalized faces using our proposed Pokerface not only exhibit very high fidelity against neutrally illuminated face, but also allow for a significant improvement in face verification experiments using even the simplest classifier. Simultaneously achieving high level of faithfulness and expressiveness is very rare among other methods. These conclusions are drawn after benchmarking our algorithm against 22 prevailing illumination normalization techniques on both the CMU Multi-PIE database and Extended YaleB database that are widely adopted for face illumination problems.
Gaussian mixture models based on the frequency spectra for human identification and illumination classification

The importance of Fourier domain phase in human face identification is well-established Hayes et al., (1982). It therefore seems natural that identification tools based on phase features should be very efficient. In this paper we introduce a model-based approach using Gaussian mixture models (GMM) based on phase for performing human identification. Identification is performed using a MAP estimate and we show that we are able to achieve misclassification error rates as low as 2% on a database with 65 individuals with extreme illumination variations. The proposed method is easily adaptable to deal with other distortions such as expressions and poses, and hence this establishes its robustness to intra-personal variations. Finally, we demonstrate that GMM based on the Fourier domain magnitude is effective for illumination normalization, so that near perfect identification is obtained using the reconstructed illumination-free images.
Automatic segmentation of cardiosynchronous waveforms using cepstral analysis and continuous wavelet transforms

The cardiosynchronous signal obtained through Radio Frequency Impedance Interrogation (RFII) is a non-invasive method for monitoring hemodynamics with potential applications in combat triage and biometric identification. The RFII signal is periodic in nature dominated by the heart beat cycle. The first step in both of these applications is to segment the signal by identifying a fiducial point in each heart beat cycle. A continuous wavelet transform was utilized to locate the fiducial points with high temporal resolution. Cepstral Analysis was used to estimate the average heart rate to focus on the appropriate portion of the time-frequency spectrum. Robust heartbeats from RFII signals collected from four subjects were segmented using this method.
Hallucinating the Full Face from the Periocular Region via Dimensionally Weighted K-SVD

Identifying a suspect wearing a mask (where only the suspect's periocular region is visible) is one of the toughest real-world challenges in biometrics that exist. In this paper, we present a practical method to hallucinate the full frontal face given only the periocular region of a face. This is an important problem faced in many law-enforcement applications on almost a daily basis. In such real-world situations, we only have access to the periocular region of a person's face. Unfortunately commercial matchers are unable to process these images successfully. We propose in this paper, an approach that will reconstruct the entire frontal face using just the periocular region. We empirically show that our reconstruction technique, based on a modified sparsifying dictionary learning algorithm, can effectively reconstruct faces which we show are actually very similar to the original ground-truth faces. Further, our method is open set, thus can reconstruct any face not seen in training. We show the real-world applicability of method by benchmarking face verification results using the reconstructed faces to show that they still match competitively compared to the original faces when evaluated under a large-scale face verification protocol such as NIST's FRGC protocol where over 256 million face matches are made.
A robust approach to facial ethnicity classification on large scale face databases

In this paper, we propose a novel approach using Kernel Class-dependent Feature Analysis (KCFA) combined with facial color based features to tackle the problem of ethnicity classification on large scale face databases. In our approach, a new design of multiple filtered responses of the Kernel Class-dependent Feature Analysis is used for ethnicity classes. In order to improve the robustness of our system, the facial color based features are also employed to incorporate with the filtered responses to extract the stable ethnicity features. Compared to previous approaches, our proposed method achieves the best accuracy of ethnicity classification on large industrial based face databases and the standard MBGC face database.
Can your eyebrows tell me who you are?

In this paper, we will show that eyebrow region can be used as a stand-alone biometric for recognition. We compare the recognition performance of eyebrow with eye and full face following NIST's FRGC Experiment 4 protocol, the most harsh experiment in FRGC ver2.0 database which involves matching 8,014 uncontrolled probe images from 466 subjects to 16,028 controlled target images from the same 466 subjects (∼128 million comparisons). We are the first to evaluate the recognition performance of eyebrow on such a big database and compare to the performance of full face. We will use multiple discrete transform encoded features to capture the local information of eyebrow as well as full face. The experimental results amazingly show that compared with the full face, the eyebrow region has 5/6 drop in size but only 1/6 drop in rank-1 identification rate.
Reformulating Level Sets as Deep Recurrent Neural Network Approach to Semantic Segmentation

Variational Level Set (LS) has been a widely used method in medical segmentation. However, it is limited when dealing with multi-instance objects in the real world. In addition, its segmentation results are quite sensitive to initial settings and highly depend on the number of iterations. To address these issues and boost the classic variational LS methods to a new level of the learnable deep learning approaches, we propose a novel definition of contour evolution named <italic>Recurrent Level Set (RLS)</italic><xref ref-type="fn" rid="fn1"><sup>1</sup></xref> to employ Gated Recurrent Unit under the energy minimization of a variational LS functional. The curve deformation process in RLS is formed as a hidden state evolution procedure and updated by minimizing an energy functional composed of fitting forces and contour length. By sharing the convolutional features in a <italic>fully end-to-end trainable framework</italic>, we extend RLS to <italic>Contextual RLS (CRLS)</italic> to address semantic segmentation in the wild. The experimental results have shown that our proposed RLS improves both computational time and segmentation accuracy against the classic variational LS-based method whereas the fully end-to-end system CRLS achieves competitive performance compared to the state-of-the-art semantic segmentation approaches.<fn id="fn1"><label><sup>1</sup></label><p>Source codes will be publicly available.</p></fn
Class Dependent Kernel Discrete Cosine Transform Features for Enhanced Holistic Face Recognition in FRGC-II

Face recognition is one of the least intrusive biometric modalities that can be used to identify individuals from surveillance video. In such scenarios the users are under the least co-operative conditions and thus the ability to perform robust face recognition in such scenarios is very challenging. In this paper we focus on improving the face recognition performance on a large database with over 36,000 facial images from the face recognition grand challenge phase-II data collected by University of Notre Dame. We particularly focus on Experiment 4 which is the most challenging and captured in uncontrolled conditions where the baseline PCA algorithm yields 12% verification rate at 0.1% FAR. We propose a novel approach using class-dependent kernel discrete cosine transform features which improves the performance significantly yielding a 91.33% verification rate at 0.1% FAR, and we also show that by working in the DCT transform domain for obtaining nonlinear features is more optimal than working in the original spatial-pixel domain which only yields a verification rate of 85% at 0.1% FAR. Thus our proposed method outperforms the baseline by 79.33% in verification rate @ 0.1% false acceptance rate
Fast and robust self-training beard/moustache detection and segmentation

Facial hair detection and segmentation play an important role in forensic facial analysis. In this paper, we propose a fast, robust, fully automatic and self-training system for beard/moustache detection and segmentation in challenging facial images. In order to overcome the limitations of illumination, facial hair color and near-clear shaving, our facial hair detection self-learns a transformation vector to separate a hair class and a non-hair class from the testing image itself. A feature vector, consisting of Histogram of Gabor (HoG) and Histogram of Oriented Gradient of Gabor (HOGG) at different directions and frequencies, is proposed for both beard/moustache detection and segmentation in this paper. A feature-based segmentation is then proposed to segment the beard/moustache from a region on the face that is discovered to contain facial hair. Experimental results have demonstrated the robustness and effectiveness of our proposed system in detecting and segmenting facial hair in images drawn from three entire databases i.e. the Multiple Biometric Grand Challenge (MBGC) still face database, the NIST color Facial Recognition Technology FERET database and a large subset from Pinellas County database.