A new approach to Face Verification: Local Sparse Representation (original) (raw)
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Face verification using sparse representation techniques
6th International Symposium on Telecommunications (IST), 2012
We propose a face verification framework using sparse representations that integrates two ways of employing sparsity. Given an image pair (A,B) and a dictionary D, for image A(B), we generate two sparse codes, one by using the original dictionary and the other by adding B(A) into D as an augmented dictionary. Then the correlation of the sparse codes of A and B, both under the original dictionary D, measuring how similar the pair is, is referred to as the similarity score. The dissimilarity of the sparse codes of A(B), respectively under D and D+B(A), is referred to as the dissimilarity score. We exploit multiple feature transforms to obtain several scores using these two measures and fuse them by simple averaging for the situation where no training set is available or by an SVM when a training set is given. We evaluate our algorithm on the LFW dataset, where it is shown to outperform state-of-the-art methods in the unsupervised setting by a large margin and delivers very comparable performance to methods in the image restricted setting despite its simplicity.
Sparse representation for face recognition: A review paper
IET Image Processing
With the increasing use of surveillance cameras, face recognition is being studied by many researchers for security purposes. Although high accuracy has been achieved for frontal faces, the existing methods have shown poor performance for occluded and corrupt images. Recently, sparse representation based classification (SRC) has shown the state-ofthe-art result in face recognition on corrupt and occluded face images. Several researchers have developed extended SRC methods in the last decade. This paper mainly focuses on SRC and its extended methods of face recognition. SRC methods have been compared on the basis of five issues of face recognition such as linear variation, non-linear variation, undersampled, pose variation, and low resolution. Detailed analysis of SRC methods for issues of face recognition have been discussed based on experimental results and execution time. Finally, the limitation of SRC methods have been listed to help the researchers to extend the work of existing methods to resolve the unsolved issues. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.
Indian Journal of Pharmaceutical Sciences
Over the past 20 y, face recognition has drawn the attention of many researchers in various fields, such as security, psychology and engineering. On the other hand, with the advancement of technology, interactions between humans and computers will also increase. One of the key steps in this interaction is face recognition [1]. Face recognition is one of the important tools for identification in the field of biometrics and various algorithms have been proposed in relation to it. Although most of these have experienced significant advances in various applications and research areas, they still face major challenges such as lightness, gesture, expression and occlusion. There are a lot of ways in this area, but one important point that should always be considered is which feature contains very important information for identification? According to the geometry and appearance of the face, where fixed filter banks such as down sampling, Fourier, wavelet and Gabor are not used, which are suitable tools for the analysis of static signals like texture, but instead methods that adaptively extract the facial features based on the given pictures (e.g. Eigen face [2] , Fisher faces [3] and Laplacian faces [4]) are used (fig. 1). The face recognition can be performed by using these features and designing a classifier such as the nearest-neighbor (NN), the nearest-subspace (NS) or SVM. Considering the process of designing the face recognition algorithm, it is seen that the performance of the algorithm is dependent on 2 parts,
Face recognition by sparse representation
Compressed Sensing
In this chapter, we present a comprehensive framework for tackling the classical problem of face recognition, based on theory and algorithms from sparse representation. Despite intense interest in the past several decades, traditional pattern recognition theory still stops short of providing a satisfactory solution capable of recognizing human faces in the presence of real-world nuisances such as occlusion and variabilities in pose and illumination. Our new approach, called sparse representation-based classification (SRC), is motivated by a very natural notion of sparsity, namely, one should always try to explain a query image using a small number of training images from a single subject category. This sparse representation is sought via 1-minimization. We show how this core idea can be generalized and extended to account for various physical variabilities encountered in face recognition. The end result of our investigation is a full-fledged practical system aimed at security and access control applications. The system is capable of accurately recognizing subjects out of a database of several hundred subjects with state-of-the-art accuracy.
On robust biometric identity verification via sparse encoding of faces: Holistic vs local approaches
Proceedings of the International Joint Conference on Neural Networks, 2012
In the field of face recognition, Sparse Representation (SR) has received considerable attention during the past few years. Most of the related literature focuses on holistic descriptors in closed-set identification applications. The underlying assumption in identification is that the gallery always has sufficient samples per subject to linearly reconstruct a query image. Unfortunately, such assumption is easily violated in the more challenging and realistic face verification scenario. A verification algorithm is required to determine if two faces (where one or both have not been seen before) belong to the same person, while explicitly taking into account the possibility of impostor attacks. In this paper, we first discuss why most of the SR literature is not applicable to verification problems. Motivated by the success of bag-of-words methods in the field of object recognition, which describe an image as a set of local patches or interest points, we then propose to tackle the verification problem by encoding each local face patch through SR. The locally encoded sparse vectors are pooled to form regional descriptors, where each descriptor covers a relatively large portion of the face. Experiments in various challenging conditions show that the proposed method achieves high and robust verification performance.
2011 IEEE International Conference on Multimedia and Expo, 2011
Recently, local binary patterns (LBP) based descriptors and sparse representation based classification (SRC) become both eminent techniques in face recognition. Preliminary techniques of combining LBP and SRC have been proposed in the literature. However, the state-of-art method suffers from the "curse of dimensionality" for real world scenarios. In this paper, a novel face recognition algorithm of combining LBP with SRC is proposed; in which the dimensionality problem is resolved by divide-and-conquer and the discriminative power is strengthen via its pyramid architecture. The proposed face recognition method is evaluated on AR Face Database and yields very impressive results.
Sparse Representation and Face Recognition
International Journal of Image, Graphics and Signal Processing, 2018
Now a days application of sparse representation are widely spreading in many fields such as face recognition. For this usage, defining a dictionary and choosing a proper recovery algorithm plays an important role for the method accuracy. In this paper, two type of dictionaries based on input face images, the method named SRC, and input extracted features, the method named MKD-SRC, are constructed. SRC fails for partial face recognition whereas MKD-SRC overcomes the problem. Three extension of MKD-SRC are introduced and their performance for comparison are presented. For recommending proper recovery algorithm, in this paper, we focus on three greedy algorithms, called MP, OMP, CoSaMP and another called Homotopy. Three standard data sets named AR, Extended Yale-B and Essex University are used to asses which recovery algorithm has an efficient response for proposed methods. The preferred recovery algorithm was chosen based on achieved accuracy and run time.
Sparse Representation Based Face Recognition Using Weighted Regions
Face recognition is a challenging research topic, especially when the training (gallery) and recognition (probe) images are acquired using different cameras under varying conditions. Even a small noise or occlusion in the images can compromise the accuracy of recognition. Lately, sparse encoding based classification algorithms gave promising results for such uncontrollable scenarios. In this paper, we introduce a novel methodology by modeling the sparse encoding with weighted patches to increase the robustness of face recognition even further. In the training phase, we define a mask (i.e., weight matrix) using a sparse representation selecting the facial regions, and in the recognition phase, we perform comparison on selected facial regions. The algorithm was evaluated both quantitatively and qualitatively using two comprehensive surveillance facial image databases, i.e., SCface andMFPV, with the results clearly superior to common state-of-the-art methodologies in different scenarios.
Face Verification by Using Sparse Representation Algorithm in Compressive Sensing
Abstract— Face verification, a part of security systems, is widely used in many applications. This biometric application is more hygienic comparing with other biometric systems since there is no direct contact between face and camera. Moreover, it is a low cost setup. A sparse representation algorithm as a part of compressive sensing was used in this paper with the accuracy achieved up to 88% during non-optimized sensing matrix and with the average time process of 4.37 seconds. The accuracy achieved was 94% during optimized sensing matrix but the average time process was slower at 8.73 seconds. Encryption process also happened during the image compression which not only reduced the size of the image but also increased the data security.