Sparse representation for face recognition: A review paper (original) (raw)
Related papers
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.
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 Recognition in Low-Quality Images using Adaptive Sparse Representations
Although unconstrained face recognition has been widely studied over recent years, state-of-the-art algorithms still result in an unsatisfactory performance for low-quality images. In this paper, we make two contributions to this field: the first one is the release of a new dataset called 'AR-LQ' that can be used in conjunction with the well-known 'AR' dataset to evaluate face recognition algorithms on blurred and low-resolution face images. The proposed dataset contains five new blurred faces (at five different levels, from low to severe blurriness) and five new low-resolution images (at five different levels, from 66 ⇥ 48 to 7 ⇥ 5 pixels) for each of the hundred subjects of the 'AR' dataset. The new blurred images were acquired by using a DLSR camera with manual focus that takes an out-of-focus photograph of a monitor that displays a sharp face image. In the same way, the low-resolution images were acquired from the monitor by a DLSR at different distances. Thus, an attempt is made to acquire low-quality images that have been degraded by a real degradation process. Our second contribution is an extension of a known face recognition technique based on sparse representations (ASR) that takes into account low-resolution face images. The proposed method, called blur-ASR or bASR, was designed to recognize faces using dictionaries with different levels of blurriness. These were obtained by digitally blurring the training images, and a sharpness metric for matching blurriness between the query image and the dictionaries. These two main adjustments made the algorithm more robust with respect to low-quality images. In our experiments, bASR consistently outperforms other state-of-art methods including hand-crafted features, sparse representations, and a seven well-known deep learning face recognition techniques with and without super resolution techniques. On average, bASR obtained 88.8% of accuracy, whereas the rest obtained less than 78.4%.
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,
Multi-scale Sparse Representation for Robust Face Recognition
2011
Recently the Sparse Representation-based Classification (SRC) has been successfully used in face recognition. In SRC, a test image is coded by a linear combination of the training dictionary. In this paper, we propose a model extends from SRC named Multi-scale SRC (MSRC). The MSRC build the multi-scale dictionary for the training. A test image is then coded using this multi-scale dictionary. In addition, a voting scheme is applied which not only helps improving the recognition rate significantly, but also makes the algorithm more robust with occlusion. Experiments on representative face databases demonstrate that the MSRC is much more effective than the SRC.
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.
Literature Survey On Sparse Representation For Neural Network Based Face Detection And Recognition
2018
Face detection and recognition is a challenging problem in the field of image processing. In this paper, we reviewed some of the recent research works on face recognition. Issues with the previous face recognition techniques are , time required is more for face recognition , recognition rate and database required to store the data. To overcome these problems sparse representation based classifier technique can be used .
Sparse Representation Based Face Recognition with Limited Labeled Samples
2013 2nd IAPR Asian Conference on Pattern Recognition, 2013
Sparse representations have emerged as a powerful approach for encoding images in a large class of machine recognition problems including face recognition. These methods rely on the use of an over-complete basis set for representing an image. This often assumes the availability of a large number of labeled training images, especially for high dimensional data. In many practical problems, the number of labeled training samples are very limited leading to significant degradations in classification performance. To address the problem of lack of training samples, we propose a semi-supervised algorithm that labels the unlabeled samples through a multi-stage label propagation combined with sparse representation. In this representation, each image is decomposed as a linear combination of its nearest basis images, which has the advantage of both locality and sparsity. Extensive experiments on publicly available face databases show that the results are significantly better compared to state-of-theart face recognition methods in semi-supervised setting and are on par with fully supervised techniques.
A Sparse Representation-based Face Recognition using Dynamic Group Sparsity 4-24终稿
To improve the performance of sparse representation-based classification (SRC), the article based on the potential correlations between the elements of dictionary gets a mixed group sparsity which is composed of dynamic group sparsity and fixed-length group sparsity. To solve the structured sparsity efficiently, structured greedy algorithm (structOMP) based on coding complexity is redesigned, including adjustment of the search space of the altgorithm and its neighbor. Finally, three sparse models are compared by experiments of face recognition, and the results show that the mixed group sparsity can improve the face recognition rate of other sparse models by 10% or more in dealing with corrupted data.