Robust Face Recognition via Adaptive Sparse Representation

Abstract

Abstract-Sparse Representation (or coding) based Classification (SRC) has gained great success in face recognition in recent years. However, SRC emphasizes the sparsity too much and overlooks the correlation information which has been demonstrated to be critical in real-world face recognition problems. Besides, some work considers the correlation but overlooks the discriminative ability of sparsity. Different from these existing techniques, in this paper, we propose a framework called Adaptive Sparse Representation based Classification (ASRC) in which sparsity and correlation are jointly considered. Specifically, when the samples are of low correlation, ASRC selects the most discriminative samples for representation, like SRC; when the training samples are highly correlated, ASRC selects most of the correlated and discriminative samples for representation, rather than choosing some related samples randomly. In general, the representation model is adaptive to the correlation structure, which benefits from both 1-norm and 2-norm. Extensive experiments conducted on publicly available data sets verify the effectiveness and robustness of the proposed algorithm by comparing it with state-of-the-art methods.

Gene Feature Extraction Based on Nonnegative Dual Graph Regularized Latent Low-Rank Representation

“…SRC has been successfully applied in the field of image classification and has achieved relatively ideal experimental results [31]. Similar to the image data, the gene expression profile is also composed by a series of high redundancy and heavy noise of gene samples.…”

Semi-Coupled Convolutional Sparse Learning for Image Super-Resolution

“…Specifically, consider a redundant dictionary D, SC encodes a signal x by Dα, where α is the sparse coefficient vector. The sparse representation framework has been widely used in computer vision tasks, such as color image restoration [29], robust face recognition [30,31], object detection [32,33], image segmentation [34,35], and image classification [36,37], and has achieved state-of-the-art results. Specifically, supervised dictionary learning has a good application in image classification [38], image super-resolution [39], and audio signal recognition [40], and supervised deep dictionary learning has been successfully used in classification [41] and image quality assessment [42].…”

Access Control in Smart Homes by Android-Based Liveness Detection

“…Light conditions, facial expressions, face rotations, age, physical radical changes, as well as the presence of partial occlusions, such as glasses, facial hair, or hair, covering part of the face, are just some of the elements which make the automatic face recognition challenging. Despite these issues, algorithms that allow to obtain satisfactory results of personal identification have been proposed in the literature [19][20][21].…”