Robust Face Recognition via Sparse Representation
TLDR
This work considers the problem of automatically recognizing human faces from frontal views with varying expression and illumination, as well as occlusion and disguise, and proposes a general classification algorithm for (image-based) object recognition based on a sparse representation computed by C1-minimization.Abstract:
We consider the problem of automatically recognizing human faces from frontal views with varying expression and illumination, as well as occlusion and disguise. We cast the recognition problem as one of classifying among multiple linear regression models and argue that new theory from sparse signal representation offers the key to addressing this problem. Based on a sparse representation computed by C1-minimization, we propose a general classification algorithm for (image-based) object recognition. This new framework provides new insights into two crucial issues in face recognition: feature extraction and robustness to occlusion. For feature extraction, we show that if sparsity in the recognition problem is properly harnessed, the choice of features is no longer critical. What is critical, however, is whether the number of features is sufficiently large and whether the sparse representation is correctly computed. Unconventional features such as downsampled images and random projections perform just as well as conventional features such as eigenfaces and Laplacianfaces, as long as the dimension of the feature space surpasses certain threshold, predicted by the theory of sparse representation. This framework can handle errors due to occlusion and corruption uniformly by exploiting the fact that these errors are often sparse with respect to the standard (pixel) basis. The theory of sparse representation helps predict how much occlusion the recognition algorithm can handle and how to choose the training images to maximize robustness to occlusion. We conduct extensive experiments on publicly available databases to verify the efficacy of the proposed algorithm and corroborate the above claims.read more
Citations
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Journal ArticleDOI
Dictionary-Based Face Recognition Under Variable Lighting and Pose
TL;DR: A face recognition algorithm based on simultaneous sparse approximations under varying illumination and pose that has the ability to recognize human faces with high accuracy even when only a single or a very few images per person are provided for training.
Journal ArticleDOI
Sparse Representation of Monogenic Signal: With Application to Target Recognition in SAR Images
TL;DR: The classification via sparse representation of the monogenic signal is presented for target recognition in SAR images and is robust towards noise corruption, as well as configuration and depression variations.
Journal ArticleDOI
Structured optimal graph based sparse feature extraction for semi-supervised learning
TL;DR: A novel structured optimal graph based sparse feature extraction (SOGSFE) method for semi-supervised learning is proposed, in which the local structure learning, sparse representation, and label propagation are simultaneously framed to perform data dimensionality reduction.
Journal ArticleDOI
$p$ -Laplacian Regularized Sparse Coding for Human Activity Recognition
TL;DR: The experimental results demonstrate that the proposed pLSC algorithm outperforms the manifold regularized sparse coding algorithms including the standard Laplacian regularization sparse coding algorithm with a proper p.
Journal ArticleDOI
Intra-Class Variation Reduction Using Training Expression Images for Sparse Representation Based Facial Expression Recognition
TL;DR: A new sparse representation based FER method, aiming to reduce the intra-class variation while emphasizing the facial expression in a query face image by using training expression images, which has high discriminating capability in terms of improving FER performance.
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