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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Simultaneous Codeword Optimization (SimCO) for Dictionary Update and Learning
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Image Set based Collaborative Representation for Face Recognition
TL;DR: The proposed model naturally and effectively extends the image-based collaborative representation to an image set based one, and the superiority of the proposed method to state-of-the-art ISFR methods under different set sizes in terms of both recognition rate and efficiency is shown.
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Discriminative Block-Diagonal Representation Learning for Image Recognition
TL;DR: The proposed discriminative block-diagonal low-rank representation (BDLRR) method for recognition not only shows superior potential on image recognition but also outperforms the state-of-the-art methods.
Book ChapterDOI
An Invitation to Compressive Sensing
Simon Foucart,Holger Rauhut +1 more
TL;DR: This first chapter formulates the objectives of compressive sensing and introduces the standard compressive problem studied throughout the book and reveals its ubiquity in many concrete situations by providing a selection of motivations, applications, and extensions of the theory.
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