Collaborative neighbor representation based classification using l2-minimization approach
Pattern Recognition Letters
Efficient misalignment-robust representation for real-time face recognition
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part I
Robust and practical face recognition via structured sparsity
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part IV
Face recognition via Weighted Sparse Representation
Journal of Visual Communication and Image Representation
Weighted group sparse representation based on robust regression for face recognition
CCBR'12 Proceedings of the 7th Chinese conference on Biometric Recognition
Letters: Two-dimensional relaxed representation
Neurocomputing
Engineering Applications of Artificial Intelligence
Semi-supervised learning via sparse model
Neurocomputing
An adaptive regularization method for sparse representation
Integrated Computer-Aided Engineering
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In many problems in computer vision, data in multiple classes lie in multiple low-dimensional subspaces of a high-dimensional ambient space. However, most of the existing classification methods do not explicitly take this structure into account. In this paper, we consider the problem of classification in the multi-sub space setting using sparse representation techniques. We exploit the fact that the dictionary of all the training data has a block structure where the training data in each class form few blocks of the dictionary. We cast the classification as a structured sparse recovery problem where our goal is to find a representation of a test example that uses the minimum number of blocks from the dictionary. We formulate this problem using two different classes of non-convex optimization programs. We propose convex relaxations for these two non-convex programs and study conditions under which the relaxations are equivalent to the original problems. In addition, we show that the proposed optimization programs can be modified properly to also deal with corrupted data. To evaluate the proposed algorithms, we consider the problem of automatic face recognition. We show that casting the face recognition problem as a structured sparse recovery problem can improve the results of the state-of-the-art face recognition algorithms, especially when we have relatively small number of training data for each class. In particular, we show that the new class of convex programs can improve the state-of-the-art face recognition results by 10% with only 25% of the training data. In addition, we show that the algorithms are robust to occlusion, corruption, and disguise.