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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TL;DR: A spatial-relation-constrained classifier is designed to utilize the output of MFJSC and the spatial dependences to annotate images more precisely and Experiments on a data set of 21 land-use classes and QuickBird images show the discriminative power of MF JSC andThe effectiveness of the annotation framework.
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Exploiting sparsity and equation-free architectures in complex systems
TL;DR: This work argues that data-driven dimensionality reduction methods integrate naturally with sparse sensing in the context of complex systems, and demonstrates the advantages of combining these methods on three prototypical examples: classification of dynamical regimes, optimal sensor placement, and equation-free dynamic model reduction.
Proceedings ArticleDOI
Bayesian compressive sensing for phonetic classification
TL;DR: A novel bayesian compressive sensing (CS) technique for phonetic classification is introduced and it is found that this method outperforms the SVM, kNN and Gaussian Mixture Model (GMM) methods on the TIMIT phonetics classification task.
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Automated Depression Diagnosis Based on Facial Dynamic Analysis and Sparse Coding
TL;DR: A novel method of exploring facial region visual-based nonverbal behavior analysis for automatic depression diagnosis by leveraging dynamic feature descriptors extracted from facial region subvolumes and sparse coding is employed to implicitly organize the extracted feature descriptor for depression diagnosis.
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Dynamic Screening: Accelerating First-Order Algorithms for the Lasso and Group-Lasso
TL;DR: There is an even more efficient way to screen the dictionary and obtain a greater acceleration: inside each iteration of the regression algorithm, one may take advantage of the algorithm computations to obtain a new screening test for free with increasing screening effects along the iterations.
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