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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Semisupervised Sparse Manifold Discriminative Analysis for Feature Extraction of Hyperspectral Images
TL;DR: The proposed semisupervised sparse manifold discriminative analysis method not only inherits the merits of MSR to reveal the sparse manifold properties of data but also enhances interclass separability and intraclass compactness to improve the discriminating power for classification.
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Weighted Couple Sparse Representation With Classified Regularization for Impulse Noise Removal
TL;DR: A weighted couple sparse representation model is presented to remove IN, where the dictionary is directly trained on the noisy raw data by addressing a weighted rank-one minimization problem, which can capture more features of the original data.
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Low-Rank Quaternion Approximation for Color Image Processing
TL;DR: Extensive evaluations for color image denoising and inpainting tasks verify that LRQA achieves better performance over several state-of-the-art sparse representation and LRMA-based methods in terms of both quantitative metrics and visual quality.
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Damage identification scheme based on compressive sensing
Ying Wang,Hong Hao +1 more
TL;DR: Both numerical and experimental verification results confirm that the proposed CS-based damage identification scheme will be a promising tool for structural health monitoring and will be one of the first few applications of this advanced technique to structural engineering areas.
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Sparse, collaborative, or nonnegative representation: Which helps pattern classification?
TL;DR: In this paper, the authors investigated the use of nonnegative representation (NR) for pattern classification, which is largely ignored by previous work, and showed that NR can boost the representation power of homogeneous samples while limiting the represent power of heterogeneous samples, making the representation sparse and discriminative simultaneously and thus providing a more effective solution to representation based classification than SR/CR.
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