Conference Paper

Robust sparse coding for face recognition

Hong Kong Polytech. Univ., Hong Kong, China
DOI: 10.1109/CVPR.2011.5995393 Conference: Computer Vision and Pattern Recognition (CVPR), 2011 IEEE Conference on
Source: IEEE Xplore

ABSTRACT Recently the sparse representation (or coding) based classification (SRC) has been successfully used in face recognition. In SRC, the testing image is represented as a sparse linear combination of the training samples, and the representation fidelity is measured by the l2-norm or l1-norm of coding residual. Such a sparse coding model actually assumes that the coding residual follows Gaussian or Laplacian distribution, which may not be accurate enough to describe the coding errors in practice. In this paper, we propose a new scheme, namely the robust sparse coding (RSC), by modeling the sparse coding as a sparsity-constrained robust regression problem. The RSC seeks for the MLE (maximum likelihood estimation) solution of the sparse coding problem, and it is much more robust to outliers (e.g., occlusions, corruptions, etc.) than SRC. An efficient iteratively reweighted sparse coding algorithm is proposed to solve the RSC model. Extensive experiments on representative face databases demonstrate that the RSC scheme is much more effective than state-of-the-art methods in dealing with face occlusion, corruption, lighting and expression changes, etc.

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    • ", [14], we use the Logistic weight function to determine w for fair comparison with RSC in [15], whose loss function φ(·) and weight function ω(·) (as shown in Fig. 1) are respectively defined as "
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    • "With its inspirational roots in human vision system [16], [17], this technique has been successfully employed in image restoration [18], [19], [20], compressive sensing [21], [22] and morphological component analysis [23]. More recently, sparse representation based approaches have also shown promising results in face recognition and gender classification [9], [8], [10], [13], [24], [25], [26], texture and handwritten digit classification [14], [29], [30], [31], natural image and object classification [9], [11], [32] and human action recognition [33], [34], [35], [36]. The success of these approaches comes from the fact that a sample from a class can generally be well represented as a sparse linear combination of the other samples from the same class, in a lower dimensional manifold [8]. "
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    ABSTRACT: We propose a Bayesian approach to learn discriminative dictionaries for sparse representation of data. The proposed approach infers probability distributions over the atoms of a discriminative dictionary using a Beta Process. It also computes sets of Bernoulli distributions that associate class labels to the learned dictionary atoms. This association signifies the selection probabilities of the dictionary atoms in the expansion of class-specific data. Furthermore, the non-parametric character of the proposed approach allows it to infer the correct size of the dictionary. We exploit the aforementioned Bernoulli distributions in separately learning a linear classifier. The classifier uses the same hierarchical Bayesian model as the dictionary, which we present along the analytical inference solution for Gibbs sampling. For classification, a test instance is first sparsely encoded over the learned dictionary and the codes are fed to the classifier. We performed experiments for face and action recognition; and object and scene-category classification using five public datasets and compared the results with state-of-the-art discriminative sparse representation approaches. Experiments show that the proposed Bayesian approach consistently outperforms the existing approaches.
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    • "Recently, sparse representation based face recognition methods have achieved great success [54] [55]. Motivated by these works, we observed that if a test video V comes from class c, it has similar underlying features with the videos from the same class. "
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    ABSTRACT: Existing works on action recognition rely on two separate stages: (1) designing hand-designed features or learning features from video data; (2) classifying features using a clas-sifier such as SVM or AdaBoost. Motivated by two observations: (1) independent compo-nent analysis (ICA) is capable of encoding intrinsic features underlying video data; and (2) videos of different actions can be easily distinguished by their intrinsic features, we propose a simple but effective action recognition framework based on the recently pro-posed overcomplete ICA model. After a set of overcomplete ICA basis functions are learned from the densely sampled 3D patches from training videos for each action, a test video is classified as the class whose basis functions can reconstruct the sampled 3D patches from the test video with the smallest reconstruction error. The experimental results on five benchmark datasets demonstrate that the proposed approach outperforms several state-of-the-art works.
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