Robust Face Recognition via Sparse Representation

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 l{1}-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.

Sparse Representations and Invariant Sequence-Feature Extraction for Event Detection

“…The feature extraction is targeted to certain invariances and tailored to the sparse classifier. We have shown that sparse classification as introduced by Wright et al (2009) is well suited for human event detection. Sparse classification offers a set of advantages over other methods for the problem of action recognition and event detection, being robust, adaptive and easy to tune.…”

“…This representation is sparse because it should contain only vectors from the class to which the test vector belongs (Wright et al, 2009). …”

“…such that y is assigned to the class whose training-set vectors best reproduce it (Wright et al, 2009). …”

“…Sparsity as a data analysis and representation paradigm is currently widely investigated for various purposes like compressed sensing (Candès and Tao, 2006;Donoho, 2006), but also feature extraction (d 'Aspremont et al, 2007), and data compression. Sparse representations have already been used for recognition tasks by Wright et al (2009) and even for the recognition of human actions using data from a wearable motion-sensor network by Yang et al (2008) or using video data (Guo et al, 2010). However, to the best of our knowledge, no link to event detection was discussed before.…”

Nonlinear Classification of Multispectral Imagery Using Representation-Based Classifiers

“…Recently, sparse representation classifier (SRC) [3] and collaborative representation classifier (CRC) [4] have gained much attention for hyperspectral imagery classification. Different from the traditional classifiers, such as support vector machine (SVM), these representation-based classifiers do not use the training-testing fashion.…”