Face recognition under partial occlusion and small dense noise

Abstract

Problem of automatic recognition of human faces from front views with varying expression, illumination, occlusion as well as disguise is considered. Here the problem of recognition is cast as one of the several classifying linear regression models and argued that in handling such problems a new theory using sparse representation of signals is the key. A face recognition algorithm is also introduced which uses ‘L1-minimization’ theory of optimization. This proposed concept handles two crucial problems of face recognition, which are, feature extraction and robust occlusion handling. For extraction of features, PCA is used, but later in the thesis it is shown that if sparsity is properly calculated in the face representation, selection of features doesn’t remain crucial. However, the number of extracting features is crucial here. Another crucial factor is the authenticity of calculating sparse coefficients. Unconventional feature extraction techniques such as down-sampled images and random projections give results comparable to common features like Eigenfaces, as long as the dimension of the feature space exceeds a particular threshold, predicted by the sparse representation theory. This can handle errors because of occlusion and consistently by using the fact that these errors are frequently sparse with respect to the standard basis. The sparse representation theory helps in predicting that how much of occlusion can be handled using this recognition algorithm and how can the training images be selected so that robustness to occlusion can be maximized. A Number of experiments on freely accessible facial databases are performed to justify the efficiency of the proposed algorithm and the above claims.