Abstract
In a sparse-representation-based face recognition scheme, dictionary learning has attracted growing attention for its good performance. Discriminative K-SVD (D-KSVD) is one of conventional dictionary learning algorithm, which can effectively solve the face recognition problem. However, D-KSVD doesn’t consider the discrimination of the sparse coding coefficients. To address this issue, a new algorithm named Fisher Discriminative K-SVD (FD-KSVD) is proposed. In the new algorithm, the Fisher discrimination criterion is imposed on the sparse coding coefficients to make them discriminative through small within-class scatter and big between-class scatter. The optimization is employed by the Iterative Projective Method and K-SVD method alternatively. The experimental results of face databases indicated recognition performance of the new algorithm is superior to other state-of-the-art algorithms.
Published Version
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