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Centre for Pattern Recognition and Data Analytics
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H. Qiu, D. S. Pham, S. Venkatesh, J. Lai, and W. Liu. Innovative sparse representation algorithms for robust face recognition. International Journal of Innovative Computing, Information, and Control, 7(10), 2011. (to appear).
In this paper, we propose two innovative and computationally efficient algorithms for robust face recognition, which extend the previous Sparse Representation-based Classiﬁcation (SRC) algorithm proposed by Wright et al. (2009). The two new algorithms, which are designed for both batch and online modes, operate on matrix representation of images, as opposed to vector representation in SRC, to achieve efficiency whilst maintaining the recognition performance. We ﬁrst show that, by introducing a matrix representation of images, the size of the l1-norm problem in SRC is reduced from O(whN) to O(rN), where r <<wh and thus higher computational eﬃciency can be obtained. We then show that the computational eﬃciency can be even enhanced with an online setting where the training images arrive incremental ly by exploiting the interlacing property of eigenvalues in the inner product matrix. Final ly, we demonstrate the superior computational eﬃciency and robust performance of the proposed algorithms in both batch and online modes, as compared with the original SRC algorithm through numerous experimental studies.
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