Illumination and Expression Invariant Recognition Using SSIM Based Sparse Representation

The sparse representation technique has provided a new way of looking at object recognition. As we demonstrate in this paper, however, the mean-squared error (MSE) measure, which is at the heart of this technique, is not a very robust measure when it comes to comparing facial images, which differ significantly in luminance values, as it only performs pixel-by-pixel comparisons. This requires a significantly large training set with enough variations in it to offset the drawback of the MSE measure. A large training set, however, is often not available. We propose the replacement of the MSE measure by the structural similarity (SSIM) measure in the sparse representation algorithm, which performs a more robust comparison using only one training sample per subject. In addition, since the off-the-shelf sparsifiers are also written using the MSE measure, we developed our own sparsifier using genetic algorithms that use the SSIM measure. We applied the modified algorithm to the Extended Yale Face B database as well as to the Multi-PIE database with expression and illumination variations. The improved performance demonstrates the effectiveness of the proposed modifications.