A dissimilarity kernel with local features for robust facial recognition

Local binary pattern (LBP) has recently been proposed for texture analysis and local feature description and has also been applied to face recognition with promising results. However, besides the descriptors, a suitable similarity measure that can efficiently learn to distinguish facial features is also important. In this paper, a novel framework for robust face recognition is presented that considers both local and global features by using multi-resolution LBP descriptors. The framework can tolerate variations in expression, lighting condition and occlusion. A weighted distance measure is used to learn the dissimilarity between sets of LBP features. We formulate the distance function as a conditionally positive semi-definite (CPD) kernel, thus making it suitable for kernel-based algorithms such as support vector machines (SVMs) whose optimal solutions are guaranteed. We show that by defining it in a Hilbert space, the proposed CPD kernel has advantages over traditional methods computing the l
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distances in the Euclidean space. The experiments show that the approach is efficient and significantly outperforms the current state-of-the-art methods on the publicly available AR face database.