Multi scale multi descriptor local binary features and exponential discriminant analysis for robust face authentication

In this paper we present an efficient face verification system based on the fusion of multi-scale multi-descriptor local binary features. First, the face is divided into regions and each region is divided into several patches. For each patch and at every specific scale, the statistics of the baseline Local Binary Pattern (LBP), the Local Phase Quantization (LPQ) and the recently proposed Binarized Statistical Image Feature (BSIF) are summarized by histograms. The histograms of different patches belonging to the same region are concatenated to form a highly dimensional feature vector representing a specific descriptor at a specific scale. Second, we propose an efficient dimensionality reduction technique based on Exponential Linear Discriminant Analysis EDA coupled with Within-Class Covariance Normalization (WCCN) to downgrade the effect of the directions of high intravariability and to enhance the discrimination power of the EDA. The projected histograms for each region are scored using the cosine similarity metric. Lastly, the different region scores corresponding to different descriptors at different scales are fused using support vector machine classifier (SVM). Experimental verification results demonstrate that the proposed authentication pipeline outperforms all the existing systems on the XM2VTS controlled database and interestingly compete with the top performing systems on the challenging LFW database.