Side-Information based Exponential Discriminant Analysis for face verification in the wild

Recently, there is an extensive research efforts devoted to the challenging problem of face verification in unconstrained settings and weakly labeled data, where the task is to determine whether pairs of images are from the same person or not. In this paper, we propose a novel discriminative dimensionality reduction technique called Side-Information Exponential Discriminant Analysis (SIEDA) which inherits the advantages of both Side-Information Linear Discriminant (SILD) and Exponential Discriminant Analysis (EDA). SIEDA transforms the problem of face verification under weakly labeled data into a generalized eigenvalue problem while alleviating the preprocessing step of PCA dimensionality reduction. To further boost the performance, the multi-scale variant of the binarized statistical image features histograms are adopted for efficient and rich facial texture representation. Extensive experimental evaluation on the challenging Labeled Faces in the Wild LFW benchmark database demonstrates the superiority of SIEDA over SILD. Moreover, the obtained verification accuracy is impressive and compares favorably against the state-of-the-art.