Face Verification in the Wild using Similarity in Representations

In recent years, classification using sparse representation of signals has attracted much attention and has achieved satisfactory results compared to the conventional methods. In this paper, a classification method using sparse representation is proposed for face verification in Labeled Faces in the Wild (LFW) data. The LFW dataset involves high intra-class variations due to the uncontrolled imaging conditions. According to our experimental results, matched and mismatched pairs of the LFW data can be better classified using separate dictionaries for each image of the input pair.