Face Verification Based on Relational Disparity Features and Partial Least Squares Models
Face verification approaches aim at determining whether two given faces are from the same person. This scenario has several applications, such as information security, forensics, surveillance and smart cards. Several works extract features independently from each face image, i.e., any sort of relation between the two faces is not modeled a priori to either training or classification stages. In this work, we propose an approach that compares a pair of faces by extracting relational features, assuming the hypothesis that modeling the relation between two faces can be useful for increasing the robustness and performance of the face verification task. Then, we employ multiple classification models based on Partial Least Squares to verify whether a given pair of images belongs the same subject (genuine) or belongs to different subjects (impostor). We validate our approach on the Labeled Faces in the Wild (LFW) and on the Public Figures (Pubfig) datasets, using only few images for training. According to the experiments, our approach achieves results up to 0.966 of area under the curve (AUC) for the LFW dataset using its unrestricted, labeled outside data protocol and an average equal error (EER) of 13.65% on PubFig dataset.