Face Recognition using VLAD and its Variants

Abstract

Face Recognition (FR) has grown to be one of the most productive fields in the domain of Computer Vision (CV) due to the wide range of applications it has bestowed in commercial and law enforcement settings and remains one of the most formidable problems in CV. One of the central issues with FR is that its recognition performance tends to suffer when the size of the database to search through is large, due to the cost of needing to generate and compare descriptors for each individual face. Hence in this paper, we address this issue by employing VLAD (Vector of Locally Aggregated Descriptors) to aggregate local face descriptors (which are pooled using Bag-of-Words (BOW) algorithm) and classified using RBF-SVM (Radial Basis Function-Support Vector Machine) to output a predicted label file, which can be used to directly access the descriptors, instead of computing them from individual face images for each comparison. Although variants of VLAD exist, they have been proposed primarily for object recognition and hence we proffer three novel VLAD variants for FR by utilizing the popular feature descriptors: SIFT (Scale Invariant Feature Transform), SURF (Speeded Up Robust Features) and ORB (Oriented Fast Rotated Brief). We will demonstrate using comprehensive experimentations on the ORL database that the proposed variants can considerably improve the performance of FR over the contemporary state-of-the-art algorithms.