Abstract
In this paper, we study the effects of discriminative cues (gender and age) in an image based face recognition system across age. We propose a pipeline framework to prune the search space based on gender and age of the test image to aid the process of recognition. A feature based approach which uses the Gabor phase and magnitude images of the face image is used in face image representation. The Gabor phases and magnitudes are encoded through Local Gabor Binary Pattern (LGBP) histograms. The gender and age group information of the face image is extracted using a random forest classifier based on a confidence measure and are used to prune the search space for efficient recognition. The effects of these discriminative cues are studied using a simple face recognition system in which the recognition is performed by histogram intersection technique which computes a similarity score between the LGBP histograms of two face images. The experiments on the FG-NET dataset and our private dataset show that the discriminative cues can indeed improve the performance of a face recognition system in terms of accuracy, lower time requirements, and graceful degradation.