Face Verification Across Aging Based on Deep Convolutional Networks and Local Binary Patterns

Abstract

This paper proposes a novel method to learn a set of high-level feature representations for face verification across aging. Conventional hand-crafted features are not capable to overcome aging effects. In order to obtain an accurate face representation, we apply the combination of a nine-layer deep convolutional neural network and Local Binary Pattern(LBP) histograms, both of which are essential to face recognition. On account of the need of large quantity data in deep learning methods, we train the model on the publicly available cross-age face dataset CACD (Cross-Age Celebrity Dataset), which contains more than 160000 face images of 2000 different celebrities. Experiments on the CACD and LFW (Labeled Faces in the Wild) dataset demonstrate that the proposed approach outperforms the state-of-the-art methods. In addition, hairstyle, facial expression, changes of background and occlusion provide discriminative cues to the system of face verification.