Abstract

While considerable progresses have been made on facerecognition, age-invariant face recognition (AIFR) still remains a major challenge in real world applications of facerecognition systems. The major difficulty of AIFR arisesfrom the fact that the facial appearance is subject to signif-icant intra-personal changes caused by the aging processover time. In order to address this problem, we propose a novel deep face recognition framework to learn the ageinvariant deep face features through a carefully designed CNN model. To the best of our knowledge, this is the firstattempt to show the effectiveness of deep CNNs in advancing the state-of-the-art of AIFR. Extensive experiments are conducted on several public domain face aging datasets (MORPH Album2, FGNET, and CACD-VS) to demonstrate the effectiveness of the proposed model over the state-ofthe-art. We also verify the excellent generalization of our new model on the famous LFW dataset.