Latent Factor Guided Convolutional Neural Networks for Age-Invariant Face Recognition


While considerable progresses have been made on face recognition, age-invariant face recognition (AIFR) still remains a major challenge in real world applications of face recognition systems. The major difficulty of AIFR arises from the fact that the facial appearance is subject to significant intra-personal changes caused by the aging process over 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 first attempt 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-of the-art. We also verify the excellent generalization of our new model on the famous LFW dataset.

DOI: 10.1109/CVPR.2016.529

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@article{Wen2016LatentFG, title={Latent Factor Guided Convolutional Neural Networks for Age-Invariant Face Recognition}, author={Yandong Wen and Zhifeng Li and Yu Qiao}, journal={2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)}, year={2016}, pages={4893-4901} }