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2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2016)
Las Vegas, NV, United States
June 27, 2016 to June 30, 2016
ISSN: 1063-6919
ISBN: 978-1-4673-8851-1
pp: 4893-4901
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.
Face recognition, Convolution, Face, Aging, Feature extraction, Robustness, Training,
Yandong Wen, Zhifeng Li, Yu Qiao, "Latent Factor Guided Convolutional Neural Networks for Age-Invariant Face Recognition", 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), vol. 00, no. , pp. 4893-4901, 2016, doi:10.1109/CVPR.2016.529
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