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

Yandong Wen, Zhifeng Li, Yu Qiao; The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016, pp. 4893-4901

Abstract


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 age-invariant 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.

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[bibtex]
@InProceedings{Wen_2016_CVPR,
author = {Wen, Yandong and Li, Zhifeng and Qiao, Yu},
title = {Latent Factor Guided Convolutional Neural Networks for Age-Invariant Face Recognition},
booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2016}
}