Age invariant face recognition and retrieval by coupled auto-encoder networks

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• 作者：Chenfei XuAuthor Vitae ; Qihe LiuAuthor Vitae ; Mao Ye ; ^{cvlab.uestc@gmail.com}Author Vitae
• 关键词：Face recognition ; Age invariant ; Auto-encoder
• 刊名：Neurocomputing
• 出版年：2017
• 出版时间：26 January 2017
• 年：2017
• 卷：222
• 期：Complete
• 页码：62-71
• 全文大小：2257 K
• 卷排序：222

文摘

Recently many promising results have been shown on face recognition related problems. However, age-invariant face recognition and retrieval remains a challenge. Inspired by the observation that age variation is a nonlinear but smooth transform and the ability of auto-encoder network to learn latent representations from inputs, in this paper, we propose a new neural network model called coupled auto-encoder networks (CAN) to handle age-invariant face recognition and retrieval problem. CAN is a couple of two auto-encoders which bridged by two shallow neural networks used to fit complex nonlinear aging and de-aging process. We further propose a nonlinear factor analysis method to nonlinearly decompose one given face image into three components which are identity feature, age feature and noise, where identity feature is age-invariant and can be used for face recognition and retrieval. Experiments on three public available face aging datasets: FGNET, CACD and CACD-VS show the effectiveness of the proposed approach.