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

作者：Xu, Chenfei; Liu, Qihe; Ye, Mao^*

来源：Neurocomputing, 2017, 222: 62-71.

DOI：10.1016/j.neucom.2016.10.010

• Face recognition
• Age invariant
• Auto-encoder

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.

• 出版日期2017-1-26
• 单位电子科技大学