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- W2536692889 abstract "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." @default.
- W2536692889 created "2016-10-28" @default.
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- W2536692889 date "2017-01-01" @default.
- W2536692889 modified "2023-09-27" @default.
- W2536692889 title "Age invariant face recognition and retrieval by coupled auto-encoder networks" @default.
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- W2536692889 doi "https://doi.org/10.1016/j.neucom.2016.10.010" @default.
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