Multimodal face aging framework via learning disentangled representation |
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| Affiliation: | 1. Faculty of Information, Beijing University of Technology, Beijing 100124, China;2. Beijing Key Laboratory of Computational Intelligence and Intelligent System, Beijing University of Technology, Beijing 100124, China;3. School of Computer Science and Information Engineering, Hefei University of Technology, Hefei 230009, China;1. School of Information Science and Engineering, Lanzhou University, Lanzhou, China;2. School of Computer Science, University of Guelph, Guelph, Canada;3. School of Big Data & Software Engineering, Chongqing University, Chongqing, China;4. Gansu Highway and Bridge Construction Group Co., Ltd., Lanzhou, China;5. Gansu ZhiTong Technology Engineering Detection Consulting Co., Ltd., Lanzhou, China |
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| Abstract: | Existing face aging (FA) approaches usually concentrate on a universal aging pattern, and produce restricted aging faces from one-to-one mapping. However, the diversity of living environments impact individuals differently in their oldness. To simulate various aging effects, we propose a multimodal FA framework based on face disentanglement technique of age-specific and age-irrelevant information. A Variational Autoencoder (VAE)-based encoder is designed to represent the distribution of the age-specific attributes. To capture the age-irrelevant features, a cycle-consistency loss of unpaired faces is utilized among various age spans. The extensive experimental results demonstrate that the sampled age-specific codes along with an age-irrelevant feature make the multimodal FA diverse and realistic. |
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| Keywords: | Face aging Disentangled representation Variational auto-encoder KL divergence Generative adversarial network |
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