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Face image synthesis with weight and age progression using conditional adversarial autoencoder

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Abstract

The appearance of a human face changes with the change in body weight and age. With varying lifestyle choices, it is hard to imagine the appearance of a given human face in years to come. Future self-perception is highly associated with one’s emotional state, as well as health behavior. Negative future self-perception can cause negative lifestyle choice and negative health behavior, leading to depression and eating disorder. In this paper, a new methodology is introduced for future self-face image synthesis using age and weight, resulting in visualization of future face image derived from given weight category and age. A Constrained Local Model is first used for weight progressed future face image synthesized and then age-progressed future face image is generated using Conditional Adversarial Auto Encoder. In the final step, both weight progressed and age-progressed face images fed to face morphing module which synthesized future face image by keeping natural looks. Experimental results show the advantages of proposed method with promising results.

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Acknowledgements

This work is supported by the Postgraduate Research Grant (PPP) - Research and Grand Challenge - HTM (Wellness) from the University of Malaya under Grants PG210-2016A and GC003A-14HTM.

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Authors and Affiliations

  1. Faculty Of Computer Science And Information Technology, University of Malaya, Kuala Lumpur, Malaysia

    Muhammad Anwaar & Chu Kiong Loo

  2. Faculty of Engineering, Computing and Science, Swinburne University of Technology (Sarawak Campus), Kuching, Malaysia

    Manjeevan Seera

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  1. Muhammad Anwaar
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  2. Chu Kiong Loo
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  3. Manjeevan Seera
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Correspondence to Chu Kiong Loo.

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Anwaar, M., Loo, C.K. & Seera, M. Face image synthesis with weight and age progression using conditional adversarial autoencoder. Neural Comput & Applic 32, 3567–3579 (2020). https://doi.org/10.1007/s00521-019-04217-6

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