Conditional generative adversarial networks based on the principle of homologycontinuity for face aging

Abstract

Age is one of the most important biological characteristics of the human face. The increase of age coincides with the increase of the aging degree of the face. Face aging synthesis is attracting increasingly more attention from domestic and overseas scholars in the computer vision and computer graphics fields, and it can be integrated into the basic research of face correlation, such as cross‐age face analysis and age estimation. At present, some achievements have been made in face aging synthesis research; however, it is still an urgent problem to reduce the number of parameters and computational complexity of the network while ensuring the aging effect. Therefore, a new face aging algorithm is proposed in this article. Unlike the previous methods of aging process simulation, we introduce an assisted age classification network based on the principle of homology continuity, which is more in line with the human cognition process. After pretraining, the result of age classification is improved, and the pretraining model is then added to the framework of aging face generation for fine‐tuning to constrain the generated aging face, which can improve the aging accuracy of the generated image. Furthermore, we reconstruct the input face by using the age tag of the input face and the synthesized aging face and maintain the identity invariance in the face aging process by minimizing the reconstruction loss. The experimental results show that the method proposed in this article produces a considerable effect of face aging and significantly reduces the number of parameters and the complexity of computational.

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