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.

中文翻译：

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基于同调连续性原理的条件生成对抗网络用于人脸老化

年龄是人类面部最重要的生物学特征之一。年龄的增加与面部老化程度的增加不谋而合。人脸老化合成越来越受到国内外计算机视觉和计算机图形学领域学者的关注，可以融入跨年龄人脸分析、年龄估计等人脸相关性的基础研究。目前，人脸衰老合成研究已取得一定成果；然而，在保证老化效果的同时，减少网络的参数数量和计算复杂度仍然是一个亟待解决的问题。因此，本文提出了一种新的人脸老化算法。与以往的老化过程模拟方法不同，我们引入了基于同源连续性原理的辅助年龄分类网络，更符合人类的认知过程。预训练后对年龄分类结果进行改进，然后将预训练模型加入到老化人脸生成框架中进行微调，对生成的老化人脸进行约束，可以提高生成图像的老化准确率。此外，我们通过使用输入人脸的年龄标签和合成的老化人脸来重建输入人脸，并通过最小化重建损失来保持人脸老化过程中的身份不变性。实验结果表明，本文提出的方法产生了相当大的人脸老化效果，显着减少了参数数量和计算复杂度。