Face aging is of great significance in cross-time identity verification problem. However, there is still a huge gap between the synthesized face image and the real face in terms of quality and consistency due to identity ambiguity and image distortion caused by existing face aging methods. To meet this challenge, we propose a face aging framework named as Pixel-level Alignment GAN, PAGAN, to synthesize faces of different age groups. Face images are featured by age, identity, and fine-grained pixel-value to ensure the quality, which is a typical multi-task learning problem. The proposed face aging framework with PAGAN is a combination of age estimation, identity preservation, and image de-noising. Extensive experiments demonstrate that the proposed method outperforms state-of-the-art methods not only in the accuracy of age classification but also in the image quality. With the proposed PAGAN, the face recognition accuracy with synthesized images has increased 0.21% and the image quality rating has increased around 5%, which proves the effectiveness and validity of proposed method.
Scopus Subject Areas
- Artificial Intelligence
- Face aging
- Generative adversarial networks
- Multi-view learning