FACIAL AGE ESTIMATION FROM IMAGE USING IMAGE PROCESSING TECHNIQUES AND NEURAL NETWORK

In this work, we have proposed a new effective method for synthetic aging of human faces based on Age Conditional Generative Adversarial Network (Age-cGAN). The method is composed of two steps: (1) input face reconstruction requiring the solution of an optimization problem in order to find an optimal latent approximation z∗, (2) and face aging it-self performed by a simple change of condition y at the input of the generator. The cornerstone of our method is the novel “Identity Preserving” latent vector optimization approach al-lowing to preserve the original person’s identity in the reconstruction. This approach is universal meaning that it can be used to preserve identity not only for face aging but also for other face alterations (e.g. adding a beard, sunglasses etc.)

[1] Yun Fu, Guodong Guo, and Thomas S Huang, “Age synthesis and estimation via faces: A survey,” IEEE Transactions on Pattern Analysis and Machine Intelli-gence, vol. 32, no. 11, pp. 1955–1976, 2010. [2] Xiangbo Shu, Jinhui Tang, Hanjiang Lai, Luoqi Liu, and Shuicheng Yan, “Personalized age progression with ag-ing dictionary,” in Proceedings of International Confer-ence on Computer Vision, Santiago, Chile, 2015. [3] Unsang Park, Yiying Tong, and Anil K Jain, “Age-invariant face recognition,” IEEE Transactions on Pat-tern Analysis and Machine Intelligence, vol. 32, no. 5, pp. 947–954, 2010. [4] Wei Wang, Zhen Cui, Yan Yan, Jiashi Feng, Shuicheng Yan, Xiangbo Shu, and Nicu Sebe, “Recurrent face ag-ing,” in Proceedings of Computer Vision and Pattern Recognition, Las Vegas, USA, 2016. [5] Bernard Tiddeman, Michael Burt, and David Perrett, “Prototyping and transforming facial textures for per-ception research,” IEEE Computer Graphics and Ap-plications, vol. 21, no. 5, pp. 42–50, 2001. [6] Ira Kemelmacher-Shlizerman, Supasorn Suwajanakorn, and Steven M Seitz, “Illumination-aware age progres-sion,” in Proceedings of Computer Vision and Pattern Recognition, Columbus, USA, 2014. [7] Jinli Suo, Song-Chun Zhu, Shiguang Shan, and Xilin Chen, “A compositional and dynamic model for face aging,” IEEE Transactions on Pattern Analysis and Ma-chine Intelligence, vol. 32, no. 3, pp. 385–401, 2010. [8] Yusuke Tazoe, Hiroaki Gohara, Akinobu Maejima, and Shigeo Morishima, “Facial aging simulator consider-ing geometry and patch-tiled texture,” in Proceedings of ACM SIGGRAPH, Los Angeles, USA, 2012. [9] Ian Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio, “Generative adversarial nets,” in Proceedings of advances in Neural Information Processing Systems, Montreal, Canada, 2015. [10] Diederik P Kingma and Max Welling, “Auto-encoding variational bayes,” in Proceedings of International Con-ference on Learning Representations, Banff, Canada, 2014.