2018/07/27 by Haoyi Wang, Wang, Haoyi, Xingjie Wei +5 · 1 citation
Computer Science · Medicine · #Face recognition and analysis #Facial Rejuvenation and Surgery Techniques #Generative Adversarial Networks and Image Synthesis
paper · pdf · doi:10.48550/arxiv.1807.10421
Convolutional Neural Networks (CNN) have been applied to age-related research as the core framework. Although faces are composed of numerous facial attributes, most works with CNNs still consider a face as a typical object and do not pay enough attention to facial regions that carry age-specific feature for this particular task. In this paper, we propose a novel CNN architecture called Fusion Network (FusionNet) to tackle the age estimation problem. Apart from the whole face image, the FusionNet successively takes several age-specific facial patches as part of the input to emphasize the age-specific features. Through experiments, we show that the FusionNet significantly outperforms other state-of-the-art models on the MORPH II benchmark.