Mean-Variance Loss for Deep Age Estimation from a Face

作者：Hongyu Pan, Hu Han, Shiguang Shan, Xilin Chen · 年份：2018 · DOI：10.1109/cvpr.2018.00554 · 被引用次数：242 · 研究领域：Face recognition and analysis、Generative Adversarial Networks and Image Synthesis、Video Surveillance and Tracking Methods

Age estimation has wide applications in video surveillance, social networking, and human-computer interaction. Many of the published approaches simply treat age estimation as an exact age regression problem, and thus do not leverage a distribution's robustness in representing labels with ambiguity such as ages. In this paper, we propose a new loss function, called mean-variance loss, for robust age estimation via distribution learning. Specifically, the mean-variance loss consists of a mean loss, which penalizes difference between the mean of the estimated age distribution and the ground-truth age, and a variance loss, which penalizes the variance of the estimated age distribution to ensure a concentrated distribution. The proposed mean-variance loss and softmax loss are jointly embedded into Convolutional Neural Networks (CNNs) for age estimation. Experimental results on the FG-NET, MORPH Album II, CLAP2016, and AADB databases show that the proposed approach outperforms the state-of-the-art age estimation methods by a large margin, and generalizes well to image aesthetics assessment.