Mean-variance loss for deep age estimation from a face

Hongyu Pan, Hu Han, Shiguang Shan, Xilin CHEN

Abstract:

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 meanvariance 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.