Revised Contrastive Loss for Robust Age Estimation from Face
Hongyu Pan, Hu Han, Shiguang Shan, Xilin Chen · 2018
Age estimation has broad applications in many fields, such as video surveillance, social networking, and human-computer interaction. Many of the existing approaches treat age estimation as a classification problem; however, the individual age values are not independent classes; they have an ordinal relationship. Classification loss such as softmax is not able to model such kind of relationship. In this paper, we propose a new loss, called revised contrastive loss, to model the ordinal relationship of individual ages. Specifically, the revised contrastive loss is proposed to penalize the distance between two face images in the feature space according to their age difference, which makes the learned features more discriminative for the age estimation task. We embed the proposed revised contrastive loss and softmax loss into a Convolutional Neural Network (CNN), and optimize the networks via Stochastic Gradient Descent (SGD) in an end-to-end fashion. Experimental results on a number of challenging face aging databases (FG-NET, MORPH Album II, and CLAP2016) show that the proposed approach outperforms the state-of-the-art methods by a large margin using a single model.