Part-Based Convolutional Network for Imbalanced Age Estimation

Abstract

Age estimation based on unconstrained face images remains a challenging problem in computer vision and pattern recognition. We address this by ensembling part-based features and designing a cost sensitive loss to overcome the general imbalance data in age estimation. Specifically, we treat age estimation as a multi-class classification problem and mainly make two contributions: (i) We present a Part-based Convolutional Network(PCN) for age estimation to extract regional features instead of the holistic ones, which can preserve more discriminative features in facial regions like forehead, canthus, cheek, jaw, etc; (ii) A balanced loss for multi-class classification is designed to handle the extremely imbalance age distribution. The loss pays more attention to the hard examples, and can automatically adjust the weights of samples according to their contribution. State-of-the-art performance was achieved on FG-NET, MORPH and CACD databases, validating the effectiveness of the proposed approach.