A Joint Multi-Task CNN for Cross-Age Face Recognition

Cross-age face recognition (CAFR) has received more and more attention in real applications, but it is a challenging task due to complex facial aging process. One popular way is modeling CAFR as a traditional face classification problem. However, most of them suffer from one main difficulty: how to effectively extract identity sensitive features that are age insensitive. In this paper, we propose a joint multi-task convolutional neural network (JMCNN) framework. JMCNN consists of two tasks: one for face recognition to learn identity sensitive features (i.e., age-invariant features), the other for age classification to learn age sensitive features, meanwhile, two tasks enhance each other by enforcing a regularization term on two kinds of features. The experimental results on two well-known cross-age datasets (Morph Album 2, CACD) have shown JMCNN is superior to the existing methods.