Age Factor Removal Network Based on Transfer Learning and Adversarial Learning for Cross-Age Face Recognition

It is well known that expression, pose variations, and especially age factors always affect the performance of a face recognition system in practical conditions. In this paper, we propose a novel framework called age factor removal network (AFRN) for cross-age face recognition, which combines the concepts of transfer learning and adversarial learning to enhance the performance of a pretrained face recognition network and suppress the influence of attributes, such as aging, expression, and pose variations. Similar to the generative adversarial network (GAN), our model consists of two networks: an identity feature generator G and an age discriminator D. First, the D network is trained to discriminate the age information from the feature extracted by G. Second, the G network simultaneously learns two tasks: extracting the feature through transfer learning from a pretrained face recognition network and suppressing age information through adversarial learning with D. Through the optimization of the two networks, the age factor in feature extraction process of G is removed. Besides, our network only requires the attributes' label to train and preserves the identity discriminant power through transfer learning, and thus, it does not depend on multi-label face databases. The extensive experiments have been performed on the benchmark cross-age face datasets, including MORPH Album2, CACD-VS, and Cross Age LFW, which verify the effectiveness of our model. What is more, our model can be extended to other practical face recognition tasks and decreases the influence of expressions and poses, which is verified by the experimental results on CMU Multi-PIE database.