Review of Cross-Age Face Recognition in Discriminative Models

The purpose of cross-age face recognition is to identify people with a large age difference. It is of great significance in security, finance, fighting criminals and other fields. In order to organize the development process of Convolutional Neural Networks (CNN) in the field of cross-age face recognition, the main discriminant methods in the field in recent years are summarized. First of all, the evolution and performance of cross-age face recognition based on CNN are systematically described, and summarized. Secondly, the advantages and disadvantages of the most commonly used cross-age face recognition data sets are introduced. Finally, combined with practical application, the existing challenges and bottlenecks of discriminant models are discussed, and the future research direction of cross-age face recognition is prospected.