Cross-age face recognition has become a hot topic in the field of face recognition because of its wide application in real life. In order to improve the accuracy of cross-age face recognition, this paper proposed an identity preserving disentangled representation learning(IPDRL) network based on generative adversarial network. The network consisted of encoder, generator and discriminator. The encoder disentangled any age change in the feature, and encoded the identity information of the image at the same time, and extracted features that were only conducive to identity identification, so that it realized the disentanglement of identity feature and age feature. The generator generated the corresponding identified preserving age image according to the input age features, and the discriminator realized the class distribution prediction of age and identity through adversarial learning and multi-task learning. The combination of disentangled representation learning, adversary learning and multi-task learning can preserve the identity information of face image well, which improves the accuracy of cross-age face recognition.

[2021-11-05] Printed Article /doc/2021.01.0073/v2