Age-Related Factor Guided Joint Task Modeling Convolutional Neural Network for Cross-Age Face Recognition

Cross-age face recognition has remained a popular research topic as most regular facial recognition systems have failed in dealing with facial changes through age. In order to enhance the system's capability of discriminating facial identity features in spite of age changes, this paper proposes a novel deep convolutional network method for cross-age face recognition called age-related factor guided joint task modeling convolutional neural networks, which combines an identity discrimination network with an age discrimination network that shares the same feature layers. By alternatively training the fusion networks and the combined factor model, the cross-age identity features and cross-identity age features can be effectively separated with high inter-class distension and intra-class compactness. Extensive experiments have been performed on the benchmark aging data sets, including MORPH, CACD-VS, and Cross Age LFW. The results have demonstrated the superiority and effectiveness of our model.