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Parallel Multi-Path Age Distinguish Network for Cross-Age Face Recognition

Cross Age Face Recognition (CAFR) is a challenging task in the field of face recognition. There still exist some limitations in mainstream CAFR methods. On one hand, some methods need synthesizing multiple groups of features and fusing the results under different ages, where the similarity score in some age groups may affect final classification results. On the other hand, many other methods takes a facial image as a linear combination of identity information and age information, which treats age factor as a value independent of identity information, but may be inconsistent with the aging pattern of many individuals. And these methods require both age labels and identity labels in training, which is limited by the scale of existing CAFR datasets. To address the above limitations, this work proposes the Parallel Multi-path Age Distinguish Network (PMADN) model. Specifically, our model consists of two cascading networks, an Age Distinguish Mapping Network (ADMN) and a Cross-Age Feature Recombination Network (CFRN). Firstly, the face features are mapped into different age groups by parallel multi-path full connected layers in ADMN, which can better extract the identity features in a small age span. Secondly, CFRN nonlinearly recombines the mapped features to extract the age robust features that are beneficial to identity classification, which can avoid the simple linear combination of identity factor and age factor in the existing methods. What’s more, our algorithm is combined with transfer learning and only uses the age label and a pre-trained ordinary face recognition network for training, which can make use of a larger aging face dataset for training. Extensive CAFR experiments performed on the benchmark MORPH Album2, CACD-VS and Cross Age LFW databases demonstrate the effectiveness and superiority of our method.