Multi-Features Fusion and Decomposition for Age-Invariant Face Recognition

Although the General Face Recognition (GFR) research achieves great success, Age-Invariant Face Recognition (AIFR) is still a challenging problem since facial appearance changing over time brings significant intra-class variations. The existing discriminative methods for the AIFR task mostly focus on decomposing the facial feature from a sigle image into age-related feature and age-independent feature for recognition, which suffer from the loss of facial identity information. To address this issue, in this work we propose a novel Multi-Features Fusion and Decomposition (MFFD) framework to learn more discriminative feature representations and alleviate the intra-class variations for AIFR. Specifically, we first sample multiple face images of different ages with the same identity as a face time series. Next, we combine feature decomposition with fusion based on the face time series to ensure that the final age-independent features effectively represent the identity information of the face and have stronger robustness against aging. Moreover, we also present two feature fusion methods and several different training strategies to explore the impact on the model. Extensive experiments on several cross-age datasets (CACD, CACD-VS) demonstrate the effectiveness of our proposed method. Besides, our method also shows comparable generalization performance on the well-known LFW dataset.