Facial appearances such as shape and texture are subject to significant intra-class variations caused by the aging process over time, resulting in the performance reduction of face recognition. To overcome this problem, this paper proposes a novel method (age decomposition convolution neural network, AD-CNN) based on deep convolution neural network to learn age-invariant face features. Firstly, the AD-CNN utilizes convolutional block attention module (CBAM) to extract facial features and estimates age factors by linear regression. Then, the facial features and age factors are projected into the same linear separable space by multi-layer perceptron. Finally, the age-invariant face features can be obtained by separating age factors from the whole facial features. Here, the improved angle loss function is considered to guide the training process. The proposed AD-CNN achieves 98.93%, and 90.0% recognition accuracy on MORPH and FGNET datasets, respectively, which demonstrates the AD-CNN with a great potential for age-invariant face recognition. Copyright ©2022 Acta Automatica Sinica. All rights reserved.
