Patch strategy for deep face recognition

Abstract

Convolutional neural network (CNN) has proven to be a highly efficient approach to face recognition. In this study, the authors introduce a new layer to embed the patch strategy in convolutional architectures to improve the effectiveness of face representation. Meanwhile, a multi-branch CNN is constructed to learn features of each cropped patch by the patch strategy and then fuses all the patch features together to form the entire face representation. Compared with the traditional patch methods, their approach has the advantage that no extra space is needed to store the facial patches since the images are cropped online. Moreover, due to the end-to-end training, this approach makes a better use of the interactions between global and local features in the model. Two baseline CNNs (i.e. AlexNet and ResNet) are used to analyse the effectiveness of their method. Experiments show that the proposed system achieves comparable performance with other state-of-the-art methods on the labelled faces in the wild and YouTube face verification tasks. To ensure the reproducibility, the publicly available training set CASIA-WebFace is used.