Face recognition by landmark pooling-based CNN with concentrate loss
Rui Huang, Xiaohua Xie, Zhanxiang Feng, Jianhuang Lai · 2017
Face recognition has been a hot research topic in recent years, convolutional neural network (CNN) based methods have achieved state of the art results and significantly improve the performance. Along with the CNN framework, we propose a novel loss function called concentrate loss which focuses on the class centers in the mini-batch. The concentrate loss aims to push the samples towards corresponding class centers and simultaneously enlarge the gap between different class centers. Additionally, we ultilize facial landmark pooling technique to take full advantage of facial structure information. Experiment results on Labeled Faces in the Wild (LFW), YouTube Faces (YTF), and the BluFR benchmark demonstrate the efficiency of our proposal.