Deep Difference Analysis in Similar-looking Face recognition

Yaoyao Zhong, Weihong Deng

Abstract: Deep convolutional neural networks (DCNNs) have recently demonstrated impressive performance in face recognition. However, there is no clear understanding of what difference they find between two similar-looking faces. In this paper, we propose a visualization method that gives insight into difference of similar-looking faces found by DCNNs. This method, used as an assistant role, could help human to identify people who try to invade the biometric system using a similar-looking face. We design a crowdsourcing task to evaluate our method. With assistance of our method, accuracy of participants is greatly increased by 8%, which is also better than the accuracy of network, while participants get little improvement with assistance of Deconvolutional network or Gradient Back-propagation. The experiment result suggests that our method makes a difference in human-machine cooperation.