关键词: 人脸识别, 卷积神经网络, 深度学习, 多任务学习

Abstract: With the development of deep learning, face recognition algorithm has made tremendous breakthroughs. However, among current face recognition frameworks, each task （face identification, face verification or attribute classification） is independently designed and manipulated, which makes the algorithm inefficient and time-consuming. According to the problem, this paper proposes a multi-task convolution deep network. By combining face identification, verification and attribute classification losses as this loss function, the deep convolution network can be trained from end to end and the algorithm will be simple and efficient. This network can complete these three tasks without additional steps. Experiments show that the model can still achieve good performance with limited training data and get 97.3% accuracy in the authoritative face recognition dataset LFW（Labeled Face in the Wild）.

Key words: face recognition, convolution neural network, deep learning, multitask learning