### Multitask learning and CNN for application of face recognition

SHAO Weiyuan1，2, GUO Yuefei1

1. 1.School of Computer Science and Technology, Fudan University, Shanghai 201203, China
2.Shanghai Key Laboratory of Intelligent Information Processing, Fudan University, Shanghai 201203, China
• Online:2016-07-01 Published:2016-07-15

### 多任务学习及卷积神经网络在人脸识别中的应用

1. 1.复旦大学 计算机科学技术学院，上海 201203
2.上海市智能信息处理重点实验室（复旦大学），上海 201203

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）.