Deep Learning Face Recognition Based on Improved Additive Cosine Margin Softmax Loss Function [基于改进型加性余弦间隔损失函数的深度学习人脸识别]

作者：Zhang D.^*; Chen S.; Li J.; Zhou Z.; Sun S.

来源：Chinese Journal of Sensors and Actuators, 2019, 32(12): 1830-1835.

DOI：10.3969/j.issn.1004-1699.2019.12.011

• Face recognition
• Loss function
• Network structure
• Surveillance

In order to realize face recognition in surveillance scenes,two faces of 500 people in the surveillance video were collected to form the Surveillance Video Faces(SVF)test set,including 500 positive sample pairs and 499 000 negative sample pairs. An improved additive cosine margin softmax loss function is proposed to improve the additive margin softmax loss function. By subtracting a value from the cosine value of the angle between the feature and the target weight,and adding a value to the cosine value of the angle between the feature and the non-target weight,which can reduce the intra-class distance and enlarge the inter-class distance. The value is between 0 and 1,and the best value is selected by experiment. The experimental results show that compared with the face recognition model of the softmax loss function,the angular softmax loss function and the additive margin softmax loss function training,the method has the highest accuracy of face recognition in the monitoring scene test set,which is 99.1%.

• 单位
  中国计量大学; 三峡大学