A joint loss function for deep face recognition

作者：Wang, Shanshan; Chen, Ying^*

来源：Multidimensional Systems and Signal Processing, 2019, 30(3): 1517-1530.

DOI：10.1007/s11045-018-0614-0

• Triplet loss
• Deep learning
• Face recognition
• Convolutional neural network
• Absolute constraint

Convolutional neural networks (CNNs) have been widely used in computer vision community, and significantly improving the state-of-the-art. How to train an intra-class variant and inter-class discriminative feature is a central topic in face recognition. This paper proposes to learn an effective feature from face images by a joint loss function which combines the hard sample triplet (HST) and the absolute constraint triplet (ACT) loss, under the criteria that a maximum intra-class distance should be smaller than any inter-class distance. With the joint supervision of HST and ACT loss, CNNs is enable to learn discriminative features to improve face recognition performance. Experiments on labeled faces in the wild, IARPA Janus Benchmark (IJB-A) and YouTube Faces datasets achieve a comparable or superior performance to the state-of-the-arts.

• 出版日期2019-7
• 单位江南大学