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- Title
Discriminative metric learning for face verification using enhanced Siamese neural network.
- Authors
Lu, Tao; Zhou, Qiang; Fang, Wenhua; Zhang, Yanduo
- Abstract
Although face verification algorithms have made great success under controlled conditions in recent years, there's plenty of room at its performance under uncontrolled real-world due to lack of discriminative feature representation ability. From the perspective of metric learning, we proposed a context-aware based Siamese neural network (CASNN) to learn a simple yet powerful network for face verification task to enhance its discriminative feature representation ability. Firstly, a context-aware module is used to automatically focus on the key area of the input facial images without irrelevant background area. Then we design a Siamese network equipped with center-classification loss to compress intra-class features and enlarge between-class ones for discriminative metric learning. Finally, we propose a quantitative indicator named "D-score" to show the discriminative representation ability of the learnt features from different methods. The extensive experiments are conducted on LFW dataset, YouTube Face dataset (YTF) and real-world dataset. The results confirm that CASNN outperforms some state-of-the-art deep learning-based face verification methods.
- Subjects
ARTIFICIAL neural networks
- Publication
Multimedia Tools & Applications, 2021, Vol 80, Issue 6, p8563
- ISSN
1380-7501
- Publication type
Article
- DOI
10.1007/s11042-020-09784-8