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Face recognition in unconstrained environment with CNN.
- Source :
- Visual Computer; Feb2021, Vol. 37 Issue 2, p217-226, 10p
- Publication Year :
- 2021
-
Abstract
- In recent years, convolutional neural networks have proven to be a highly efficient approach for face recognition. In this paper, we develop such a framework to learn a robust face verification in an unconstrained environment using aggressive data augmentation. Our objective is to learn a deep face representation from large-scale data with massive noisy and occluded face. Besides, we add an adaptive fusion of softmax loss and center loss as supervision signals, which are helpful to improve the performance and to conduct the final classification. The experiment results show that the suggested system achieves comparable performances with other state-of-the-art methods on the Labeled Faces in the Wild and YouTube face verification tasks. [ABSTRACT FROM AUTHOR]
- Subjects :
- CONVOLUTIONAL neural networks
DEEP learning
HUMAN facial recognition software
Subjects
Details
- Language :
- English
- ISSN :
- 01782789
- Volume :
- 37
- Issue :
- 2
- Database :
- Complementary Index
- Journal :
- Visual Computer
- Publication Type :
- Academic Journal
- Accession number :
- 149049722
- Full Text :
- https://doi.org/10.1007/s00371-020-01794-9