Face recognition in unconstrained environment with CNN

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...

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Bibliographic Details
Published inThe Visual computer Vol. 37; no. 2; pp. 217 - 226
Main Authors Ben Fredj, Hana, Bouguezzi, Safa, Souani, Chokri
Format Journal Article
LanguageEnglish
Published Berlin/Heidelberg Springer Berlin Heidelberg 01.02.2021
Springer Nature B.V
Subjects
Accuracy
Artificial Intelligence
Artificial neural networks
Computer Graphics
Computer Science
Data augmentation
Datasets
Deep learning
Face recognition
Facial recognition technology
Image Processing and Computer Vision
Methods
Neural networks
Original Article
Verification
Deep learning
Face recognition
Data augmentation
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Summary: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.
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ISSN:0178-2789
1432-2315
DOI:10.1007/s00371-020-01794-9