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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| Published in | The Visual computer Vol. 37; no. 2; pp. 217 - 226 |
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| Main Authors | , , |
| Format | Journal Article |
| Language | English |
| Published |
Berlin/Heidelberg
Springer Berlin Heidelberg
01.02.2021
Springer Nature B.V |
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| 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. |
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| AbstractList | 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. |
| Author | Souani, Chokri Ben Fredj, Hana Bouguezzi, Safa |
| Author_xml | – sequence: 1 givenname: Hana orcidid: 0000-0003-2667-6132 surname: Ben Fredj fullname: Ben Fredj, Hana email: ben.fredj.hanaa@gmail.com organization: Laboratoire de microélectronique et instrumentations, Faculté des sciences de Monastir, Université de Monastir – sequence: 2 givenname: Safa surname: Bouguezzi fullname: Bouguezzi, Safa organization: Laboratoire de microélectronique et instrumentations, Faculté des sciences de Monastir, Université de Monastir – sequence: 3 givenname: Chokri surname: Souani fullname: Souani, Chokri organization: Institut supérieur des sciences appliquées et de technologie de Sousse, Université de Sousse |
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| StartPage | 217 |
| SubjectTerms | 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 |
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| Title | Face recognition in unconstrained environment with CNN |
| URI | https://link.springer.com/article/10.1007/s00371-020-01794-9 https://www.proquest.com/docview/2917939304 |
| Volume | 37 |
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