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Face recognition in unconstrained environment with CNN

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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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Authors and Affiliations

  1. Laboratoire de microélectronique et instrumentations, Faculté des sciences de Monastir, Université de Monastir, Monastir, Tunisia

    Hana Ben Fredj & Safa Bouguezzi

  2. Institut supérieur des sciences appliquées et de technologie de Sousse, Université de Sousse, Sousse, Tunisia

    Chokri Souani

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  1. Hana Ben Fredj
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  2. Safa Bouguezzi
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  3. Chokri Souani
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Correspondence to Hana Ben Fredj.

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Ben Fredj, H., Bouguezzi, S. & Souani, C. Face recognition in unconstrained environment with CNN. Vis Comput 37, 217–226 (2021). https://doi.org/10.1007/s00371-020-01794-9

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