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Single-Sample Face Recognition Based on Shared Generative Adversarial Network

Author

Listed:
  • Yuhua Ding

    (School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing 210094, China)

  • Zhenmin Tang

    (School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing 210094, China)

  • Fei Wang

    (College of Computer and Information, Hohai University, Nanjing 210098, China)

Abstract

Single-sample face recognition is a very challenging problem, where each person has only one labeled training sample. It is difficult to describe unknown facial variations. In this paper, we propose a shared generative adversarial network (SharedGAN) to expand the gallery dataset. Benefiting from the shared decoding network, SharedGAN requires only a small number of training samples. After obtaining the generated samples, we join them into a large public dataset. Then, a deep convolutional neural network is trained on the new dataset. We use the well-trained model for feature extraction. With the deep convolutional features, a simple softmax classifier is trained. Our method has been evaluated on AR, CMU-PIE, and FERET datasets. Experimental results demonstrate the effectiveness of SharedGAN and show its robustness for single sample face recognition.

Suggested Citation

  • Yuhua Ding & Zhenmin Tang & Fei Wang, 2022. "Single-Sample Face Recognition Based on Shared Generative Adversarial Network," Mathematics, MDPI, vol. 10(5), pages 1-20, February.
    • Handle: RePEc:gam:jmathe:v:10:y:2022:i:5:p:752-:d:759416
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