Single-Sample Face Recognition Based on Shared Generative Adversarial Network

Yuhua Ding, Zhenmin Tang and Fei Wang
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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

Mathematics, 2022, vol. 10, issue 5, 1-20

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.

Keywords: single-sample face recognition; shared generative adversarial network; softmax classifier (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2022
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