基于深度自编码器的单样本人脸识别<sup>*</sup>

doi:10.16451/j.cnki.issn1003-6059.201704006

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Abstract Since there is only one sample for each subject, it is hard to describe intra-class variations of the subject. The performance of state-of-the-art face recognition algorithms declines in one sample per person(OSPP) face recognition. In this paper, an OSPP face recognition algorithm based on deep autoencoder(OSPP-DA) is proposed. In OSPP-DA, deep autoencoder is trained by all the images in the gallery firstly, and a generalized deep autoencoder(GDA) is generated. Then, the GDA is fine-tuned by the single sample of the subject, and a class-specified deep autoencoder(CDA) is obtained. For classification, query images are input to CDAs and the reconstruction samples of the corresponding subjects have the same intra-class variation as query images. A Softmax regression model is trained by the reconstruction samples and the query images are identified by the Softmax regression model. Experiments on public testing database are conducted and the results show the validity of OSPP-DA. Compared with some state-of-the-art algorithms, the proposed algorithm produces better performance with less time.

Key words： One Sample per Person Face Recognition Deep Autoencoder Sample Reconstruction

ZTFLH:

TP 391.4

Fund:Supported by Key Project for Science and Technology Research of Henan Provincial Department of Education(No.12A510027)

About author:: (ZHANG Yan, born in 1975, Ph.D. candidate, lecturer. Her research interests include image processing and pattern recognition.)
(PENG Hua(Corresponding author), born in 1973, Ph.D., professor. His research interests include software radio and communication signal processing.)

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ZHANG Yan,PENG Hua. One Sample per Person Face Recognition Based on Deep Autoencoder[J]. , 2017, 30(4): 343-352.

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http://manu46.magtech.com.cn/Jweb_prai/EN/10.16451/j.cnki.issn1003-6059.201704006 OR http://manu46.magtech.com.cn/Jweb_prai/EN/Y2017/V30/I4/343

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