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Undersampled face recognition based on virtual samples and representation classification

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Abstract

Classifiers such as sparse representation or collaborative representation can get good performance in face recognition. But these methods require a number of train samples in each class to construct the dictionary. On the condition of undersampled train samples, their performance decreases dramatically. A novel method is proposed in this paper to address undersampled face recognition problem. Firstly, virtual face images are generated by principal component analysis and mirror transform. Secondly, the test sample is collaboratively represented by the augmented train samples and is recognized by classifier based on representation. A number of face recognition experiments on three benchmark face database show that the recognition accuracy of our method is greater than that of a similar method, while time efficiency of our method is competitive to similar method.

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Acknowledgements

This article was partly supported by the National Science and Technology Support Program (Nos. 2014BAH11F02, 2014BAH11F01), the National Nature Science Foundation of China (No. 61373163), the Scientific Research Fund of Sichuan Provincial Education Department (No. 15ZA0039), Project of Visual Computing and Virtual Reality Key Laboratory of Sichuan Province (No. PJ2012001) and the scientific research project of Sichuan Normal University (No. 14yb02).

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Authors and Affiliations

  1. College of Computer Science, Sichuan Normal University, Chengdu, China

    Jun Yang

  2. College of Mathematics and Software, Sichuan Normal University, Chengdu, China

    Yanli Liu

  3. Visual Computing and Virtual Reality Key Laboratory of Sichuan Province, Sichuan Normal University, Chengdu, China

    Jun Yang

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  1. Jun Yang
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  2. Yanli Liu
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Correspondence to Jun Yang.

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Yang, J., Liu, Y. Undersampled face recognition based on virtual samples and representation classification. Neural Comput & Applic 31, 2447–2453 (2019). https://doi.org/10.1007/s00521-017-3204-4

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  • DOI: https://doi.org/10.1007/s00521-017-3204-4

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