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An improvement to linear regression classification for face recognition

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Abstract

Linear regression classification (LRC) has attracted a great amount of attention owning to its promising performance in face recognition. However, its performance will dramatically decline in the scenario of limited training samples per class, particularly when only single training sample is available for a specific person. In this paper, a novel LRC based method is proposed to solve this problem. Specifically, we first perform LRC on the training set to obtain a kind of residual for each class. Next, a reverse representation residual is derived for each training sample of a class by exploiting the linear combination of the training samples of its nearest classes and the test sample. Then, we combine the reverse representation residuals of the class by an adaptive weighted-average approach to produce the other kind of residual. Finally, two kinds of residuals are fused to classify the test sample. Experimental results on the ORL, FERET, Libor94 and CMU-PIE face databases demonstrate that the proposed method obtains a higher recognition rate than some state-of-the-art face recognition methods.

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Acknowledgements

This work is supported by the National Natural Science Foundation of China (No. 61672333, 61402274, 61461025, 61703096), China Postdoctoral Science Foundation Special project (No. 2014T70937), China Postdoctoral Science Foundation (No. 2017M611655), the Program of Key Science and Technology Innovation Team in Shaanxi Province (No. 2014KTC-18), the Key Science and Technology Program of Shaanxi Province, China (No. 2016GY-081), the Natural Science Foundation of Jiangsu Province (No. BK20170691), the Fundamental Research Funds for the Central Universities (No. GK201803059, GK201803088), Interdisciplinary Incubation Project of Learning Science of Shaanxi Normal University, and the Natural Science Foundation of Shaanxi Province of China (No. 2018JM6050).

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Authors and Affiliations

  1. Key Laboratory of Modern Teaching Technology, Ministry of Education, Xi’an, 710062, China

    Yali Peng, Jingcheng Ke & Shigang Liu

  2. Engineering Laboratory of Teaching Information Technology of Shaanxi Province, Xi’an, 710119, Shaanxi, China

    Yali Peng, Jingcheng Ke & Shigang Liu

  3. School of Computer Science, Shaanxi Normal University, Xi’an, 710119, China

    Jingcheng Ke & Shigang Liu

  4. School of Automation, Southeast University Nanjing, Nanjing, 210096, China

    Jun Li

  5. College of Electronics and Information Engineering, Shaanxi University of Science and Technology, Xi’an, 710021, China

    Tao Lei

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  1. Yali Peng
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Correspondence to Shigang Liu.

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Peng, Y., Ke, J., Liu, S. et al. An improvement to linear regression classification for face recognition. Int. J. Mach. Learn. & Cyber. 10, 2229–2243 (2019). https://doi.org/10.1007/s13042-018-0862-1

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