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Single-sample face recognition under varying lighting conditions based on logarithmic total variation

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Abstract

The logarithmic total variation (LTV) algorithm is a classical algorithm that is proposed to address the illumination interference in face recognition. Some state-of-the-art techniques based on LTV assume that the illumination component mainly lies in the low-frequency features among face images. However, these techniques adopt unsuitable methods to process low-frequency features, resulting in final unsatisfactory recognition rates. In this paper, we propose an improved illumination normalization method based on the LTV method, called the RETINA&TH-LTV algorithm. In this algorithm, the retina model is utilized to eliminate most of the illumination component in low-frequency features. Then, an advanced contrast-limited adaptive histogram equalization technique is proposed to remove the residual lighting component. At the same time, through realizing threshold-value filtering on high-frequency features, the enhancement of facial features is achieved. Finally, the processed frequency features are combined to form a robust holistic feature image, which is then utilized for recognition. Insufficient training images in face recognition are also taken into consideration in this research. Comparative experiments for single-sample face recognition are conducted on YALE B, CMU PIE and our self-built driver databases. The nearest neighbor classifier and extended sparse representation classifier are employed as classification methods. The results indicate that the RETINA&TH-LTV algorithm has promising performance, especially in serious illumination and insufficient training sample conditions.

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Acknowledgements

This work was supported by the National Natural Science Foundation of China (No. 61871123), Key Research and Development Program in Jiangsu Province (No. BE2016739) and a Project Funded by the Priority Academic Program Development of Jiangsu Higher Education Institutions.

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Authors and Affiliations

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

    Yang Zhang & Xiaobo Lu

  2. Key Laboratory of Measurement and Control of CSE, Ministry of Education, Southeast University, Nanjing, 210096, China

    Yang Zhang & Xiaobo Lu

  3. Faculty of Engineering and Information Technology, University of Technology Sydney, Ultimo, NSW, 2007, Australia

    Jun Li

Authors
  1. Yang Zhang
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  2. Xiaobo Lu
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  3. Jun Li
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Correspondence to Xiaobo Lu.

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Zhang, Y., Lu, X. & Li, J. Single-sample face recognition under varying lighting conditions based on logarithmic total variation. SIViP 13, 657–665 (2019). https://doi.org/10.1007/s11760-018-1394-6

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  • DOI: https://doi.org/10.1007/s11760-018-1394-6

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