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Face recognition based on local binary pattern and improved Pairwise-constrained Multiple Metric Learning

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Abstract

During the image acquisition process of face recognition, the obtained face images are affected inevitably by varied illumination and position in different environment. Local Binary Pattern (LBP) operator is used to decrease illumination effectiveness. Improved Pairwise-constrained Multiple Metric Learning method (IPMML) is proposed as a classification metric in our prior work, which solves the misalignment problem in a better way compared with PMML. To solve the high computation complexity of IPMML, Linear Discriminant Analysis (LDA) is performed before IPMML. Thus, a face recognition method based on LBP and IPMML is proposed, which can overcome the illumination and misalignment problems. LBP is selected to extract texture features of face images firstly. Second, LDA is applied to reduce the dimension. Then the fisher features are divided into sub-blocks according to the dimension of features and every block is a column vector. Fourth, a classification metric -- IPMML is used to obtain the optimum Mahalanobis matrix. Fifth, the Mahalanobis matrix is used to compute the final discriminative distance. Finally, the Nearest Neighborhood Classifier (NNC) is applied to classify face images. The experimental results show that the proposed method can achieve high recognition rates and is robust to illumination and facial expression variation, especially for misaligned face images.

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Acknowledgements

This work was supported by Shandong Province Natural Science Foundation, China: ZR2016FQ14, the Key Research and Development Programs of Shandong Province Project under grant 2018GGX101040, Science and Technology Research Program for Colleges and Universities in Shandong Province under Grant J18KA315.

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Authors and Affiliations

  1. School of Information and Control Engineering, Qingdao University of Technology, Qingdao, 2660520, People’s Republic of China

    Lijian Zhou, Hui Wang, Shanshan Lin & Siyuan Hao

  2. Jinan Technician College, Jinan, 2660520, People’s Republic of China

    Hui Wang

  3. School of Aeronautics and Astronautics, Zhejiang University, Hangzhou, 310027, People’s Republic of China

    Zhe-Ming Lu

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  1. Lijian Zhou
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  2. Hui Wang
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  3. Shanshan Lin
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  4. Siyuan Hao
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  5. Zhe-Ming Lu
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Correspondence to Lijian Zhou.

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Zhou, L., Wang, H., Lin, S. et al. Face recognition based on local binary pattern and improved Pairwise-constrained Multiple Metric Learning. Multimed Tools Appl 79, 675–691 (2020). https://doi.org/10.1007/s11042-019-08157-0

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