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A Face Recognition Algorithm Based on LBP-EHMM

Tao Li1, Lingyun Wang1, Yin Chen1,*, Yongjun Ren1, Lei Wang1, Jinyue Xia2

1 College of Electronic and Information Engineering, Nanjing University of Information Science and Technology, Nanjing, 210044, China.
2 International Business Machines Corporation (IBM), New York, USA.
* Corresponding Author: Yin Chen. Email: cy_nuist@foxmail.com.

Journal on Artificial Intelligence 2019, 1(2), 59-68. https://doi.org/10.32604/jai.2019.06346

Abstract

In order to solve the problem that real-time face recognition is susceptible to illumination changes, this paper proposes a face recognition method that combines Local Binary Patterns (LBP) and Embedded Hidden Markov Model (EHMM). Face recognition method. The method firstly performs LBP preprocessing on the input face image, then extracts the feature vector, and finally sends the extracted feature observation vector to the EHMM for training or recognition. Experiments on multiple face databases show that the proposed algorithm is robust to illumination and improves recognition rate.

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APA Style
Li, T., Wang, L., Chen, Y., Ren, Y., Wang, L. et al. (2019). A face recognition algorithm based on LBP-EHMM. Journal on Artificial Intelligence, 1(2), 59-68. https://doi.org/10.32604/jai.2019.06346
Vancouver Style
Li T, Wang L, Chen Y, Ren Y, Wang L, Xia J. A face recognition algorithm based on LBP-EHMM. J Artif Intell . 2019;1(2):59-68 https://doi.org/10.32604/jai.2019.06346
IEEE Style
T. Li, L. Wang, Y. Chen, Y. Ren, L. Wang, and J. Xia "A Face Recognition Algorithm Based on LBP-EHMM," J. Artif. Intell. , vol. 1, no. 2, pp. 59-68. 2019. https://doi.org/10.32604/jai.2019.06346

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cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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