JOURNAL OF SHANDONG UNIVERSITY (ENGINEERING SCIENCE) ›› 2016, Vol. 46 ›› Issue (3): 74-78.doi: 10.6040/j.issn.1672-3961.2.2015.075

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Face recognition based on kernel Bayesian compressive sensing

ZHOU Kai1,2, YUAN Changan1,2*, QIN Xiao1, ZHENG Yan2, FENG Wenduo2   

  1. 1. College of Computer and Information Engineering, Guangxi Teachers Education University, Nanning 530001, Guangxi, China;
    2. School of Computer, Electronics and Information, Guangxi University, Nanning 530004, Guangxi, China
  • Received:2015-05-20 Online:2016-06-30 Published:2015-05-20

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Abstract: In order to improve the speed and rate of face recognition, Bayesian compressive sensing algorithm was applied and its kernel extension to face recognition was proposed. Combined with the spatial pyramid model, statistical local feature was improved to extract the features of face images. Firstly, the statistical local feature was used as a feature extractor to obtain facial features and a second layer of local statistics was processed based on the former layer. Then the spatial pyramid was used to obtain features in different spatial scales in order to accomplish the final step of face recognition, the features were classified through kernel Bayesian compressive sensing. The experimental results on the basis of the AR and FERET databases demonstrated that this algorithm had better performance than other traditional ones.

Key words: face recognition, kernel function, statistical local feature, spatial pyramid model, Bayesian compressive sensing

CLC Number: 

  • TP391
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