Computer Science ›› 2021, Vol. 48 ›› Issue (4): 197-204.doi: 10.11896/jsjkx.200600033

• Computer Graphics & Multimedia • Previous Articles     Next Articles

Optimization of GPU-based Eigenface Algorithm

LI Fan1, YAN Xing2, ZHANG Xiao-yu1   

  1. 1 Network & Experimental Teaching Center,Xinjiang University of Finance and Economics,Urumqi 830012,China
    2 School of Information Management,Xinjiang University of Finance and Economics,Urumqi 830012,China
  • Received:2020-06-24 Revised:2020-08-28 Online:2021-04-15 Published:2021-04-09
  • About author:LI Fan,born in 1974,Ph.D,associate professor.His main research interests include high-performance computing and so on.
  • Supported by:
    National Natural Science Foundation of China(41830101),Social Science Foundation of Xinjiang Uygur Autonomous Region(17BTQ093) and Doctoral Research Start-up Fund of Xinjiang University of Finance and Economics(2015BS003).

Abstract: Eigenface algorithm is one of the commonly used face recognition methods based on facial representation.When the amount of training data is large,it is very time-consuming both training and testing modules.Based on this,the CUDA parallel computing architecture is used to implement GPU accelerated eigenface algorithm.The effect of GPU parallel computing depends on the hardware specifications,the complexity and parallelism of the algorithm itself,and the parallelization method used by the program developer to use GPU.Therefore,this paper first proposes the calculation of the average value and zero mean in the training phase of the eigenface algorithm.The calculation steps such as normalizing the eigenface and the calculation steps of the projection to the eigenface space and calculating the Euclidean distance in the test phase are optimized and accelerated by GPU.Secondly,different parallelization acceleration methods are used in the corresponding calculation steps and performance evaluation is made.Experimental results show that in the range of face training data from 320 to 1920,the acceleration effect of each calculation step is obvious.Compared with Intel i7-5960X,the GTX1060 display adapter can achieve an average acceleration effect of about 71.7 times in the training module,and an average acceleration effect of about 34.1 times in the test module.

Key words: Eigenface, Face recognition, GPU parallel computing, Kernel function, Rotary operation

CLC Number: 

  • TP301
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