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Face Detection Method for Public Safety Surveillance based on Convex Grouping

Jianhui Wu1,2, Feng Huang1,2, Wenjing Hu2,Wei He1,2, Bing Tu1,2, Longyuan Guo1,2, Xianfeng Ou1,2,*

1 Key Laboratory of Optimization and Control for Complex Systems,College of Information & Communication Engineering
2 Research Center for Heterogeneous Computing and It’s Application, Hunan Institute of Science &Technology. Hunan Province, Yueyang 414006, China)

∗ Corresponding authors: , , Guoyun Zhang1,2,*

Computer Systems Science and Engineering 2018, 33(5), 327-334.


Face detection is very important in video surveillance of public safety. This paper proposed a face detection method based on the best optimization convex grouping to detect the face regions from different face shape images at actual conditions. Firstly, the basic principle of convex grouping was discussed, the main rules of convex and the structure of the convex polygons was described. And then the best optimization convex grouping algorithm of the convex polygons was designed. At last, all of the algorithms, which used the best optimization convex grouping to detect the face region on the data set of MIT single face sample library were tested. This sample library contains positive faces, side faces and other sorts of posture. The experiment result showed that the best optimization convex grouping method could detected the face regions accurately, even if the face postures are positive, side or others, our proposed method was effective, and it was not affected by color and light. Compared with other typical algorithms, this proposed method had higher detection accuracy, and it could be used directly without training. Meanwhile, there was a better stability and reliability in the actual processing, which could satisfy the requirements of practical application.


Cite This Article

J. Wu, F. Huang, W. Hu, W. He, B. Tu et al., "Face detection method for public safety surveillance based on convex grouping," Computer Systems Science and Engineering, vol. 33, no.5, pp. 327–334, 2018.

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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