Face Detection using AdaBoost and ASM

AdaBoost와 ASM을 활용한 얼굴 검출

  • Lee, Yong-Hwan (Department Of Digital Contents, Wonkwang University) ;
  • Kim, Heung-Jun (Dept. of Computer Science and Engineering, Gyeongnam National University of Science and Technology)
  • 이용환 (원광대학교 디지털콘텐츠공학과) ;
  • 김흥준 (경남과학기술대학교 컴퓨터공학과)
  • Received : 2018.12.23
  • Accepted : 2018.12.25
  • Published : 2018.12.31


Face Detection is an essential first step of the face recognition, and this is significant effects on face feature extraction and the effects of face recognition. Face detection has extensive research value and significance. In this paper, we present and analysis the principle, merits and demerits of the classic AdaBoost face detection and ASM algorithm based on point distribution model, which ASM solves the problems of face detection based on AdaBoost. First, the implemented scheme uses AdaBoost algorithm to detect original face from input images or video stream. Then, it uses ASM algorithm converges, which fit face region detected by AdaBoost to detect faces more accurately. Finally, it cuts out the specified size of the facial region on the basis of the positioning coordinates of eyes. The experimental result shows that the method can detect face rapidly and precisely, with a strong robustness.



Supported by : 한국연구재단


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