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

Abstract

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.

Keywords

References

  1. D. Masip, J. Vitria, "Real Time Face Detection and Verification for Uncontrolled Environments", COST Workshop - Biometrics on the Internet Vigo, 2004.
  2. W.R. Han, Y.H. Lee, J.H. Park, Y.S. Kim, "Dynamic Emotion Classification through Facial Recognition", Journal of the Semiconductor & Display Technology, vol.12, no.3, pp.53-57, 2013.
  3. W. Zhao, R. Chellappa, P.J. Phillips and A. Rosenfeld, "Face Recognition: A Literature Survey", ACM Computing Surveys, vol.35, no.4, pp.399-458, 2003. https://doi.org/10.1145/954339.954342
  4. L.H. Thai, V.N. Truong, "Face Alignment using Active Shape Model and Support Vector Machine", International Journal of Biometrics and Bioinformatics, vol.4, issue.6, pp.224-234, 2011.
  5. P. Viola and M. J. Jones, "Robust Real-time Object Detection", Technical Report Series, Compaq Cambridge research Laboratory, CRL 2001/01, 2001.
  6. T. Jabid, M. H. Kabir, and O. Chae, "Robust Facial Expression Recognition based on Local Directional Pattern", ETRI Journal, Vol.32, No.5, pp.784-794, 2010. https://doi.org/10.4218/etrij.10.1510.0132
  7. Tim Cootes, An Introduction to Active Shape Models, Image Processing and Analysis, pp.223-248, Oxford University Press, 2000.
  8. T. F. Cootes, G. J. Edwards, C. J. Tahlor, "Active Appearance Models", IEEE Transaction on Pattern Analysis and Machine Intelligence, vol.23, no.6, 2001.
  9. T. F. Cootes, G. J. Edwards, C. J. Tahlor, "Comparing active shape models with active appearance models", British Machine Vision Conference, vol.1, pp.173-182, 1999.
  10. X. Yi, W. Ying and P. Jun, "An Improved AdaBoost Face Detection Algorithm based on the Weighting Parameters of Weak Classifier", IEEE International Conference on Cognitive Informatics and Cognitive Computing, 2013, pp. 347-350.
  11. G. Gibert, D. D'Alessandro and F. Lance, "Face Detection Method based on Photoplethysmography", IEEE International Conference on Advanced Video and Signal Based Surveillance, 2013, p.449-453.