영상객체 spFACS ASM 알고리즘을 적용한 얼굴인식에 관한 연구

ASM Algorithm Applid to Image Object spFACS Study on Face Recognition

  • 최병관 (가톨릭대학교 미디어기술콘텐츠학과)
  • 투고 : 2016.09.07
  • 심사 : 2016.11.29
  • 발행 : 2016.12.30


Digital imaging technology has developed into a state-of-the-art IT convergence, composite industry beyond the limits of the multimedia industry, especially in the field of smart object recognition, face - Application developed various techniques have been actively studied in conjunction with the phone. Recently, face recognition technology through the object recognition technology and evolved into intelligent video detection recognition technology, image recognition technology object detection recognition process applies to skills through is applied to the IP camera, the image object recognition technology with face recognition and active research have. In this paper, we first propose the necessary technical elements of the human factor technology trends and look at the human object recognition based spFACS (Smile Progress Facial Action Coding System) for detecting smiles study plan of the image recognition technology recognizes objects. Study scheme 1). ASM algorithm. By suggesting ways to effectively evaluate psychological research skills through the image object 2). By applying the result via the face recognition object to the tooth area it is detected in accordance with the recognized facial expression recognition of a person demonstrated the effect of extracting the feature points.


  1. Pavlovic, A.Garg, M. Rehg. Multimodel speaker detection using error feedback dynamic Bayesian network, in: Proc. IEEE Internet. Conf. Computer Vision and Pattern Recognition, 2010.
  2. C.Padgett, G.Cottrell, "Representing faceimages for emotion classification," Advances in Nural Infor-mation Processing System. Vol. 9, MIT Press. 2011.
  3. P. Ekman, W.V. Friesen. J.C. Hager, Facial Coding System: Manual, CD Rom, Sanfrancisco, CA, 2011.
  4. Ieek "Adaboost and circular multitarget classification technique based on the function Rox," thesis paper 2010.03, 2010-47CI-3-3.
  5. S. Pastoor and M. Wopking, "3-D Displays: A review of current technologies," Displays, Vol.17, 2012, pp. 100-110.
  6. Kipo "3-dimensional imaging technology," 2001 Technology Trends survey report, electrical / electronic field Article 1, 2009. 11, pp. 80-82.
  7. N. Hiruma and T. Fukuda, "Accommodation response to binocular stereoscopic TV images and their viewing conditions," SMPTE Journal, Dec. 2008, pp. 1137-1144.
  8. J.O. Merrit, "Stereoscopic display appli ca tions issues-Part 1: Human factor Issues," Course Notes, IS&T/SPIE Symposium Electronic Imaging ScienceandTechnology, Feb.2007.
  9. S. Pastoor, "Human factors of 3D displays in advanced image communications," Displays, Vol. 14, No.3, 2013, pp. 150-157.
  10. P.Engeldrum, Psychometric Scaling Imcotek Press, Winchester, Massachusetts, USA., 2011.
  11. W. Blohm, I.P. Bleldie, K. Schenke, K. Fazel, S. Pastoor, "Stereoscopic image representation with synthetic depth of field," Journal of the SID, Vol.5, No.3, 2009, pp. 307-313.
  12. N. Hiruma and T. Fukuda, "Accommodation response to binocular stereoscopic TV images and their viewing conditions," SMPTE Journal, Dec. 2010, pp. 1137-1144.
  13. W.A. Ijsselsteijn, P. J. H. Seutiens and L. M. J. Meesters, "State-of-the-art in human factors and quality issues of stereoscopic broadcast television," Deliverable ATTEST/WP5/01, Aug. 2012, pp. 43-57.
  14. ETRI, CRC, "Binocular vision system, reduce fatigue Techniques," Final Report of the International Joint, 2013. 7.
  15. S. Yano and M. Emoto, "Two factors in visual fatigue caused by stereoscopic HDTV images," Proceedings of SPIE, Vol. 4864, Aug. 2010.