다면기법 SPFACS 영상객체를 이용한 AAM 알고리즘 적용 미소검출 설계 분석

Using a Multi-Faced Technique SPFACS Video Object Design Analysis of The AAM Algorithm Applies Smile Detection

  • 최병관 (가톨릭대학교 미디어기술콘텐츠학과)
  • 투고 : 2015.08.05
  • 심사 : 2015.09.04
  • 발행 : 2015.09.30


Digital imaging technology has advanced beyond the limits of the multimedia industry IT convergence, and to develop a complex industry, particularly in the field of object recognition, face smart-phones associated with various Application technology are being actively researched. Recently, face recognition technology is evolving into an intelligent object recognition through image recognition technology, detection technology, the detection object recognition through image recognition processing techniques applied technology is applied to the IP camera through the 3D image object recognition technology Face Recognition been actively studied. In this paper, we first look at the essential human factor, technical factors and trends about the technology of the human object recognition based SPFACS(Smile Progress Facial Action Coding System)study measures the smile detection technology recognizes multi-faceted object recognition. Study Method: 1)Human cognitive skills necessary to analyze the 3D object imaging system was designed. 2)3D object recognition, face detection parameter identification and optimal measurement method using the AAM algorithm inside the proposals and 3)Face recognition objects (Face recognition Technology) to apply the result to the recognition of the person's teeth area detecting expression recognition demonstrated by the effect of extracting the feature points.


연구 과제 주관 기관 : 가톨릭대학교


  1. Koni "Improved Face Detection Using Adaboost Algorithm tilted," Papers 2008,06 Article 12 No. 3.
  2. Ieek "Using Adaboost for Iris Recognition in Mobile Environments A Study on Eye Detection," paper, 2008.07 papers 2008-45CI-4-1.
  3. Ieek" AdaBoost and circular multitarget classification technique based on the function Rox "thesis paper 2010.03 2010-47CI-3-3
  4. Journal of Korea Multimedia Society "Adaboost algorithm, real-time face detection and tracking using," 2006,10 pp. 1266-1275.
  5. Stephen Milborrow, Fred nicolls, "Locating Facial Feature with an Extended Active shape Model," Lecture in computer science, 2008.
  6. C. Padgett, G. Cottrell, "Representing faceimages for emotion classification," Advances in Nural Information Processing System, Vol9, MIT Press, 2011.
  7. Mark F. Bear, Barry Connors, and Michael Paradiso, Neuroscience: Exploring the Brain 3E, 2009.
  8. V. 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.
  9. P. Ekman, W. V. Friesen. J. C. Hager, Facial Coding System: Manual, CD Rom, Sanfrancisco, CA, 2009.
  10. Y. Hu, D. Jiang. S. Yan, L. Zhang, H. zhang. "Automatic 3D reconstruction for face recognition," Proc. 6th IEEE Int'l Conf. on Automatic Face and Gesture Recognition, 2007, pp. 843-848.
  11. Yongmian Zhang, Qiang Ji "Facial Expression Understanding in Image Sequences Using Dynamic and Active Vision," Proceedings of the Ninth IEEE International Conforence on Computer Vision(ICCV 2008), 2-Volume, Set 2008.
  12. Kipo, "3-dimensional imaging technology," 2010 Technology Trends survey report, electrical/electronic field Article 1, 2010.11, pp. 80-82.
  13. S. Pastoor and M. Wopking, "3-D Displays: A review of current technologies," Displays, Vol. 17, 2008, pp. 100-110.
  14. N. Hiruma and T. Fukuda, "Accommodation response to binocular stereoscopic TV images and their viewing conditions," SMPTE Journal, Dec. 2010, pp. 1137-1144.