Browse > Article

A Study on Real-time Tracking Method of Horizontal Face Position for Optimal 3D T-DMB Content Service  

Kang, Seong-Goo (School of Information & Communication Engineering, Sungkyunkwan University)
Lee, Sang-Seop (Samsung Techwin)
Yi, June-Ho (School of Information & Communication Engineering, Sungkyunkwan University)
Kim, Jung-Kyu (School of Information & Communication Engineering, Sungkyunkwan University)
Publication Information
Abstract
An embedded mobile device mostly has lower computation power than a general purpose computer because of its relatively lower system specifications. Consequently, conventional face tracking and face detection methods, requiring complex algorithms for higher recognition rates, are unsuitable in a mobile environment aiming for real time detection. On the other hand, by applying a real-time tracking and detecting algorithm, we would be able to provide a two-way interactive multimedia service between an user and a mobile device thus providing a far better quality of service in comparison to a one-way service. Therefore it is necessary to develop a real-time face and eye tracking technique optimized to a mobile environment. For this reason, in this paper, we proposes a method of tracking horizontal face position of a user on a T-DMB device for enhancing the quality of 3D DMB content. The proposed method uses the orientation of edges to estimate the left and right boundary of the face, and by the color edge information, the horizontal position and size of face is determined finally to decide the horizontal face. The sobel gradient vector is projected vertically and candidates of face boundaries are selected, and we proposed a smoothing method and a peak-detection method for the precise decision. Because general face detection algorithms use multi-scale feature vectors, the detection time is too long on a mobile environment. However the proposed algorithm which uses the single-scale detection method can detect the face more faster than conventional face detection methods.
Keywords
Face detection; Eye tracking; Sobel edge; 3D T-DMB;
Citations & Related Records
Times Cited By KSCI : 1  (Citation Analysis)
연도 인용수 순위
1 Y.K. Park, K. Jung, Y. Oh, S. Lee, J.K. Kim, G. Lee, H. Lee, K. Yun, N. Hur, and J. Kim "Depth-image-based rendering for 3DTV service over T-DMB," Signal Processing: Image Communication, Vol. 24, No. 1-2, pp. 122-136, Jan. 2009.   DOI   ScienceOn
2 Neil A. Dogson , "Autostereoscopic 3D Displays," Computer, pp. 31-36, Aug. 2005.
3 Erik Hjelmas, Boon Kee Low, "Face Detection: A Survey," Computer Vision and Image Understanding, Vol. 83, No. 3, pp.236-274, Sep. 2001.   DOI   ScienceOn
4 Paul Viola, "Robust Real-Time Face Detection," International Journal of Computer Vision, Vol. 57, No. 2, pp.137-154, 2004.
5 Chengjun Liu, "A Bayesian discriminating features method for face detection," Pattern Analysis and Machine Intelligence, IEEE Transactions on, Vol. 25, Issue 6, pp. 725-740, Jun. 2003.   DOI   ScienceOn
6 J. Canny, "A computational approach to edge detection," IEEE Trans. PAMI, Vol. 8, pp. 679-714, Nov. 1986.
7 P. Kakumanu, "A survey of skin-color modeling and detection methods," Pattern Recognition, Vol. 40, Issue 3, pp. 1106-1122, Mar. 2007.   DOI   ScienceOn
8 Hai Han, Tsuyoshi, T., Nagata, R., "Eye Detection Based On Grayscale Morphology," TENCON '02. Proceedings. 2002 IEEE Region 10 Conference on Computers, Communications, Control and Power Engineering , Vol. 1 , 28-31, Oct. 2002.
9 김성훈, 이현수, "피부색 영역의 분할을 통한 후보 검출과 부분 얼굴 분류기에 기반을 둔 얼굴 검출 시스템," 전자공학회논문지, 제47권 CI편 제2호, 11-20쪽, 2010년 3월
10 CMU Image Database: face (http://vasc.ri.cmu.edu/idb/html/face)
11 Caltech Faces 1999 Databse (http://www.vision.caltech.edu/html-files/archive.html)