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  • Research Article
  • Open Access

A Novel Face Segmentation Algorithm from a Video Sequence for Real-Time Face Recognition

EURASIP Journal on Advances in Signal Processing20072007:051648

  • Received: 1 September 2006
  • Accepted: 14 April 2007
  • Published:


The first step in an automatic face recognition system is to localize the face region in a cluttered background and carefully segment the face from each frame of a video sequence. In this paper, we propose a fast and efficient algorithm for segmenting a face suitable for recognition from a video sequence. The cluttered background is first subtracted from each frame, in the foreground regions, a coarse face region is found using skin colour. Then using a dynamic template matching approach the face is efficiently segmented. The proposed algorithm is fast and suitable for real-time video sequence. The algorithm is invariant to large scale and pose variation. The segmented face is then handed over to a recognition algorithm based on principal component analysis and linear discriminant analysis. The online face detection, segmentation, and recognition algorithms take an average of 0.06 second on a 3.2 GHz P4 machine.


  • Face Recognition
  • Video Sequence
  • Linear Discriminant Analysis
  • Recognition Algorithm
  • Face Region

Authors’ Affiliations

Department of Electronics and Communication, Siddaganga Institute of Technology, Tumkur, Karnataka, 572103, India
Department of Electronics and Communication, Sri Jayachamarajendra College of Engineering, Mysore, India


  1. Yang M-H, Kriegman DJ, Ahuja N: Detecting faces in images: a survey. IEEE Transactions on Pattern Analysis and Machine Intelligence 2002,24(1):34-58. 10.1109/34.982883View ArticleGoogle Scholar
  2. Vezhnevets V, Sazonov V, Andreeva A: A survey on pixel-based skin color detection techniques. Proceedings of the International Conference on Computer Graphics (GRAPHICON '03), September 2003, Moscow, Russia 85–92.Google Scholar
  3. Wren CR, Azarbayejani A, Darrell T, Pentland AP: Pfinder: real-time tracking of the human body. IEEE Transactions on Pattern Analysis and Machine Intelligence 1997,19(7):780-785. 10.1109/34.598236View ArticleGoogle Scholar
  4. Stauffer C, Grimson WEL: Adaptive background mixture models for real-time tracking. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR '99), June 1999, Fort Collins, Colo, USA 2: 246–252.View ArticleGoogle Scholar
  5. Turk M, Pentland A: Eigenfaces for recognition. Journal of Cognitive Neuroscience 1991,3(1):71-86. 10.1162/jocn.1991.3.1.71View ArticleGoogle Scholar
  6. Belhumeur PN, Hespanha JP, Kriegman DJ: Eigenfaces vs. Fisherfaces: recognition using class specific linear projection. Proceedings of the 4th European Conference on Computer Vision (ECCV '96), April 1996, Cambridge, UK 1: 45–58.Google Scholar


© R. Srikantaswamy and R. D. Sudhaker Samuel. 2007

This article is published under license to BioMed Central Ltd. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.