A Novel Face Segmentation Algorithm from a Video Sequence for Real-Time Face Recognition
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Research Article A Novel Face Segmentation Algorithm from a Video Sequence for Real-Time Face Recognition R. Srikantaswamy1 and R. D. Sudhaker Samuel2 1 Department 2 Department
of Electronics and Communication, Siddaganga Institute of Technology, Tumkur 572103, Karnataka, India of Electronics and Communication, Sri Jayachamarajendra College of Engineering, Mysore, India
Received 1 September 2006; Accepted 14 April 2007 Recommended by Ebroul Izquierdo 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. Copyright © 2007 R. Srikantaswamy and R. D. Sudhaker Samuel. 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.
1.
INTRODUCTION
In literature, it is found that most of the face recognition work is carried out on still face images, which are carefully cropped and captured under well-controlled conditions. 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. Various methods have been proposed in literature for face detection. Important techniques include template-matching, neural network based, feature-based, motion-based and facespace methods [1]. Though most of these techniques are efficient, they are computationally expensive for real time applications. Skin colour has proved to be a fast and robust cue for human face detection, localization, and tracking [2]. Skin colour based face detection and localization however has the following drawbacks: (a) it gives only a coarse face segmentation, (b) it gives spurious results when the background is cluttered with skin colour regions. Further, appearance based holistic approaches based on statistical pattern recognition tools such as principal component analysis and linear discriminant analysis provides a compact nonlocal representation of face images, based on the appearance of an image at a specific view. Hence, these algorithms can
be regarded as picture recognition algorithm. Therefore, face presented for recognition to these a
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