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http://dx.doi.org/10.5391/JKIIS.2005.15.2.163

A New Face Tracking Method Using Block Difference Image and Kalman Filter in Moving Picture  

Jang, Hee-Jun (Dept. of Electronic Engineering, Soongsil University)
Ko, Hye-Sun (Computer Science, Sookmyung Women's Univ.)
Choi, Young-Woo (Computer Science, Sookmyung Women's Univ.)
Han, Young-Joon (Dept. of Electronic Engineering, Soongsil University)
Hahn, Hern-Soo (Dept. of Electronic Engineering, Soongsil University)
Publication Information
Journal of the Korean Institute of Intelligent Systems / v.15, no.2, 2005 , pp. 163-172 More about this Journal
Abstract
When tracking a human face in the moving pictures with complex background under irregular lighting conditions, the detected face can be larger including background or smaller including only a part of the face. Even background can be detected as a face area. To solve these problems, this paper proposes a new face tracking method using a block difference image and a Kalman estimator. The block difference image allows us to detect even a small motion of a human and the face area is selected using the skin color inside the detected motion area. If the pixels with skin color inside the detected motion area, the boundary of the area is represented by a code sequence using the 8-neighbor window and the head area is detected analysing this code. The pixels in the head area is segmented by colors and the region most similar with the skin color is considered as a face area. The detected face area is represented by a rectangle including the area and its four vertices are used as the states of the Kalman estimator to trace the motion of the face area. It is proved by the experiments that the proposed method increases the accuracy of face detection and reduces the fare detection time significantly.
Keywords
Face Detection; Face Trarking; Kalman Filter; Surveillance System;
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1 M.Turk and A.Pentland, 'Eigenfaces for Recognition,' J.Cognitive Neuroscience, vol.3, no.1, pp. 71-86,1991   DOI   ScienceOn
2 Rein-Lien Hsu, Abdel-Mottaleb, and M. Jain, A.K, 'Face detection in color images,' Pattern Analysis and Machine Intelligence, IEEE Transactions, PAMI, Vol.24, No 5, pp. 696-706, May 2002   DOI   ScienceOn
3 Bowyer, K.W, 'Face recognition technology: security versus privacy,' Technology and Society Magazine, IEEE, Volume. 23, pp. 9-19, 2004
4 K. Sung and T. Poggio, 'Example-Based Learning for View-Based Human Face Detection,' IEEE Trans. pattern Analysis and Machine Intelligence, vol.20, no.1, pp.39-51, Jan. 1998   DOI   ScienceOn
5 P. Sinha, 'Object Recognition via Image Invariants : A Case Study,' Investigative Ophthalmology and Visual Science, vol. 35, no. 4, pp. 1735-1740, 1994
6 A.Lanitis, C.J. Taylor, and T.F. Cootes, 'An Automatic Face Identification System Using Flexible Appearance Models,' Image and Vision Computing, vol. 13, no.5, pp. 393-401, 1995   DOI   ScienceOn
7 Ming-Hsuan Yang, David J. Kriegman, Narendra Ahuja, 'Detecting Faces in Images : A Survey,' Pattern Analysis and Machine Intelligence, IEEE Transactions, Vol.24, No 1, pp.34-58, 2002   DOI   ScienceOn
8 G. Yang and T.S. Huang, 'Human Face Detection in Complex Background,' Pattern Recognition, vol.27, no.1, pp. 53-63, 1994   DOI   ScienceOn