Browse > Article

Face Detection Using A Selectively Attentional Hough Transform and Neural Network  

Choi, Il (Department of Electronics, Kyungpook National University)
Seo, Jung-Ik (Department of Electronics, Kyungpook National University)
Chien, Sung-Il (Department of Electronics, Kyungpook National University)
Publication Information
Journal of the Institute of Electronics Engineers of Korea SP / v.41, no.4, 2004 , pp. 93-101 More about this Journal
Abstract
A face boundary can be approximated by an ellipse with five-dimensional parameters. This property allows an ellipse detection algorithm to be adapted to detecting faces. However, the construction of a huge five-dimensional parameter space for a Hough transform is quite unpractical. Accordingly, we Propose a selectively attentional Hough transform method for detecting faces from a symmetric contour in an image. The idea is based on the use of a constant aspect ratio for a face, gradient information, and scan-line-based orientation decomposition, thereby allowing a 5-dimensional problem to be decomposed into a two-dimensional one to compute a center with a specific orientation and an one-dimensional one to estimate a short axis. In addition, a two-point selection constraint using geometric and gradient information is also employed to increase the speed and cope with a cluttered background. After detecting candidate face regions using the proposed Hough transform, a multi-layer perceptron verifier is adopted to reject false positives. The proposed method was found to be relatively fast and promising.
Keywords
Face Detection; Hough Transform; Ellipse Detection; Neural Network;
Citations & Related Records
연도 인용수 순위
  • Reference
1 A. S. Aguado, M. E. Montiel, and M. S. Nixon, 'On using directional information for parameter space decomposition in ellipse detection,' Pattern Recognition, vol. 29, no. 3, pp. 369- 381, 1996   DOI   ScienceOn
2 D. Reisfeld, H. Wolfson, and Y. Yeshurun, 'Context-free attentional operators: The generalized symmetry transform,' International Journal of Computer Vision, 31, pp. 50-66, 1985   DOI
3 Y. Lei and K. C. Wong, 'Ellipse detection based on symmetry,' Pattern Recognition Letters, vol. 20, pp. 41-47, 1999   DOI   ScienceOn
4 Y. H. Kwon and N. da V. Lobo, 'Face detection using templates,' Proc. of the 12th IAPR Workshop, vol. 1, pp. 764-767, 1994   DOI
5 Y. Yokoo and M. Hagiwara, 'Human faces detection method using genetic algorithm,' Proc. of IEEE conf. Evolutionary Comutation, pp. 113-118, 1996   DOI
6 J. Wang and T. Tan, 'A new face detection method based on shape information,' Pattern Recognition Letters, 21, pp. 463-471, 2000.   DOI   ScienceOn
7 C.Kimme, D.H.Ballard. J.Sklansky, 'Finding Circles by an array of Accumulators,' Comm of ACM, Vol. 18, No. 2, pp. 120-I22, Feb., 1975   DOI   ScienceOn
8 R. Chellappa, C.Wilson, and S.Sirohey, 'Human and machine recognition of faces: A survey.'Proc. IEEE, Vo1. 83, pp. 705-740, May 1995   DOI   ScienceOn
9 H. A. Rowley, S. Baluja, and T. Kanade, 'Neural network-based face detection,' IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 20, no. 1, pp. 23-38, 1998   DOI   ScienceOn
10 K. 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, 1998   DOI   ScienceOn
11 A. Samal and P. A. Iyenger, 'Automatic recognition and analysis of human faces and facial expression: A survey,' Pattern Recognition, vol. 25, pp. 65-77, 1992   DOI   ScienceOn