Face contour extraction from front-view images

Abstract Facial feature detection is crucial for visual recognition of human faces. This paper reports on a study in detecting face contours from front-view ID-type pictures. Based on the eye and mouth positions, which can be detected by the algorithms proposed in Ref. 1 [G. Chow and X. Li, Pattern Recognition 26, 1739–1755 (1993)], a simplified adaptive Hough transform (AHT) technique is used to identify straight cheek lines which are approximately vertical from the edge image. Independently, parabolas forming the chin are detected by another AHT procedure. The location and curvature of the chin line, together with the cheek lines, characterize the shape of the face. This method was tested on over 70 different face images and is shown to produce results with a high degree of accuracy. We discuss in detail the specific issues involved in the detections, such as the definition of relevant subimage, parameter ranges, resolution of the accumulator array, peak cells, and end point determination.

[1]  A. Young,et al.  Handbook of Research on Face Processing , 1989 .

[2]  L Sirovich,et al.  Low-dimensional procedure for the characterization of human faces. , 1987, Journal of the Optical Society of America. A, Optics and image science.

[3]  Mark S. Nixon,et al.  Eye Spacing Measurement for Facial Recognition , 1985, Optics & Photonics.

[4]  Josef Kittler,et al.  The Adaptive Hough Transform , 1987, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[5]  Dana H. Ballard,et al.  Computer Vision , 1982 .

[6]  Xiaobo Li,et al.  Towards a system for automatic facial feature detection , 1993, Pattern Recognit..

[7]  Ruud M. Bolle,et al.  The Multiple Window Parameter Transform , 1992, IEEE Trans. Pattern Anal. Mach. Intell..

[8]  T. Sakai,et al.  Computer analysis and classification of photographs of human faces , 1973 .

[9]  Robert J. Baron.  STRENGTHS AND WEAKNESSES OF COMPUTER RECOGNITION SYSTEMS , 1989 .

[10]  Ian Craw,et al.  Finding Face Features , 1992, ECCV.

[11]  Dana H. Ballard,et al.  Generalizing the Hough transform to detect arbitrary shapes , 1981, Pattern Recognit..

[12]  Laurence C Lambert.  Evaluation and Enhancement of the AFIT Autonomous Face Recognition Machine. , 1987 .

[13]  W. J. Welsh,et al.  Classification of facial features for recognition , 1991, Proceedings. 1991 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[14]  W. Eric L. Grimson,et al.  On the Sensitivity of the Hough Transform for Object Recognition , 1990, IEEE Trans. Pattern Anal. Mach. Intell..

[15]  Chung-Lin Huang,et al.  Human facial feature extraction for face interpretation and recognition , 1992, Proceedings., 11th IAPR International Conference on Pattern Recognition. Vol.II. Conference B: Pattern Recognition Methodology and Systems.

[16]  Venu Govindaraju,et al.  Locating human faces in newspaper photographs , 1989, Proceedings CVPR '89: IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[17]  Josef Kittler,et al.  A survey of the hough transform , 1988, Comput. Vis. Graph. Image Process..

[18]  Ian Craw,et al.  Automatic extraction of face-features , 1987, Pattern Recognit. Lett..

[19]  Mohammed Atiquzzaman,et al.  Multiresolution Hough Transform-An Efficient Method of Detecting Patterns in Images , 1992, IEEE Trans. Pattern Anal. Mach. Intell..

[20]  Alex Pentland,et al.  Face Processing: Models For Recognition , 1990, Other Conferences.

[21]  Venu Govindaraju,et al.  A computational model for face location , 1990, [1990] Proceedings Third International Conference on Computer Vision.