Adapting Hausdorff Metrics to Face Detection Systems: A Scale-Normalized Hausdorff Distance Approach
Template matching face detection systems are used very often as a previous step in several biometric applications. These biometric applications, like face recognition or video surveillance systems, need the face detection step to be efficient and robust enough to achieve better results. One of many template matching face detection methods uses Hausdorff distance in order to search the part of the image more similar to a face. Although Hausdorff distance involves very accurate results and low error rates, overall robustness can be increased if we adapt it to our concrete application. In this paper we show how to adjust Hausdorff metrics to face detection systems, presenting a scale-normalized Hausdorff distance based face detection system. Experiments show that our approach can perform an accurate face detection even with complex background or varying light conditions.
KeywordsNormalization Factor Face Detection Template Match Complex Background Video Surveillance System
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- 5.Huttenlocher, D.P., Rucklidge, W.J.: A multi-resolution technique for comparing images using the Hausdorff distance, Technical Report 1321, Cornell University, Department of Computer Science (1992)Google Scholar
- 7.Shapiro, M.D., Blaschko, M.B.: On Hausdorff Distance Measures, Technical Report UM-CS-2004-071, Department of Computer Science, University of Massachusetts Amherst (2004)Google Scholar
- 9.Manian, V., Ross, A.: A Texture-based Approach to Face Detection. In: Biometric Consortium Conference (BCC), Crystal City, VA (September 2004)Google Scholar
- 10.Fröba, B., Küblbeck, C.: Robust Face Detection at Video Frame Rate Based on Edge Orientation Features. In: Fifth IEEE International Conference on Automatic Face and Gesture Recognition (FGR 2002), Washington, USA, pp. 342–347 (2002)Google Scholar