Abstract
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.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Yang, M.H., Kriegman, D., Ahuja, N.: Detecting Faces in Images: A Survey. IEEE Transactions on Pattern Analysis and Machine Intelligence (PAMI)Ā 24(1), 34ā58 (2002)
Lam, K., Yan, H.: Fast Algorithm for Locating Head Boundaries. J. Electronic ImagingĀ 3(4), 351ā359 (1994)
Jesorsky, O., Kirchberg, K.J., Frischholz, R.W.: Robust Face Detection Using the Hausdorff Distance. In: Bigun, J., Smeraldi, F. (eds.) AVBPA 2001. LNCS, vol.Ā 2091, pp. 90ā95. Springer, Heidelberg (2001)
Huttenlocher, D.P., Klanderman, G.A., Rucklidge, W.J.: Comparing Images Using the Hausdorff Distance. IEEE Transactions on Pattern Analysis and Machine IntelligenceĀ 14(9), 850ā853 (1993)
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)
Kirchberg, K.J., Jesorsky, O., Frischholz, R.W.: Genetic Model Optimization for Hausdorff Distance-Based Face Localization. In: Tistarelli, M., Bigun, J., Jain, A.K. (eds.) ECCV 2002. LNCS, vol.Ā 2359, pp. 103ā111. Springer, Heidelberg (2002)
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)
Srisuk, S., Kurutach, W., Limpitikeat, K.: A Novel Approach for Robust, Fast and Accurate Face Detection. International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems (IJUFKS)Ā 9(6), 769ā779 (2001)
Manian, V., Ross, A.: A Texture-based Approach to Face Detection. In: Biometric Consortium Conference (BCC), Crystal City, VA (September 2004)
Frƶba, B., KĆ¼blbeck, 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. 342ā347 (2002)
Rosenblum, M., Yacoob, Y., Davis, L.: Human Expression Recognition from Motion using a Radial Basis Function Network Architecture. IEEE Transactions on Neural NetworksĀ 7(5), 1121ā1138 (1996)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
Ā© 2005 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Suau, P. (2005). Adapting Hausdorff Metrics to Face Detection Systems: A Scale-Normalized Hausdorff Distance Approach. In: Bento, C., Cardoso, A., Dias, G. (eds) Progress in Artificial Intelligence. EPIA 2005. Lecture Notes in Computer Science(), vol 3808. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11595014_8
Download citation
DOI: https://doi.org/10.1007/11595014_8
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-30737-2
Online ISBN: 978-3-540-31646-6
eBook Packages: Computer ScienceComputer Science (R0)