Abstract
We consider the problem of fusing colour information to enhance the performance of a face authentication system. The discriminatory information potential of a vast range of colour spaces is investigated. The verification process is based on the normalised correlation in an LDA feature space. A sequential search approach which is in principle similar to the “plus L and take away R” algorithm is applied in order to find an optimum subset of the colour spaces. The colour based classifiers are combined using the SVM classifier. We show that by fusing colour information using the proposed method, the resulting decision making scheme considerably outperforms the intensity based verification system.
Chapter PDF
References
Berens, J., Finlayson, G.: Log-opponent chromaticity coding of colour space. In: Proceedings of the Fourth IEEE International Conference on Pattern Recognition, pp. 1206–1211. IEEE Computer Society Press, Los Alamitos (2000)
Colantoni, P., et al.: Color space transformations. Technical report, http://www.raduga-ryazan.ru/files/doc/colorspacetransform95.pdf
Foley, J., van Dam, A., Feiner, S., Hughes, J.: Computer graphics: principles and practice, 2nd edn. Addison-Wesley Longman Publishing Co., Inc., Boston, MA, USA (1996)
Gevers, T., Smeulders, A.: Colour based object recognition. In: ICIAP, vol. 1, pp. 319–326 (1997)
Kawato, S., Ohya, J.: Real-time detection of nodding and head-shaking by directly detecting and tracking the ”between-eyes”. In: Proceedings of the Fourth IEEE International Conference on Automatic Face and Gesture Recognition, pp. 40–45. IEEE Computer Society Press, Los Alamitos (2000)
Kittler, J., Sadeghi, M.: Physics-based decorrelation of image data for decision level fusion in face verification. In: Roli, F., Kittler, J., Windeatt, T. (eds.) MCS 2004. LNCS, vol. 3077, pp. 354–363. Springer, Heidelberg (2004)
Marcel, S., Bengio, S.: Improving face verification using skin colour information. In: 16th International Conference on Pattern Recognition, vol. 2, pp. 20378–20382 (2002)
Ohta, Y., Kanade, T., Sakai, T.: Colour information for region segmentation. Computer Graphics and Image Processing 13(3), 222–241 (1980)
Platt, J.: Sequential minimal optimization: A fast algorithm for training support vector machines. Technical Report 98-14, Microsoft Research, Redmond, Washington (April 1998)
Pudil, P., Novovicova, J., Kittler, J.: Floating search methods in feature selection. Pattern Recognition Letters 15, 1119–1125 (1994)
Sadeghi, M., Khoshrou, S., Kittler, J.: Colour feature selection for face authentication. In: MVA 2007. Proceedings of the International Conference on Macine Vision Applications, Japan, May 2007 (2007)
Sadeghi, M., Khoshrou, S., Kittler, J.: Confidence based gating of colour features for face authentication. In: MCS 2007. Proceedings of the 7th International Workshop on Multiple Classifier System, Czech Republi, May 2007, pp. 121–130 (2007)
Sadeghi, M., Kittler, J.: A comparative study of data fusion strategies in face verification. In: The 12th European Signal Processing Conference, Vienna, Austria, 6-10 September 2004 (2004)
Sadeghi, M., Kittler, J.: Decision making in the LDA space: Generalised gradient direction metric. In: The 6th International Conference on Automatic Face and Gesture Recognition, Seoul, Korea, May 2004, pp. 248–253 (2004)
Vapnik, V.: The Nature of Statistical Learning Theory. Springer, New York (1995)
Vertan, C., Cuic, M., Boujemaa, N.: On the introduction of a chrominance spectrum and its applications. In: Proceedings of the First International Conference on Colour in Graphics and Image Processing, 1-4 October 2000, pp. 214–218 (2000)
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 2007 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Sadeghi, M.T., Khoshrou, S., Kittler, J. (2007). SVM-Based Selection of Colour Space Experts for Face Authentication. In: Lee, SW., Li, S.Z. (eds) Advances in Biometrics. ICB 2007. Lecture Notes in Computer Science, vol 4642. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-74549-5_95
Download citation
DOI: https://doi.org/10.1007/978-3-540-74549-5_95
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-74548-8
Online ISBN: 978-3-540-74549-5
eBook Packages: Computer ScienceComputer Science (R0)