Abstract
In this paper investigate how to preprocess method from input images for robust face recognition varying illumination environments. By training the different classifiers with different clusters of training data and adopting fusion method considering fitness correlation between clusters we found out better recognition performance than combining classifiers fed with same data. The proposed method tries to provide adaptive preprocessing as well as by exploring the filter selection and fusion based on illumination cluster. Illuminant based clustering is enhanced face recognition ratio. Face image is clustered several cluster unsupervised or statistical method and we adopt adaptive filter each cluster. In this paper, some cluster is preprocessed by single filter others and some cluster adopted preprocessing by filter fusion. We found that the performance of individual filtering methods for image enhancement is highly depending upon face image cluster. Also, in this paper we present the recognition system using the table of fitness correlations between clusters for combining the results from the individual clusters. We present examples from real applications for bad illuminant face images.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Lim, L., Suen, C.Y.: Optimal combination of pattern classifiers. Pattern Recognition Letters 16, 945–954 (1995)
Kittler, J., Hatef, M., Duin, R.P.W., Matas, J.: On combining classifiers. IEEE Trans. Pattern Analysis and Machine Intelligence 20(3), 226–239 (1998)
Bay, S.D.: Nearest neighbor classification from multiple feature subsets. Intelligent Data Analysis 3(3), 191–209 (1999)
Indyk, P.: Approximate nearest neighbor algorithm for frechet distance via product metrics. In: Proc. of Symposium on Computational Geometry, p. 27 (2002)
Kuncheva, L.: Switching Between Selection and Fusion in Combining Classifiers. AnExperiment, IEEE Transaction on Systems, Man and Cybernetics—PARTB 32(2), 146–156 (2002)
Jobson, D.J., Rahman, Z.-u., Woodell, G.A.: The Spatial Aspect of Color and Scientific Implications of Retinex Image Processing. In: Proc. SPIE, vol. 4388, pp. 117–128 (2001)
Funt, B., Barnard, K.: Luminance-Based Multi-Scale Retinex. In: Rmalize proceedings AIC Colour 1997 8th Congress of the International Colour Association (1997)
Ramuhalli, P., Polikar, R., Udpa, L., Udpa, S.: Fuzzy ARTMAP network with evolutionary learning. In: Proc. of IEEE 25th Int. Conf. on Acoustics, Speech and Signal Processing (ICASSP 2000), Istanbul, Turkey, vol. 6, pp. 3466–3469 (2000)
Phillips, P.: The FERET database and evoluation procedure for face recognition algorithms. Image and Vision Computing 16(5), 295–306 (1999)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2005 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Nam, M.Y., Battulga, Rhee, P.K. (2005). Filter Selection and Identification Similarity Using Clustering Under Varying Illumination. In: Gagalowicz, A., Philips, W. (eds) Computer Analysis of Images and Patterns. CAIP 2005. Lecture Notes in Computer Science, vol 3691. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11556121_55
Download citation
DOI: https://doi.org/10.1007/11556121_55
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-28969-2
Online ISBN: 978-3-540-32011-1
eBook Packages: Computer ScienceComputer Science (R0)