Abstract
A face verification method is presented in this chapter by fusing the frequency and color features for improving the face recognition grand challenge performance. In particular, the hybrid color space RIQ is constructed, according to the discriminating properties among the individual component images. For each component image, the frequency features are extracted from the magnitude, the real and imaginary parts in the frequency domain of an image. Then, an improved Fisher model extracts discriminating features from the frequency data for similarity computation using a cosine similarity measure. Finally, the similarity scores from the three component images in the RIQ color space are fused by means of a weighted summation at the decision level for the overall similarity computation. To alleviate the effect of illumination variations, an illumination normalization procedure is applied to the R component image. Experiments on the Face Recognition Grand Challenge (FRGC) version 2 Experiment 4 show the feasibility of the proposed frequency and color fusion method.
This is a preview of subscription content, log in via an institution to check access.
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
Belhumeur, P.N., Hespanha, J.P., Kriegman, D.J.: Eigenfaces vs. Fisherfaces: Recognition using class specific linear projection. IEEE Trans. Pattern Analysis and Machine Intelligence 19(7), 711–720 (1997)
Etemad, K., Chellappa, R.: Discriminant analysis for recognition of human face images. J. Opt. Soc. Am. A 14, 1724–1733 (1997)
Finlayson, G.D., Hordley, S.D., Hubel, P.M.: Color by correlation: A simple, unifying framework for color constancy. IEEE Trans. Pattern Analysis and Machine Intelligence 23(11), 1209–1221 (2001)
Fisher, R.A.: The use of multiple measures in taxonomic problems. Ann. Eugenics 7, 179–188 (1936)
Fukunaga, K.: Introduction to Statistical Pattern Recognition, 2nd edn. Academic Press (1990)
Geusebroek, J.M., van den Boomgaard, R., Smeulders, A.W.M., Geerts, H.: Color invariance. IEEE Trans. Pattern Analysis and Machine Intelligence 23(12), 1338–1350 (2001)
Gonzalez, R.C., Woods, R.E.: Digital Image Processing, 2nd edn. Prentice Hall (2002)
Hsu, R.-L., Abdel-Mottaleb, M., Jain, A.K.: Face detection in color images. IEEE Trans. Pattern Analysis and Machine Intelligence 24(5), 696–706 (2002)
Hwang, W., Park, G., Lee, J., Kee, S.C.: Multiple face model of hybrid fourier feature for large face image set. In: Proc. 2006 IEEE Conf. Computer Vision and Pattern Recognition, CVPR (2006)
Kittler, J., Hatef, M., Robert, P.W., Matas, J.: On combining classifiers. IEEE Trans. Pattern Analysis and Machine Intelligence 20(3), 226–239 (1998)
Liu, C.: Enhanced independent component analysis and its application to content based face image retrieval. IEEE Trans. Systems, Man, and Cybernetics, Part B: Cybernetics 34(2), 1117–1127 (2004)
Liu, C.: Capitalize on dimensionality increasing techniques for improving face recognition grand challenge performance. IEEE Transactions on Pattern Analysis and Machine Intelligence 28(5), 725–737 (2006)
Liu, C.: The Bayes decision rule induced similarity measures. IEEE Transactions on Pattern Analysis and Machine Intelligence 29(6), 1086–1090 (2007)
Liu, C.: Learning the uncorrelated, independent, and discriminating color spaces for face recognition. IEEE Transactions on Information Forensics and Security 3(2), 213–222 (2008)
Liu, C., Wechsler, H.: Robust coding schemes for indexing and retrieval from large face databases. IEEE Trans. on Image Processing 9(1), 132–137 (2000)
Liu, C., Yang, J.: ICA color space for pattern recognition. IEEE Transactions on Neural Networks 20(2), 248–257 (2009)
Liu, Z., Liu, C.: Fusion of the complementary discrete cosine features in the yiq color space for face recognition. Computer Vision and Image Understanding 111(3), 249–262 (2008)
Liu, Z., Liu, C.: A hybrid color and frequency features method for face recognition. IEEE Trans. on Image Processing 17(10), 1975–1980 (2008)
Liu, Z., Liu, C.: Fusion of color, local spatial and global frequency information for face recognition. Pattern Recognition 43(8), 2882–2890 (2010)
Phillips, P.J., Flynn, P.J., Scruggs, T., Bowyer, K.W., Chang, J., Hoffman, K., Marques, J., Min, J., Worek, W.: Overview of the face recognition grand challenge. In: Proc. IEEE Conference on Computer Vision and Pattern Recognition (2005)
Shih, P., Liu, C.: Comparative assessment of content-based face image retrieval in different color spaces. International Journal of Pattern Recognition and Artificial Intelligence 19(7), 873–893 (2005)
Swets, D.L., Weng, J.: Using discriminant eigenfeatures for image retrieval. IEEE Trans. Pattern Analysis and Machine Intelligence 18(8), 831–836 (1996)
Tan, X., Triggs, B.: Fusing gabor and lbp feature sets for kernel-based face recognition. In: 2007 IEEE International Workshop on Analysis and Modaling of Faces and Gestures (October 2007)
Torres, L., Reutter, J.Y., Lorente, L.: The importance of color information in face recognition. In: Proc. IEEE Int. Conf. Image Processing, October 24-28 (1999)
Xie, C., Kumar, V.: Comparison of kernel class-dependence feature analysis (kcfa) with kernel discriminant analysis (kda) for face recognition. In: Proc. IEEE on Biometrics: Theory, Applicationa and Systems (BTAS 2007), September 27-29 (2007)
Yang, J., Liu, C.: Horizontal and vertical 2DPCA-based discriminant analysis for face verification on a large-scale database. IEEE Transactions on Information Forensics and Security 2(4), 781–792 (2007)
Yang, J., Liu, C.: Color image discriminant models and algorithms for face recognition. IEEE Transactions on Neural Networks 19(12), 2088–2098 (2008)
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
Copyright information
© 2012 Springer Berlin Heidelberg
About this chapter
Cite this chapter
Liu, Z., Liu, C. (2012). Frequency and Color Fusion for Face Verification. In: Cross Disciplinary Biometric Systems. Intelligent Systems Reference Library, vol 37. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-28457-1_4
Download citation
DOI: https://doi.org/10.1007/978-3-642-28457-1_4
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-28456-4
Online ISBN: 978-3-642-28457-1
eBook Packages: EngineeringEngineering (R0)