Abstract
In automated face recognition, a human face can be described by several features, but very few of them are used in combination to improve discrimination ability. This paper demonstrates how different feature sets can be used to enhance discrimination for the purpose of face recognition. We have used geometrical features and Gabor features in combination for face recognition. The geometrical features include distances, areas, fuzzy membership values and evaluation values of the facial features namely eyes, eyebrows, nose and mouth. The Geometric-Gabor features are extracted by applying the Gabor filters on the highly energized facial feature points on the face. These features are more robust to image variations caused by the imprecision of facial feature localization. An Extended-Geometric feature vector is constructed by combining both the feature sets and is found to achieve satisfactory results for face recognition using a simple matching function. The matching performance is analyzed for both the feature sets as well as for an Extended-Geometric feature vector. Experimental results demonstrate that no feature set alone is sufficient for recognition but the Extended-Geometric feature vector yields an improved recognition rate and speed at reduced computational cost and yet it is more discriminating and easy to discern from others.
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Brunelli, R., Poggio, T.: Face recognition: Features versus Templates. IEEE Trans. on PAMI 15(10), 1042–1052 (1993)
Belhumeur, P.N., Hespanha, J.P., Kriegman, D.J.: Eigenfaces vs. Fisherfaces: Recognition using class specific Linear Projection. IEEE Trans. on PAMI 19(7), 711–720 (1997)
Duc, B., Fisher, S., Bigün, J.: Face Authentication with Gabor Information on Deformable Graphs. IEEE Transactions on Image Proc. 8(4), 504–515 (1999)
Daugman, J.D.: Two dimensional spectral analysis of cortical receptive field profiles. Vision Research 20, 847–856 (1980)
Gabor, D.: Theory of Communications. Jr. of Institute of Elect. Eng. 93, 429–557 (1946)
Gutkowski, P.: Algorithm for retrieval and verification of person identity using bimodal biometrics. Jr. of Information fusion 5(1), 65–71 (2004)
He, X., Yan, S., Hu, Y., Niyogi, P., Zhang, H.-J.: Face Recognition Using Laplacianfaces. IEEE transactions on PAMI 27(3), 328–340 (2005)
Hiremath, P.S., Danti, A.: Optimized Geometrical Feature Vector for Face Recognition. In: Proceedings of the International Conference on Human Machine Interface, Indian Institute of Science, Bangalore, December 2004, pp. 309–320 (2004)
Hiremath, P.S., Danti, A.: Detection of multiple faces in an image using skin color information and Lines-of-Separability face model. Intl. Jr. of Pattern Recognition and Artificial Intelligence (Accepted on May 2005)
Jain, A.K., Ross, A., Prabhakar, S.: An Introduction to Biometric Recognition. IEEE Trans on circuits and systems for video technology 14(1), 4–20 (2004)
Kim, T.-K., Kittler, J.: Locally Linear Discriminant Analysis for Multimodally Distributed Classes for Face Recognition with a Single Model Image. IEEE transactions on PAMI 27(3), 318–327 (2005)
Olugbenga, A., Yang, Y.-H.: Face Recognition approach based on rank correlation of Gabor-Filtered images. Pattern Recognition 35, 1275–1289 (2002)
Shan, S., Gao, W., Chang, Y., Cao, B., Yang, P.: Review the strength of Gabor features for face recognition from the angle of its robustness miss-alignment. In: Proc. of the 17th Intl. Conference on Pattern recognition, ICPR 2004 (2004)
Turk, M., Pentland, A.: Eigenfaces for recognition. Journal of Cognitive Neuroscience 3(1), 71–86 (1991)
Wiskott, L., Fellous, J.M., Krüger, N.: Christoph von der Malsburg: Face Recognition by Elastic Graph Matching. In: Intelligent Biometric Techniques in fingerprint and Face Recognition, ch. 11, pp. 355–396. CRC Press, Boca Raton (1999)
Zhao, W., Chellappa, R., Rosenfeld, A., Phillips, P.J.: Face recognition: A Literature survey. Tech. Reports of Comp. Vision Lab. of Univ. of Maryland (2000)
Zhang, H., Zhang, B., Huang, W., Tian, Q.: Gabor wavelet associate memory for face recognition. IEEE Trans. on Neural Network 16(1), 275–278 (2005)
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© 2006 Springer-Verlag Berlin Heidelberg
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Hiremath, P.S., Danti, A. (2006). Combining Geometric and Gabor Features for Face Recognition. In: Narayanan, P.J., Nayar, S.K., Shum, HY. (eds) Computer Vision – ACCV 2006. ACCV 2006. Lecture Notes in Computer Science, vol 3851. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11612032_15
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DOI: https://doi.org/10.1007/11612032_15
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-31219-2
Online ISBN: 978-3-540-32433-1
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