Abstract
In this work, we present a novel approach for face recognition which use boosted statistical local Gabor feature based classifiers. Firstly, two Gabor parts, real part and imaginary part, are extracted for each pixel of face images. The two parts are transformed into two kinds of Gabor features, magnitude feature and phase feature. 40 magnitude Gaborfaces and 40 phase Gaborfaces are generated for each face image by convoluting face images with five scales and eight orientations Gabor filters. Then these Gaborfaces are scanned with a sub-window from which the quantified Gabor features histograms are extracted representing efficiently the face image. The multi-class problem of face recognition is transformed into a two-class one of intra-and extra-class classification using intra-personal and extra-personal images, as in [5]. The intra/extra features are constructed based on these histograms of two different face images with Chi square statistic as dissimilarity measure. A strong classifier is learned using boosting examples, similar to the way in face detection framework [10]. Experiments on FERET database show good results comparable to the best one reported in literature [6].
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References
Bartlett, M.S., Martin Lades, H., Sejnowski, T.J.: Independent component representations for face recognition. In: Proceedings of the SPIE, Conference on Human Vision and Electronic Imaging III, vol. 3299, pp. 528–539 (1998)
Etemad, K., Chellapa, R.: Face recognition using discriminant eigenvectors. In: Proceedings of the International Conference on Acoustic, Speech and Signal Processing (1996)
Freund, Y., Schapire, R.E.: A decision-theoretic generalization of on-line learning and an application to boosting. Journal of Computer and System Sciences 55(1), 119–139 (1997)
Friedman, J., Hastie, T., Tibshirani, R.: Additive logistic regression: a statistical view of boosting. The Annals of Statistics 28(2), 337–374 (2000)
Moghaddam, B., Nastar, C., Pentland, A.: A Bayesain similarity measure for direct image matching. Media Lab Tech Report No. 393, MIT (August 1996)
Jonathon Phillips, P., Moon, H., Rizvi, S.A., Rauss, P.J.: The FERET evaluation methodology for face-recognition algorithms. IEEE Transactions on Pattern Analysis and Machine Intelligence 22(10), 1090–1104 (2000)
Zhang, L., Li, S.Z., Qu, Z.Y., Huang, X.S.: Boosting local feature based classifiers for face recognition. In: The First IEEE Workshop on Face Processing in Video, Washington D.C. (June 2004)
Schapire, R.E., Singer, Y.: Improved boosting algorithms using confidencerated predictions. In: Proceedings of the Eleventh Annual Conference on Computational Learning Theory, pp. 80–91 (1998)
Turk, M.A., Pentland, A.P.: Eigenfaces for recognition. Journal of Cognitive Neuroscience 3(1), 71–86 (1991)
Viola, P., Jones, M.: Robust real time object detection. In: IEEE ICCV Workshop on Statistical and Computational Theories of Vision, Vancouver, Canada, July 13 (2001)
Wiskott, L., Fellous, J.M., Kruger, N., Vonder malsburg, C.: face recognition by elastic bunch graph matching. IEEE Transactions on Pattern Analysis and Machine Intelligence 19(7), 775–779 (1997)
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Huang, X., Wang, Y. (2005). Boosting Statistical Local Feature Based Classifiers for Face Recognition. In: Marques, J.S., Pérez de la Blanca, N., Pina, P. (eds) Pattern Recognition and Image Analysis. IbPRIA 2005. Lecture Notes in Computer Science, vol 3523. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11492542_7
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DOI: https://doi.org/10.1007/11492542_7
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