Abstract
This paper proposes a hybrid method for face recognition using local features and statistical feature extraction methods. First, a dense set of local feature points are extracted in order to represent a facial image. Each local feature point is described by the keypoint descriptor defined by SIFT feature. Then, the statistical feature extraction methods, PCA and LDA, are applied to the set of local feature descriptors in order to find low dimensional features. With the obtained low dimensional feature vectors, we can conduct face recognition task efficiently using a simple classifier. Through computational experiments on benchmark data sets, we show that the proposed method is superior to the conventional PCA and LDA in the classification performance. In addition, we also show that the proposed method can achieve remarkable improvement in the processing time compared to the conventional keypoint matching methods proposed for local features.
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References
Zhao, W., Chellappa, R., Phillips, P.J., Rosenfeld, A.: Face recognition: A literature survey. ACM Comput. Surv. 35(4), 399–458 (2003)
Turk, M., Pentland, A.: Eigenfaces for recognition. Journal of Cognitive Neuro-Science 3(1), 71–86 (1991)
Belhumeur, P., Hespanha, J., Kriegman, D.: Eigenfaces vs. fisherfaces: Recognition using class specific linear projection. In: Buxton, B.F., Cipolla, R. (eds.) ECCV 1996. LNCS, vol. 1065, pp. 43–58. Springer, Heidelberg (1996)
Lowe, D.G.: Distinctive image features from Scale-Invariant keypoints. Int. J. Comput. Vision 60(2), 91–110 (2004)
Bicego, M., Lagorio, A., Grosso, E., Tistarelli, M.: On the use of SIFT features for face authentication. In: Proceedings of the 2006 Conference on Computer Vision and Pattern Recognition Workshop, p. 35. IEEE Computer Society, Los Alamitos (2006)
Dalai, N., Triggs, B., Rhone-Alps, I., Montbonnot, F.: Histograms of oriented gra- dients for human detection. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2005, vol. 1 (2005)
Dreuw, P., Steingrube, P., Hanselmann, H., Ney, H., Aachen, G.: SURF-Face: face recognition under viewpoint consistency constraints. In: British Machine Vision Conference, London, UK. (2009)
Bosch, A., Zisserman, A., Munoz, X.: Scene classification via pLSA. In: Leonardis, A., Bischof, H., Pinz, A. (eds.) ECCV 2006. LNCS, vol. 3954, pp. 517–530. Springer, Heidelberg (2006)
Fulkerson, B., Vedaldi, A., Soatto, S.: Localizing objects with smart dictionaries. In: Forsyth, D., Torr, P., Zisserman, A. (eds.) ECCV 2008, Part I. LNCS, vol. 5302, pp. 179–192. Springer, Heidelberg (2008)
Martinez, A.M., Kak, A.C.: PCA versus LDA. IEEE Trans. Pattern Anal. Mach. Intell. 23(2), 228–233 (2001)
Martinez, A., Benavente, R.: The AR face database. CVC Technical Report #24 (June 1998)
Vedaldi, A., Fulkerson, B.: VLFeat: An open and portable library of computer vision algorithms (2008)
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Kim, D., Park, H. (2010). An Efficient Face Recognition through Combining Local Features and Statistical Feature Extraction. In: Zhang, BT., Orgun, M.A. (eds) PRICAI 2010: Trends in Artificial Intelligence. PRICAI 2010. Lecture Notes in Computer Science(), vol 6230. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-15246-7_42
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DOI: https://doi.org/10.1007/978-3-642-15246-7_42
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