Abstract
Naive Bayesian is a simple and powerful classification algorithm. In this paper, we propose an optimized naive Bayesian algorithm with the application to face recognition. Firstly, the algorithm estimates the probability distribution of each pixel at each gray level. Secondly, it performs Laplace smoothing to resolve the zero probability problem. Thirdly, the maximum filtering is used to optimize the probability distribution matrix for classification. Experiments on three face databases show that the proposed algorithm is effective and performs better than some state-of-the-art algorithms.
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References
Leung, K.M.: Naive bayesian classifier. Polytechnic University Department of Comptuter Science Finance and Risk Engineering (2007)
Maxwell, J.C.: A Treatise on Electricity and Magnetism, 3rd edn., vol. 2, pp. 68–73. Oxford, Clarendon (1892)
Yager, R.R.: An extension of the naive Bayes classifier. J. Inf. Sci. 176(5), 577–588 (2006)
Zhang, L., Zhu, J., Yao, T.: An evaluation of statistical spam filtering techniques. J. ACM Trans. Asian Lang. Inf. Process. 3(4), 243–269 (2004)
Bledsoe, W.W.: The model method in facial recognition. Panoramic Research, Inc., Palo Alto (1966)
Moghaddam, B., Jebara, T., Pentland, A.: Bayesian face recognition. J. Pattern Recogn. 33(11), 1771–1782 (2000)
Wang, X., Tang, X.: Bayesian face recognition using Gabor features. In: Proceedings of ACM SIGMM Workshop on Biometric Methods and Applications, pp. 70–73 (2003)
Chen, D., Cao, X., Wang, L., Wen, F., Sun, J.: Bayesian face revisited: a joint formulation. In: Fitzgibbon, A., Lazebnik, S., Perona, P., Sato, Y., Schmid, C. (eds.) ECCV 2012. LNCS, vol. 7574, pp. 566–579. Springer, Heidelberg (2012). doi:10.1007/978-3-642-33712-3_41
Lyu, S.: Bayesian supervised learning with non-Gaussian latent variables. In: ChinaSIP, pp. 659–663 (2013)
Hafilizara, M.: Metode Smoothing dalam Naïve Bayes untuk Klasifikasi Email Spam. UT - Computer Science (2014)
Wright, J., Ma, Y., Mairal, J.: Sparse representation for computer vision and pattern recognition. J. Proc. IEEE 98(6), 1031–1044 (2010)
Shi, Q., Eriksson, A., Hengel, A.: Is face recognition really a compressive sensing problem? In: CVPR, pp. 553–560 (2011)
Zhang, L., Yang, M., Feng, X.: Sparse representation or collaborative representation: which helps face recognition?. In: ICCV, pp. 471–478 (2011)
Debruyne, M., Verdonck, T.: Robust kernel principal component analysis and classification. J. Adv. Data Anal. Classif. 4(2–3), 151–167 (2010)
Xu, Y., Zhang, D.: Represent and fuse bimodal biometric images at the feature level: complex-matrix-based fusion scheme. Opt. Eng. 49, 037002 (2010)
The Database of Faces. http://www.cl.cam.ac.uk/research/dtg/attarchive/facedatabase.html
The Georgia Tech (GT) face databases: http://www.anefian.com/research/face_reco.htm
http://cobweb.ecn.purdue.edu/∼aleix/aleix−face−DB.html
Acknowledgment
This work was supported by the National Natural Science Foundation of China under Grant 61300208.
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Yan, R., Wen, J., Cao, J., Xu, Y., Yang, J. (2017). An Optimized Naive Bayesian Method for Face Recognition. In: Sun, F., Liu, H., Hu, D. (eds) Cognitive Systems and Signal Processing. ICCSIP 2016. Communications in Computer and Information Science, vol 710. Springer, Singapore. https://doi.org/10.1007/978-981-10-5230-9_14
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DOI: https://doi.org/10.1007/978-981-10-5230-9_14
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