Abstract
In this paper we propose a novel boosting based classification algorithm, SODA-Boosting (where SODA stands for Second Order Discriminant Analysis). Unlike the conventional AdaBoost based algorithms widely applied in computer vision, SODA-Boosting does not involve time consuming procedures to search a huge feature pool in every iteration during the training stage. Instead, in each iteration SODA-Boosting efficiently computes discriminative weak classifiers in closed-form, based on reasonable hypotheses on the distribution of the weighted training samples. As an application, SODA-Boosting is employed for image based gender recognition. Experimental results on publicly available FERET database are reported. The proposed algorithm achieved accuracy comparable to state-of-the-art approaches, and demonstrated superior performance to relevant boosting based algorithms.
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References
Golomb, B.A., Lawrence, D.T., Sejnowski, T.J.: Sexnet: A neural network identifies sex from human faces. In: NIPS-3: Proceedings of the 1990 conference on Advances in neural information processing systems 3, pp. 572–577. Morgan Kaufmann Publishers Inc., San Francisco, CA, USA (1990)
Cottrell, G.W., Metcalfe, J.: Empath: face, emotion, and gender recognition using holons. In: NIPS-3: Proceedings of the 1990 conference on Advances in neural information processing systems 3, pp. 564–571. Morgan Kaufmann Publishers Inc., San Francisco, CA, USA (1990)
Tamura, S., Kawai, H., Mitsumoto, H.: Male/female identification from 86 very low resolution face images by neural network. Pattern Recognition 29(2), 331–335 (1996)
Gutta, S., Huang, J.R.J., Phillips, P.J., Wechsler, H.: Mixture of experts for classification of gender, ethnic origin, and pose of human faces. IEEE Trans. Neural Networks 11, 948–960 (2000)
Moghaddam, B., Yang, M.: Learning gender with support faces. IEEE Transactions on Pattern Analysis and Machine Intelligence 24(5), 707–711 (2002)
Graf, A.B.A., Wichmann, F.A.: Gender classification of human faces. In: Bülthoff, H.H., Lee, S.-W., Poggio, T.A., Wallraven, C. (eds.) BMCV 2002. LNCS, vol. 2525, pp. 491–500. Springer, Heidelberg (2002)
Shakhnarovich, G., Viola, P.A., Moghaddam, B.: A unified learning framework for real time face detection and classification. In: FGR 2002. Proceedings of the Fifth IEEE International Conference on Automatic Face and Gesture Recognition, p. 16. IEEE Computer Society, Washington, DC, USA (2002)
Wu, B., Ai, H., Huang, C.: Lut-based adaboost for gender classification. In: Kittler, J., Nixon, M.S. (eds.) AVBPA 2003. LNCS, vol. 2688, pp. 104–110. Springer, Heidelberg (2003)
Baluja, S., Rowley, H.: Boosting sex identification performance. In: AAAI-IAAI 2005 (2005)
Schapire, R.E., Freund, Y., Bartlett, P., Lee, W.S.: Boosting the margin: a new explanation for the effectiveness of voting methods. In: Proc. 14th International Conference on Machine Learning, pp. 322–330. Morgan Kaufmann, San Francisco (1997)
Tu, J., Zhang, Z., Zeng, Z., Huang, T.: Face localization via hierarchical condensation with fisher boosting feature selection. In: CVPR 2004. IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 02, pp. 719–724 (2004)
Xu, X., Huang, T.S.: Face recognition with MRC-Boosting. In: ICCV 2005. 10th IEEE International Conference on Computer Vision, vol. 2, pp. 1770–1777 (2005)
Phillips, P.J., Moon, H., Rizvi, S.A., Rauss, P.J.: The feret evaluation methodology for face-recognition algorithms. IEEE Trans. Pattern Anal. Mach. Intell. 22(10), 1090–1104 (2000)
Viola, P., Jones, M.J.: Robust real-time object detection. In: IEEE Workshop on Statistical and Theories of Computer Vision, IEEE Computer Society Press, Los Alamitos (2001)
Duda, R.O., Hart, P.E., Stork, D.G.: Pattern Classification, 2nd edn. Wiley-Interscience, Chichester (2000)
Elad, M., Hel-Or, Y., Keshet, R.: Pattern detection using a maximal rejection classifier. Pattern Recognition Letters 23(12), 1459–1471 (2002)
Liu, C., Shum, H.Y.: Kullback-leibler boosting. In: CVPR 2003. IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 01, p. 587. IEEE Computer Society Press, Los Alamitos (2003)
Joachims, T.: Making large-Scale SVM Learning Practical. In: Advances in Kernel Methods - Support Vector Learning, MIT Press, Cambridge (1999)
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Xu, X., Huang, T.S. (2007). SODA-Boosting and Its Application to Gender Recognition. In: Zhou, S.K., Zhao, W., Tang, X., Gong, S. (eds) Analysis and Modeling of Faces and Gestures. AMFG 2007. Lecture Notes in Computer Science, vol 4778. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-75690-3_15
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DOI: https://doi.org/10.1007/978-3-540-75690-3_15
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