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Combining Classifiers for Robust Face Detection

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Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 3972))

Abstract

In this paper, we propose a face detection method by combining classifiers. We apply two classifiers using features extracted from complementary feature subspaces learned by principal component analysis (PCA). The two classifiers employ the same classification model named a polynomial neural network (PNN). The outputs of the two classifiers are fused to make the final decision. The effectiveness of the proposed method has been demonstrated in experimentals.

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References

  • Maio, D., Maltoni, D.: Real-Time Face Location on Gray-Level Static Images. Pattern Recognition 33, 1525–1539 (2000)

    Article  Google Scholar 

  • Liu, C.: A Bayesian Discriminating Features Method for Face Detection. IEEE Trans. Pattern Anal. Mach. Intell. 25, 725–740 (2003)

    Article  Google Scholar 

  • Rowley, H.A., Baluja, S., Kanade, T.: Neural Network-Based Face Detection. IEEE Trans. Pattern Anal. Mach. Intell. 20, 23–38 (1998)

    Article  Google Scholar 

  • Yang, M.H., Kriegman, D.J., Ahuja, N.: Detecting Faces in Images: A Survey. IEEE Trans. Pattern Anal. Mach. Intell. 24, 34–58 (2002)

    Article  Google Scholar 

  • Kittler, J., Duin, P.W., Matas, J.: On Combining Classifiers. IEEE Trans. Pattern Anal. Mach. Intell. 20, 226–239 (1998)

    Article  Google Scholar 

  • Huang, L.-L., Shimizu, A., Hagihara, Y., Kobatake, H.: Gradient Feature Extraction for Classification-Based Face Detection. Pattern Recognition 36, 2502–2511 (2003)

    Google Scholar 

  • Ali, K.M., Pazzani, M.J.: On the Link Between Error Correlation and Error Reduction in Decision Tree Ensembles. Technical Report, ICS-UCI, 95–138 (1995)

    Google Scholar 

  • Schürmann, J.: Pattern Classification: A Unified View of Statistical Pattern Recognition and Neural Networks. Wiley Interscience, Hoboken (1996)

    Google Scholar 

  • Robbins, H., Monro, S.: A Stochastic Approximation Method. Annals of Mathematics and Statistics 22, 400–407 (1951)

    Article  MATH  MathSciNet  Google Scholar 

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© 2006 Springer-Verlag Berlin Heidelberg

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Huang, LL., Shimizu, A. (2006). Combining Classifiers for Robust Face Detection. In: Wang, J., Yi, Z., Zurada, J.M., Lu, BL., Yin, H. (eds) Advances in Neural Networks - ISNN 2006. ISNN 2006. Lecture Notes in Computer Science, vol 3972. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11760023_18

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  • DOI: https://doi.org/10.1007/11760023_18

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-34437-7

  • Online ISBN: 978-3-540-34438-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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