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Human Face Detection Improvement Using Incremental Learning Based on Low Variance Directions

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Advanced Concepts for Intelligent Vision Systems (ACIVS 2017)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 10617))

Abstract

Systems that rely on Face Detection have gained great importance ever, since large-scale databases of thousands of face images are collected from several sources. Thus, the use of an outperforming face detector becomes a challenging problem. Different classification models have been studied and applied for face detection. However, such models involve large scale datasets, which requires huge memory and enormous amount of training time. Therefore, in this paper, we investigate the potency of incrementally projecting data in low variance directions. In fact, in one-class classification, the low variance directions in the training data carry crucial information to build a good model of the target class. On the other hand, incremental learning is known to be powerful, when dealing with dynamic data. We performed extensive tests on human faces, and comparative experiments have been carried out to show the effectiveness and superiority of our proposed method over other face detection methods.

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References

  1. 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 

  2. Hamdy, M.K., Berbar, M.A., Kandeel A.A.: Faces and facial features detection in color images. In: Geometric Modeling and Imaging-New Trends, pp. 209–214 (2006)

    Google Scholar 

  3. Hatem, H., Beiji, Z., Majeed, R.: A survey of feature base methods for human face detection. Int. J. Control Autom. 8, 61–78 (2015)

    Article  Google Scholar 

  4. Bakshi, U., Singhal, R.: A survey on face detection methods and feature extraction techniques of face recognition. Int. J. Emerging Trends Technol. Comput. Sci. 3, 233–37 (2014)

    Google Scholar 

  5. Fang, J., Qiu, G.: Learning sample subspace with application to face detection. In: 17th International Conference on Pattern Recognition, ICPR 2004, Cambridge, UK, 23–26 August 2004, pp. 423–426 (2004)

    Google Scholar 

  6. Feraud, R., Bernier, O., Viallet, J., Collobert, M.: A fast and accurate face detector based on neural networks. IEEE Trans. Pattern Anal. Mach. Intell. 23, 42–53 (2002)

    Article  Google Scholar 

  7. Li, S., Zhang, Z.: Floatboost learning and statistical face detection. IEEE Trans. Pattern Anal. Mach. Intell. 26, 1112–1123 (2004)

    Article  Google Scholar 

  8. Jin, H.L., Liu, Q.S., Lu, H.Q.: Face detection using one-class-based support vectors. In: Sixth IEEE International Conference on Automatic Face and Gesture Recognition, pp. 457–462 (2004)

    Google Scholar 

  9. Christopher, A.W., Xiuwen, L.: Face detection using spectral histograms and SVMs. IEEE Trans. Syst. Man Cybern. - Part B: Cybern. 35(3), 467–476 (2005)

    Article  Google Scholar 

  10. Vapnik, V.: Statistical Learning Theory. Wiley, Hoboken (1998)

    MATH  Google Scholar 

  11. Laskov, P., Gehl, C.: Incremental support vector learning: analysis, implementation and applications. J. Mach. Learn. Res. 7, 1909–1936 (2006)

    MathSciNet  MATH  Google Scholar 

  12. Khan, N.M., Ksantini, R., Ahmad, I.S., Guan, L.: Covariance-guided one-class support vector machine. Pattern Recogn. 47, 2165–2177 (2014)

    Article  Google Scholar 

  13. Kefi, T., Ksantini, R., Bécha Kaâniche, M., Bouhoula, A.: A novel incremental covariance-guided one-class support vector machine. In: Frasconi, P., Landwehr, N., Manco, G., Vreeken, J. (eds.) ECML PKDD 2016. LNCS, vol. 9852, pp. 17–32. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46227-1_2

    Chapter  Google Scholar 

  14. Tax, D.M.J., Müller, K.-R.: Feature extraction for one-class classification. In: Artificial Neural Networks and Neural Information Processing, pp. 342–349. IEEE Press (2003)

    Google Scholar 

  15. http://cbcl.mit.edu/software-datasets/FaceData.html

  16. Davy, M., Desorby, F., Gretton, A., Doncarli, C.: An online support vector machine for abnormal events detection. Sig. Process. 86, 2009–2025 (2005)

    Article  MATH  Google Scholar 

  17. Hua, X., Ding, S.: Incremental learning algorithm for support vector data description. J. Softw. 6, 1166–1173 (2011)

    Article  Google Scholar 

  18. Myint, H.O., Meesad, P.: Incremental learning algorithm based on support vector machine with mahalanobis distance (ISVMM) for intrusion prevention. In: International Conference on Electrical Engineering/Electronics, Computer, Telecommunications, and Information Technology, vol. 2, pp. 630–633 (2009)

    Google Scholar 

  19. Pauwels, E.J., Ambekar, O.: One class classification for anomaly detection: support vector data description revisited. In: Perner, P. (ed.) ICDM 2011. LNCS, vol. 6870, pp. 25–39. Springer, Heidelberg (2011). https://doi.org/10.1007/978-3-642-23184-1_3

    Chapter  Google Scholar 

  20. Chang, W.C., Lee, C.P., Lin, C.J.: A revisit to support vector data description (2015)

    Google Scholar 

  21. Tax, D.M.J.: DDtools, the Data Description Toolbox for Matlab, version 2.1.2 (2015)

    Google Scholar 

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Correspondence to Takoua Kefi .

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Kefi, T., Ksantini, R., Kaâniche, M.B., Bouhoula, A. (2017). Human Face Detection Improvement Using Incremental Learning Based on Low Variance Directions. In: Blanc-Talon, J., Penne, R., Philips, W., Popescu, D., Scheunders, P. (eds) Advanced Concepts for Intelligent Vision Systems. ACIVS 2017. Lecture Notes in Computer Science(), vol 10617. Springer, Cham. https://doi.org/10.1007/978-3-319-70353-4_15

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  • DOI: https://doi.org/10.1007/978-3-319-70353-4_15

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-70352-7

  • Online ISBN: 978-3-319-70353-4

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