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.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Yang, M.H., Kriegman, D.J., Ahuja, N.: Detecting faces in images: a survey. IEEE Trans. Pattern Anal. Mach. Intell. 24, 34–58 (2002)
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)
Hatem, H., Beiji, Z., Majeed, R.: A survey of feature base methods for human face detection. Int. J. Control Autom. 8, 61–78 (2015)
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)
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)
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)
Li, S., Zhang, Z.: Floatboost learning and statistical face detection. IEEE Trans. Pattern Anal. Mach. Intell. 26, 1112–1123 (2004)
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)
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)
Vapnik, V.: Statistical Learning Theory. Wiley, Hoboken (1998)
Laskov, P., Gehl, C.: Incremental support vector learning: analysis, implementation and applications. J. Mach. Learn. Res. 7, 1909–1936 (2006)
Khan, N.M., Ksantini, R., Ahmad, I.S., Guan, L.: Covariance-guided one-class support vector machine. Pattern Recogn. 47, 2165–2177 (2014)
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
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)
Davy, M., Desorby, F., Gretton, A., Doncarli, C.: An online support vector machine for abnormal events detection. Sig. Process. 86, 2009–2025 (2005)
Hua, X., Ding, S.: Incremental learning algorithm for support vector data description. J. Softw. 6, 1166–1173 (2011)
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)
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
Chang, W.C., Lee, C.P., Lin, C.J.: A revisit to support vector data description (2015)
Tax, D.M.J.: DDtools, the Data Description Toolbox for Matlab, version 2.1.2 (2015)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer International Publishing AG
About this paper
Cite this paper
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
Download citation
DOI: https://doi.org/10.1007/978-3-319-70353-4_15
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-70352-7
Online ISBN: 978-3-319-70353-4
eBook Packages: Computer ScienceComputer Science (R0)