Abstract
We present an example-based learning approach for detecting a partially occluded human face in a scene provided by a camera of Automated Teller Machine (ATM) in a bank. Gradient mapping in scale space is applied on an original image, providing human face representation robust to illumination variance. Detection of the partially occluded face, which can be used in characterization of suspicious ATM users, is then performed based on Support Vector Machine (SVM) method. Experimental results show that a high detection rate over 95% is achieved in image samples acquired from in-use ATM.
Access provided by Autonomous University of Puebla. Download to read the full chapter text
Chapter PDF
Similar content being viewed by others
Keywords
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
References
Jain, A.K.: Fundamentals of Digital Image Processing, pp. 348–351. Prentice Hall, New Jersey (1989)
Davis, L.S.: A Survey of Edge Detection Techniques. Computer Graphics and Image Processing 4, 248–270 (1975)
Frei, W., Chen, C.C.: Fast Boundary Detection: a Generalization and a New Algorithm. IEEE Trans. Computer 26(2) (1977)
Yoon, S.M., Kee, S.C.: Detection of Partially Occluded Face using Support Vector Machines. In: MVA, pp. 546–549 (2002)
Martinez, A.M.: Recognition of Partially Occluded and/or Imprecisely Localized Faces using a Probabilistic Approach. CVPR 1, 712–717 (2000)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2005 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Kim, J., Sung, Y., Yoon, S.M., Park, B.G. (2005). A New Video Surveillance System Employing Occluded Face Detection. In: Ali, M., Esposito, F. (eds) Innovations in Applied Artificial Intelligence. IEA/AIE 2005. Lecture Notes in Computer Science(), vol 3533. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11504894_10
Download citation
DOI: https://doi.org/10.1007/11504894_10
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-26551-1
Online ISBN: 978-3-540-31893-4
eBook Packages: Computer ScienceComputer Science (R0)