A new video surveillance system employing occluded face detection

  • Authors:

  • Jaywoo Kim;Younghun Sung;Sang Min Yoon;Bo Gun Park

  • Affiliations:

  • Interaction Lab. Samsung Advanced Institute of Technology, Suwon, Rep. of Korea;Interaction Lab. Samsung Advanced Institute of Technology, Suwon, Rep. of Korea;Computing Lab. Samsung Advanced Institute of Technology, Suwon, Rep.of Korea;Automation & Systems Research Institute, Seoul National University, Seoul, Rep. of Korea

  • Venue:
  • IEA/AIE'2005 Proceedings of the 18th international conference on Innovations in Applied Artificial Intelligence
  • Year:
  • 2005

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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.