Recognizability assessment of facial images for automated teller machine applications

  • Authors:

  • Jae Kyu Suhr;Sungmin Eum;Ho Gi Jung;Gen Li;Gahyun Kim;Jaihie Kim

  • Affiliations:

  • Research Institute of Automotive Electronics and Control, Hanyang University, Seoul, Republic of Korea;Biometrics Engineering Research Center, School of Electrical and Electronic Engineering, Yonsei University, Seoul, Republic of Korea;School of Mechanical Engineering, Hanyang University, Seoul, Republic of Korea;Biometrics Engineering Research Center, School of Electrical and Electronic Engineering, Yonsei University, Seoul, Republic of Korea;Biometrics Engineering Research Center, School of Electrical and Electronic Engineering, Yonsei University, Seoul, Republic of Korea;Biometrics Engineering Research Center, School of Electrical and Electronic Engineering, Yonsei University, Seoul, Republic of Korea

  • Venue:
  • Pattern Recognition
  • Year:
  • 2012

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Abstract

Crimes related to automated teller machines (ATMs) have increased as a result of the recent popularity in the devices. One of the most practical approaches for preventing such crimes is the installation of cameras in ATMs to capture the facial images of users for follow-up criminal investigations. However, this approach is vulnerable in cases where a criminal's face is occluded. Therefore, this paper proposes a system which assesses the recognizability of facial images of ATM users to determine whether their faces are severely occluded. The proposed system uses a component-based face candidate generation and verification approach to handle various facial postures and acceptable partial occlusions. Element techniques are implemented via grayscale image-based methods which are robust against illumination conditions compared to skin color detection approach. The system architecture for achieving both high performance and cost-efficiency is proposed to make the system applicable to practical ATM environments. In the experiment, the feasibility of the proposed system was evaluated using a large-scale facial occlusion database consisting of 3168 image sequences including 21 facial occlusions, 8 illumination conditions, and 2 acquisition scenarios. Based on the results, we drew up the guidelines of recognizability assessment systems for ATM applications.