An efficient method for detecting covered face scenarios in ATM surveillance camera
Covering face with accessories such as mask, scarf and sunglass is a common criminal activity in automated teller machine (ATM) robbery. Therefore, detection of covered face using ATM surveillance camera can be an effective solution to reduce robbery or crime. This paper presents a novel method to d...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | en |
| Published: |
Springer
2020
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| Subjects: | |
| Online Access: | https://umpir.ump.edu.my/id/eprint/46342/1/An%20efficient%20method%20for%20detecting%20covered%20face%20scenarios.pdf https://doi.org/10.1007/s42979-020-00163-6 https://umpir.ump.edu.my/id/eprint/46342/ |
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| Summary: | Covering face with accessories such as mask, scarf and sunglass is a common criminal activity in automated teller machine (ATM) robbery. Therefore, detection of covered face using ATM surveillance camera can be an effective solution to reduce robbery or crime. This paper presents a novel method to detect covered face from ATM surveillance camera images. Specifically, three facial features, i.e., skin color, elliptical face shape and facial width-to-height ratio (fWHR), incorporated with geometrical property of ellipse have been employed to estimate the covered region. In addition, three parameters, i.e., facial area, fWHR and covered area percentage, have been utilized for reliable classification. Experiment results demonstrate that the method can detect full covered, uncovered and partially covered faces at a correct detection rate of 98.3%, 93.3% and 97.78%, respectively. The overall correct detection rate is 96.48%, which is found to be better than previous studies. Also, the proposed method can handle faces covered with few new face hiding objects such as hijab, niqab and robber’s ski mask. Furthermore, processing time of the proposed algorithm is significantly improved while it is compared to the existing methods. The detection time varies between 31 and 67 ms which is equivalent to 15–32 frames per second. |
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