An Efficient Method for Detecting Covered Face Scenarios in ATM Surveillance Camera

Abstract

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.

Face mask detection using deep learning on NVIDIA Jetson Nano

“…Sikandar et al [15] suggested a technique to detect masked faces from automated teller machine (ATM) monitoring security cameras correctly, and the detection rate was 96.48%. Rao et al [16] proposed CNN model for facial detection that had an accuracy of 91.21% while scanning the public without a facemask.…”

A novel design and implementation of IoT based real-time ATM surveillance and security system

“…The person's movement inside the ATM with covered faces like a helmet and face mask affects surveillance. The detection of the covered faces inside the ATM helps in suspicious activity detection and enables security using the Noise error detection techniques (Sikandar, et al 2020). Suspicious activity detection with Convolutional Neural Networks (CNN) gives much better results (Parab, et al 2020).…”