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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.

This paper presents two gait authentication features based on geometric shape for gait analysis. Specifically, triangle and trapezoid based features are proposed for gait authentication. The features are based on the geometric pattern extracted from a particular gait cycle of a gait model. These features use four points from hip-knee-toe joints and construct a triangle and a right trapezoid. The area of the triangle and trapezoid are calculated using geometric formula as well as image processing methods. Later two areas are compared to validate the model free approach. The results show that, the proposed feature can be used as the features in model free gait analysis.

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