Face Recognizability Evaluation for ATM Applications with Exceptional Occlusion Handling

Abstract

Biometrics has been extensively utilized to lessen the ATM-related crimes. One of the most widely used methods is to capture the facial images of the users for follow-up criminal investigations. However, this method is vulnerable to attacks made by the criminals with heavy facial occlusions. To overcome this drawback, this paper proposes a novel method for face recognizability evaluation with exceptional occlusion handling (EOH). The proposed method conducts a recognizability evaluation based on local regions of the facial components. Subsequently, the resulting decisions are reaffirmed by the EOH exploiting the global aspect of the frequently occurring facial occlusions. The EOH can be divided into two separate approaches: 1) accepting the falsely rejected cases, 2) rejecting the falsely accepted cases. In this paper, two typical facial occlusions, eyeglasses and sunglasses, are chosen to prove the validity of the EOH. To evaluate the proposed method in the most realistic environment, an ATM database was constructed by using an off-the-shelf ATM while the users were asked to make withdrawals as they would in real situations. The proposed method was evaluated by the ATM database which includes 480 video sequences with 20 subjects. The results showed the feasibility of the face recognizability evaluation with the EOH in practical ATM environments.