REAL-TIME FACE DETECTION AND SECURITY SYSTEM FOR ATM MACHINE

Information Technology

Face recognition play a vital role in variety of applications from biometrics, surveil-lance, security, identification to the authentication. In this paper we design and implement a Bank locker security system where access people whose faces are available in the training database. First, we are going to detect the face by de- testing the human motion. Then face recognition is performed to determine the authority of the person to enter the sensitive area. At the same time, we track the coordinate of detected motion. Failing to recognize the face finally passes the estimated coordinate to anesthetic gun for targeting the intruder automatically. Experimental results demonstrate the effectiveness of proposed Bank locker security system in order to restrict the unauthorized access and enhanced reliability by use of Liveness face recognition. Human face detection is the most promising field of image processing that hasa vast area of research oriented real life applications. In the real world the concept is widely used for the content annotation, access control, profiling and potential discrimination in the web world.. It has been extensively adopted by the applications including biometrics, surveillance, security, identification, and authentication. Face recognition usually exploit high-dimensional information which makes it computationally intensive. Face plays a big role in conveying identity and emotion, being the primary focusof attention in social life. Also, familiar faces can be identified at a glance even after years of separation. Computational model of face recognition can be applied to criminal identification, security systems, image and film processing and human computer interaction. The Psychological process of locating the human face in the visual frame is also possible. It is also categorized as a special case of object class detection. This project introduces an innovative solution to enhance ATM security using machine learning technology. By implementing a real-time face detection and recognition system, we aim to significantly reduce the risk of unauthorized access and fraudulent activities at ATMs.

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