<![CDATA[Face detection and recognition in images and videos is a widely studied topic in the field of biometrics, with increasing importance in security and surveillance applications. This paper presents the development of a real-time face recognition system designed to enhance security and automation. The system utilizes a Haar-cascade classifier for initial face detection and the Local Binary Pattern Histogram (LBPH) algorithm for face recognition, based on a locally generated training dataset. It operates in two main stages: detecting human faces and identifying individuals. In cases where an unrecognized face is detected, the system sends an immediate alert via the LINE application. Key components of the system include real-time processing, identity verification, and access control. The proposed system shows strong potential for practical deployment in areas such as crowd monitoring and personal security in sensitive environments like airports. Experimental results demonstrate a recognition accuracy ranging from 90% to 93.45%, validating the effectiveness of the approach.]]>
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