DEVELOPMENT OF A BIOMETRIC SECURITY SYSTEM FOR ACCESS CONTROL USING FACE RECOGNITION BASED ON EMGUCV

This research details the design and implementation of a biometric security system for automated access control utilizing facial recognition technology. Traditional security mechanisms, such as physical keys, identification cards, and passwords, are increasingly vulnerable to theft, loss, or sophisticated forgery. To address these limitations, this study leverages EmguCV, a cross-platform .NET wrapper for the Intel-developed OpenCV library, to implement high-performance computer vision algorithms within a managed C# environment. The developed system facilitates a seamless, contactless authentication process, which has become critically important for hygiene in modern organizational infrastructure. The system architecture incorporates a multi-phase workflow: image acquisition via standard camera sensors, face detection using traditional and deep learning models, feature extraction through holistic recognizers, and physical actuation of security barriers via microcontroller integration. Experimental results indicate that the system achieves up to 99% recognition accuracy using Local Binary Pattern Histograms (LBPH) in controlled environments. This research underscores the importance of integrating robust computer vision frameworks with scalable software architectures to enhance identity management in smart building environments.