Facial Recognition Based Intruder Detection System for Enhanced Security

Security systems serve the major functions of protection and safety. Home security is a major predicament in today’s world. It is proven that facial recognition, which is a relatively new technology, is the most efficient way of providing security. The high-end security systems used today are very expensive and use biometrics like fingerprint or iris scanners. These systems require additional equipment for recognition which can be replaced by a simple camera while using facial recognition. Our project is aimed at creating an efficient security system incorporating the concepts of Artificial Intelligence (AI) and Machine Learning (ML) to implement facial recognition. This investigation focuses on the examination and experimentation of the Haar cascade classifier technique for facial recognition. The study additionally rationalizes the selection of the algorithm and furnishes an account of how the system’s implementation, utilized for the analysis, was carried out. Throughout the study, the developed system underwent testing using a set of facial photographs that encompassed a variety of attributes, including differing distances from the camera, diverse lighting conditions, and varied facial orientations within the camera’s field of view. A thorough evaluation of the test outcomes was performed, leading to conclusions that shed light on the essential considerations in the design and application of facial recognition systems, aiming for optimal accuracy.