Artificial Intelligence and Computer Vision Based Technique for Effective Facial Recognition System

In recent years, face recognition has risen in prominence due to its numerous practical uses. Face recognition is a challenging field in computer vision, requiring finding and identifying faces in photos. Although significant progress has been made in using facial recognition and detection techniques for authentication purposes, including security and identity verification, some lingering issues still need to be addressed to reach outstanding precision on par with human-level performance. As a result, the AICV-FRS system, that combines artificial intelligence (AI) with computer vision assistance, has been proposed in this work. Due to the prominent portrayal, the developed system uses deep neural networks (DNN) to extract features from the images it gets from cameras. Conversely, facial expressions, occlusions, position, and lighting can alter well with the DNNs executing deep facial recognition. The traditional feature-based cascade classifiers can be used for face detection. The properties of facial recognition may be effectively extracted from the virtual facial data, and greater facial recognition precision can be reached using the suggested DNN. Compared to the comparative approaches, the proposed model has superior precision, accuracy, error rates and processing time.