Face Recognition System: A Computer Vision Approach for Facial Identification and Verification

This paper, titled “Face Recognition System: A Computer Vision Approach for Facial Identification and Verification,” presents a conceptual design for a computer vision-based system that identifies or verifies a person by analyzing facial characteristics in an image or video frame. The proposed system follows a modular workflow consisting of image acquisition, face detection, face alignment, feature extraction, template matching, and result display. A face embedding model converts a detected face into a numerical representation, which is compared with authorized reference templates using a similarity measure such as cosine similarity or Euclidean distance. A validation-selected threshold determines whether the system reports a potential match, no match, or an uncertain result. The paper discusses verification and identification modes, dataset preparation, model configuration, implementation components, and evaluation methods. Potential implementation technologies include Python, OpenCV, Flask or FastAPI, and protected database or encrypted storage for biometric templates. Evaluation measures may include verification accuracy, false acceptance rate, false rejection rate, precision, recall, and response time. The proposed framework emphasizes informed consent, secure biometric data handling, access control, limited data retention, privacy protection, and human review for uncertain or consequential decisions. It also considers limitations caused by lighting, pose, image quality, occlusion, demographic variation, and presentation attacks. This paper presents a conceptual system design rather than verified experimental findings. The implementation details, dataset sources, matching threshold, and numerical performance results must be established through actual implementation and documented testing before the system's effectiveness can be claimed.