2771-1463ISSN (Online)
2771-1471ISSN (Print)
Volume 10 , Issue 1 , PP: 33-38, 2025 | Cite this article as | XML | Html | PDF | Full Length Article
S. Hemamalini 1 * , J. Beryl Sharon 2 , M. Dharshini 3 , M. Indu 4
Doi: https://doi.org/10.54216/JCHCI.100104
Face recognition technology is increasingly integrated into daily life, from unlocking smartphones to taking attendance in classrooms, despite challenges like lighting, occlusion, and posture variety in real-world scenarios. Therefore, this study aims to develop an Automated Face Recognition System for Data Retrieval and Management using OpenCV. Using a camera, the system records users' photos in real time. Computer vision techniques are then applied, particularly the face identification and recognition functions of the Local Binary Pattern Histogram (LBPH) and the Haar Cascade algorithm, which are implemented using OpenCV. The system correctly recognizes people and makes it easier to handle student information by comparing the faces it detects with a database of photographs of students that has been stored. Improved face recognition accuracy, real-time data retrieval, and efficient data management procedures are the main goals. Although the system performed satisfactorily in normal lighting, difficulties with low light were shown to affect the accuracy of detection and recognition. The primary causes of these constraints were changes in the quality of the camera and lighting. Subsequent developments will concentrate on optimizing the accuracy and overall performance of the system, maybe by incorporating better cameras and more sophisticated processing. The study highlights how computer vision and facial recognition technology can revolutionize data management procedures in a variety of applications. In conclusion, the suggested system effectively makes use of cutting-edge methods for dependable and effective data retrieval.
Haar Cascade, OpenCV , Automated Face Recognition , Data Retrieval , Local Binary Pattern Histogram (LBPH) , Computer Vision
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