<![CDATA[Personal data recording through facial recognition is a modern solution for individual identification; however, the main challenge lies in the accuracy and reliability of the system under various conditions. This study examines the implementation of machine learning as a solution, utilizing video and photo data for face detection and recognition. The study’s goal is to evaluate the effectiveness of facial image recognition by combining several methods, aiming for practical application across diverse settings, such as offices and schools. The methodology includes segmentation testing for edge detection, feature extraction, and real-time recognition. The system was developed using Eigenface, Support Vector Machine, and Viola-Jones methods, trained over 20 sessions. The results indicate that the system can recognize faces under both daytime and nighttime conditions, achieving 87% accuracy during the day and 81% at night. These findings make a significant contribution to the development of security systems based on facial recognition and emphasize the potential of this technology to enhance personal data security across various contexts]]>
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