<![CDATA[Recognition technology with Raspberry Pi to transform attendance management practices in educational institutions and workplaces. By harnessing advanced technologies like the Haar Cascade Classifier and Local Binary Patterns (LBP) algorithm, the system exhibits strong performance in accurately detecting and identifying faces across diverse environmental settings. Through rigorous experimental evaluation, the system achieves its highest accuracy in the distance comparison test at 30 cm, with an average accuracy of 92.4%. Similarly, it demonstrates optimal performance in the light comparison test at 100 lux, achieving an average accuracy of 91.3%. These results underscore the system's effectiveness in identifying faces in close proximity and under suitable lighting conditions. Overall, the proposed system offers a promising solution for optimizing attendance management processes while mitigating the shortcomings of traditional recording methods. By providing a reliable and efficient means of tracking attendance, it lays a solid groundwork for enhancing productivity and outcomes in both educational and professional settings.]]>
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