<![CDATA[Face detection is an essential part of many applications, such as security systems, social networking platforms, and human-computer interaction. In order to detect human faces, this work investigates the application of the Viola-Jones algorithm in a graphical user interface (GUI) system created with Matlab. The Viola-Jones algorithm is a cutting-edge real-time face detection technique that uses AdaBoost learning to choose the most important features, Haar-like features, and an integral picture for quick feature computation. Fifteen randomly chosen photos from the internet with both single and numerous faces were used to test the system. The algorithm's efficacy in face detection is demonstrated by the results, which show an average accuracy of 89.86%. Nevertheless, other restrictions were noted, such as blocked faces, non-frontal facial angles, and subpar identification in dimly lit environments. These difficulties draw attention to how outside variables affect detection accuracy and point to possible areas for improvement, such using sophisticated preprocessing techniques or combining the algorithm with cutting-edge machine learning approaches. This study highlights the need for more research to increase the Viola-Jones algorithm's robustness in a variety of complicated circumstances while reaffirming its applicability.]]>
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