<![CDATA[Purpose: This research aims to test the performance of eye detection algorithms in human images using specific methods, such as the Viola-Jones algorithm. Eye detection is an important component in various applications, ranging from biometrics to surveillance systems. Methods/Study design/approach: In this study, testing was conducted on 10 images with simple variations to assess the algorithm's accuracy, precision, and recall. Result/Findings: The results showed that the algorithm achieved an accuracy rate of 80% with a precision and recall of 88.9%, which indicates a fairly reliable performance for standard conditions. However, detection errors such as false positives and negatives were found, suggesting the potential for improvement, especially in images with varying illumination or orientation. Novelty/Originality/Value: This algorithm is suitable for real-time eye detection applications with stable lighting and orientation conditions. Still, it is recommended to incorporate additional methods to improve accuracy in more complex conditions.]]>
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