<![CDATA[Lighting quality is a crucial factor that affects the performance of camera-based face detection systems, especially in CCTV surveillance systems that operate in low or uneven lighting conditions. This study aims to analyze the performance of illumination-invariant preprocessing methods in improving the accuracy of human face detection under various lighting conditions. Three preprocessing approaches were compared, namely Histogram Equalization (HE), Gamma Correction (GC), and a hybrid method that combines both (GC+HE). The dataset used consists of 1415 human face images taken using a webcam with variations in five lighting conditions, four face directions, and three shooting distances. All images were processed using the Haar Cascade Classifier algorithm as the face detection method. Performance evaluation was conducted using accuracy, precision, recall, and confusion matrix analysis metrics. The test results showed that the hybrid method provided the best performance with a precision of 92.79%, accuracy of 87.49%, and recall of 89.61%, compared to the HE and GC methods used individually. This improvement indicates that the combination of lighting normalization and contrast enhancement can produce more stable and informative facial images for the detection process. The findings of this study indicate that the hybrid-based illumination invariant approach is very effective for application in real-time visual surveillance systems, especially in environments with limited lighting.]]>
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