| dc.description.abstract |
Facial recognition technology has become a significant topic in computer vision and artificial intelligence, with applications in various fields such as security, surveillance, and humanmachine interaction. The primary stages in this system include face detection and face tracking. This study proposes the use of Adaptive Histogram Equalization (AHE) to enhance the performance of a realtime cascade classifierbased face detection and tracking system. AHE enhances image contrast by locally adjusting the image histogram, which is expected to address the variation in lighting conditions that often pose a major challenge. The results of this study indicate that the use of AHE can improve the accuracy and stability of face detection and tracking under various lighting conditions. The application of AHE in realtime cascade classifier face tracking systems is expected to make a significant contribution to the development of more advanced and reliable facial recognition systems.
Keywords: face tracking, adaptive histogram equalization, realtime cascade classifier | en_US |
