International Journal of Science, Engineering and TechnologyAuthors: Prithviraj Eknath Kharade, Kashish Dara
Abstract: Face detection is a fundamental computer vision task with applications in surveillance, biometric authentication, attendance systems, and human–computer interaction. This paper presents a comparative study of the traditional Haar Cascade algorithm and the deep learning-based YOLOv8 model. Both methods are evaluated based on accuracy, speed, computational efficiency, and performance under different conditions such as illumination, occlusion, and pose variations. The analysis shows that Haar Cascade is suitable for lightweight, real-time applications with limited resources, while YOLOv8 provides higher detection of accuracy and robustness in complex environments. This study highlights the strengths, limitations, and practical applications of both approaches to assist in selecting an appropriate face detection method for different use cases.
