Robust Face Tracking Under Challenging Conditions Using Linear Regression and YOLO algorithm

Face detection and tracking play a crucial role in various computer vision applications, including surveillance, fault face detection systems, artificial intelligence, etc. The objective of this paper is to enhance the precision of face detection and tracking through the introduction of an innovative approach centered on the linear regression algorithm. The effectiveness of the proposed method was compared to the traditional Kalman filter approach. Additionally, the study explored the integration of the YOLO algorithm for face detection with the linear regression tracking algorithm to further enhance accuracy. The proposed algorithm's performance is assessed through comprehensive experiments on annotated images and video sequences affected by occlusions or other issues such as poor lighting conditions and motion blur. These experiments utilize the COCO dataset, operating at a speed of 60 FPS. The experimental results show that the proposed method can accurately track the human face in different facial positions