Abstract:When the CascadeClassifier cascade classifier provided in OpenCV uses Haar features for face detection, the detection speed is too slow to meet the real-time requirements of the video, and the impact of lighting is also great. Based on these two points, a new face detection algorithm is proposed, which uses Camshift target tracking and face detection to improve the detection speed and uses histogram equalization to reduce the impact of light. The algorithm first sets the face area detected by the CascadeClassifier cascade classifier method as the ROI area, operates on the ROI area and uses the Camshift algorithm for target tracking, and secondly performs face detection regularly to update the ROI area to ensure the tracking accuracy. The analysis of the experimental results shows that: with the improved algorithm, the speed of face detection has been significantly increased (about 40%), and the impact of light is reduced.