Efficient Learning and Face Detection Tracking Based on Artificial Intelligence Algorithm

In the field of facial recognition and tracking, facing challenges such as complex backgrounds, lighting changes, and partial facial occlusion, how to improve detection efficiency and tracking accuracy has become an urgent problem to be solved. In response to this issue, this article proposes a comprehensive method for efficient learning and face detection tracking based on artificial intelligence algorithms. Firstly, the Convolutional Neural Network (CNN) in deep learning is used to extract facial features in order to improve the accuracy of face detection; subsequently, real-time face detection is implemented using the improved You Only Look Once (YOLO) algorithm, which improves the robustness and efficiency of detection in complex backgrounds; finally, based on Kalman filter and optical flow method, the detected faces are tracked to ensure the stability and continuity of the tracking process. In the experiment, this method also has high detection accuracy in complex backgrounds, with detection speeds reaching 15–60 FPS on different hardware platforms, significantly higher than SSD (Single Shot MultiBox Detector) and Faster R-CNN algorithms; in addition, in tracking tests under occlusion and fast motion conditions, the average displacement error (ADE) is 3.5 pixels for slight occlusion, 7.2 pixels for moderate occlusion, and 15.4 pixels for severe occlusion, respectively. Compared with traditional methods, the proposed method has higher tracking accuracy and stability, fully verifying its superiority and practicality in complex scenes.