Real-Time Eye Detection in Video Streams

A fast eye detection scheme for use in video streams rather than still images is presented in this paper. The temporal coherence of sequential frames was used to greatly improve the detection speed. First, the eye detector trained by AdaBoost algorithm is used to obtain the rough eye positions. Then these candidate positions are filtered by geometrical patterns of human eyes. The detected eye regions are then taken as the initial detecting window. After each frame is detected, the detecting window is updated. The experiments focused on video stream to exploit the benefits of our detector. In our experiments the mean detection rate was 92.73% for 320 times 240 resolution test videos, with a speed of 24.98 ms per frame. This speed is faster than previous research; however the detection rate does not dramatically decrease.