An Improved Face Detection Method in Low-resolution Video
Chih–Chung Hsu, Hsuan Ting Chang, Ting-Cheng Chang · 2007
In this study, an efficient face detection method is proposed for low-resolution video. The cascaded face detector proposed by Viola can achieve real-time detection and a high detection rate. However, the motion blurr of the face images in the low-resolution video usually exists. The detection rate in low-resolution video is lower than that in static images because the training set in the Adaboost algorithm only considers about normal face images. Therefore, the enhanced training set which contains the normal face images and the motion blurred face images is used to improve the detection rate. The simulation results show that the face images in low-resolution video can be efficiently extracted.