Research on video face detection based on AdaBoost algorithm training classifier
Yu Meng, Lijun Yun, Zaiqing Chen, Feiyan Cheng · 2017
In this paper, we train the classifier with CAS-PEAL-R1 face database which vary in pose, lighting, accessories and expression in order to solve the complexity of face detection in surveillance video, and then apply the classifier to video face detection system. First of all, single frame from video sequence is wiped off noise by the median filtering and average filtering, after that, the skin color segmentation of the preprocessed images was performed using the simple skin color model established in YCbCr space. We use geometric rules to exclude a part of facelike region in order to further accelerate the speed of face detection, and then use the classifier for the remaining face detection. Finally, the experiment results show that the algorithm can detect faces in surveillance video quickly and precisely based on OpenCV and Qt platform.