Face Recognition In Classroom

With the development of Society and Information Technology, identity recognition technology grows everyday. Identification techniques based on fingerprints, voice and human face have been applied gradually.Face detection and face recognition have important application value in a lot of fields. This paper mainly applies this technique to video stream analyzing. The aim is to develop a video stream oriented face recognition system used in remote learning environment. First ,Adaboost algorithm is applied to cut out face pictures from video stream. Then, in order to supply convenience to following face recognition, face pictures cut out from video stream are preprocessed including normalized by rotation and size normalization. This paper analyses different kinds of feature extraction algorithm and face recognition algorithm and adopt Local Binary Pattern histogram feature as the face's description. Taking the consideration of the statistic distribution of both target human faces and non target human faces, this paper usesχ2 distance and the Nearest Neighbor Classifier as classify method . At last, this paper brings up an integrated, video stream oriented face recognition system used in remote learning and local classroom. This system is very convenient for extension and easy to substitute algorithms which make a lot convenience for comparison of different algorithms.