Classroom Face Recognition System Based On Labview

With the development of modern society, requirement of the fast and efficientautomatic authentication is increasingly urgent. Biological characteristics are intrinsicproperties along with its’ strong stability, hence they can be the best basis ofauthentication. Face, which acts as one of the most important visual object in imagesand videos, occupies an important position in computer vision, multimedia technology,and pattern recognition research.This research is on the basis of the existing equipment-Virtual InstrumentLaboratory of the Qingdao University of Science and Technology, including the studyof face detection algorithm, face recognition algorithm, the design and realization ofthe face recognition system for the video stream used to classroom scene.The systemcan effectively improve the demote learning environment, which can contribute to theinteraction between the teachers and students.This paper uses a combination of machine vision technology and virtualinstruments, it can make full use of the metrics of LabVIEW such as graphicalprogramming, the friendly man-machine interface, easy operation for imageprocessing, and dynamic link library of VC++6.0is called to complete the design ofthe critical part of the algorithms to improve the running speed of the system. WithNI’s camera, Image Acquisition has the following advantages, interface simple, hightransfer rate, and better real time quality.Face detection use a combination of color and template matching algorithm,firstly in the YCbCr color space, with complexion, images are separated into skinregions and non-skin regions, then calculate the color similarity chromaticity diagram,and then split the resulting chromaticity diagram into the grayscale image, resulting inthe candidate region of the face detection, and Euler number calculation in the regionto narrow the search range, and then use template matching, and ultimately to identifythe location of human faces in the images. Face recognition, after the preprocessingsuch as human eye detection, face regularized, face mask, filtering, we can extract LBPfeature, and then use the nearest neighbor rule to identify classify faces.