projectreport1）

1.IntroductionNowadays,face detection and face recognition technology became apopular study,and these technologies started to show its importance dayby day,at institutional or personal usage of security or fun purpose.Themost widely usages of these technologies are for personal usage,suchthat,cameras use face detection for successful focusing,security systemsuse face recognition to identify the person's identity.(Today,personalcomputers,mostly laptop computers use face recognition for securitypurposes like fingerprint technology used recently).The aim of our project is,to implement the performance of one facedetection algorithm,based on MatLab programming language.For thatpurpose,I chose a classification-based approach using Gabor filterfeatures for detecting faces from cluttered images.As "Robust facedetection using Gabor filter features"face detection algorithms proposes,I used four Gabor filters corresponding to four orientations for extractingfacial features from the local image in sliding window.The feature vectorbased on Gabor filters is used as the input of the classifier,which is aBayes classifier on a reduced feature subspace learned by principalcomponent analysis (PCA).The effectiveness of the proposed method isdemonstrated by experimental results on testing a large number ofimages.