第5期 孙正兴，等：基于局部SM分类器的表情识别方法 ·457- expressons were analyzed using the facial exp ression sions For the static case,a DBN is used,organized in coding system. a tree structure For the dynam ic approach,a multi- Many automated facial expression analysis meth- level hidden Markov models (HMMs)classifier is em- ods have been devebped Masued optical plyed flow (OF)to recognize facial exp ressions He was one These methods are sm ilar in that they first extract of the first to use mage-processing techniques to recog- some features from the mages,then these features are nize facial expressions Black and Yacoobls1 used used as inputs into a classification system,and the out cal parameterized models of mage motion to recover come is one of the pre-selected emotion categories non-rigid motion Once recovered,these parameters They differ mainly in the features extracted fiom the were used as inputs o a rule-based classifier o recog video mages and in the classifiers used to distinguish nize the six basic facial expressions Ref [16]used beteen the different emotions In the follwing sec- bwer face tracking to extract mouth shape features and tions,an automatic geometric feature based method is used them as inputs to an HMM based facial expression proposed,and then LSVM classifiers are used for rec- recognition system recognizing neutral,happy,sad, ognizing facial expressions fiom video sequences and an open mouth).Bartlett autmatically detects 2 Geom etrical fea ture extraction frontal faces in the video stream and classifies them in seven classes in real tme:neutral,anger,disgust, Our work focused on the design of classifiers for fear,joy,sadness,and surprise An exp ression recog- mproving recognition accuracy,follwing the extrac- nizer receives mage regions produced by a face detec- tion of geometric features using a model-based face tor and then a Gabor representation of the facial mage tracking system.That is,the proposed process for fa- region is fomed to be later processed by a bank of cial expression recognition is composed of to steps SVM classifiers Facial feature detection and tracking one AS based geometric infomation extraction;the is based on active InfraRed illum ination in Ref [18], next LSVM based classification Geometric feature in- in order to provide visual infomation under variable fomation extraction is perfomed by ASM based auto- lighting and head motion The classification is per matic locating and tracking,while the classification of fomed using a dynam ic Bayesian netork (DBN).geometric infomation is peromed by an LSVM Classi- COHEN eta popod a methodor static and dy- fier Fig 1 shows the proposed facial expression recog- nam ic segentation and classification of facial expres- nition scheme First frame Distance Six general classification parameters of facial expressions 个 Face video ASM-based Geometric Local SVM sequences tracking features classifiers ◆ Distance Classifier Last frame parameters Samples collection training Fig 1 Process of facial expression recognition for video sequences 1994-2009 China Academic Journal Electronic Publishing House.All rights reserved.http://www.cnki.netexp ressions were analyzed using the facial exp ression coding system. Many automated facial exp ression analysis meth2 ods have been developed [ 13 ] . Mase [ 14 ] used op tical flow (OF) to recognize facial exp ressions. He was one of the first to use image2p rocessing techniques to recog2 nize facial exp ressions. B lack and Yacoob [ 15 ] used lo2 cal parameterized models of image motion to recover non2rigid motion. Once recovered, these parameters were used as inputs to a rule2based classifier to recog2 nize the six basic facial exp ressions. Ref. [ 16 ] used lower face tracking to extract mouth shape features and used them as inputs to an HMM based facial exp ression recognition system ( recognizing neutral, happy, sad, and an open mouth). Bartlett [ 17 ] automatically detects frontal faces in the video stream and classifies them in seven classes in real time: neutral, anger, disgust, fear, joy, sadness, and surp rise. An exp ression recog2 nizer receives image regions p roduced by a face detec2 tor and then a Gabor rep resentation of the facial image region is formed to be later p rocessed by a bank of SVM classifiers. Facial feature detection and tracking is based on active InfraRed illum ination in Ref. [ 18 ], in order to p rovide visual information under variable lighting and head motion. The classification is per2 formed using a dynam ic Bayesian network (DBN ). COHEN et al [ 18 ] p roposed a method for static and dy2 nam ic segmentation and classification of facial exp res2 sions. For the static case, a DBN is used, organized in a tree structure. For the dynam ic app roach, a multi2 level hidden Markov models (HMM s) classifier is em2 p loyed. These methods are sim ilar in that they first extract some features from the images, then these features are used as inputs into a classification system, and the out2 come is one of the p re2selected emotion categories. They differ mainly in the features extracted from the video images and in the classifiers used to distinguish between the different emotions. In the following sec2 tions, an automatic geometric feature based method is p roposed, and then LSVM classifiers are used for rec2 ognizing facial exp ressions from video sequences. 2 Geom etr ica l fea ture extraction Our work focused on the design of classifiers for imp roving recognition accuracy, following the extrac2 tion of geometric features using a model2based face tracking system. That is, the p roposed p rocess for fa2 cial exp ression recognition is composed of two step s: one ASM based geometric information extraction; the next LSVM based classification. Geometric feature in2 formation extraction is performed by ASM based auto2 matic locating and tracking, while the classification of geometric information is performed by an LSVM Classi2 fier. Fig. 1 shows the p roposed facial exp ression recog2 nition scheme. Fig. 1 Process of facial exp ression recognition for video sequences 第 5期 孙正兴 ,等 :基于局部 SVM分类器的表情识别方法 ·457·