第5期 孙正兴，等：基于局部SM分类器的表情识别方法 ·459- ding the classifier with data that encode the most m- portant aspects of the facial expressions The distance parameters are computed as the mplicit fixed Euclide- an distances between key points The complete list of such distance parameters is given in Table 1 In Table 1, (P,.P),represents the horion distance beteen points P,and P,.(P,.P),represents the vertical dis- tance beteen points P,and P.Because when facial Fig 3 Facial characteristic points expressions change,most movement is in the vertical 22 Facil characteristic points model direction,most of the distance parameters compute ver The shape infomation extracted by AM from a tical distance We extracted the differences beteen face mage is used to compute a set of distance parame- the last and the first frame's distance parameters as the ters that describe the appearance of facial features geometric features The geometric features capture the ASM extracts 68 facial points,however some of these subtle changes in facial expression which varied over don't reflect changes in facial exp ressions The first the video sequence Let Vena be the distance parameter step is the selection of the 20 optmal key facial points, of the last frame,Ve be the distance parameter of the those which change the most with changes in expres- first frame. sion These key points P are defined as the facial char =Vead-eem,i∈fl,2,…sNk.(3) acteristic points (FCPs,Fig 3),which were derived Where x,is the geometric feature of the i-th video se- from the Kobayashi Hara model2.In the second quence,which is defined as the difference beteen step the FCPs are converted into some distance param- static features of the first frame and the last frame The eters This parameterization has the advantage of provi- diension of the geometric feature x,is 18 Table 1 The set of distance parameters meaning Visual feature v meaning Visual feature meaning Visual feature n(Po.P) Left eyebrow h(P,P9), Left eye n3 (Pu.P16)y Mouse 2(Po,P2), Left eyebrow (P6.Ps) Left eye M4 (Pis,Pis)y Mouse (P,P, Right eyebrow (P6.P)y Left eye ns (Pu.Pis)y Mouse v(P3.Ps)Right eyebrow Vho (Pu.Pu)y Right eye h6(P4,P, Mouse s (Po.Pu),Left eyebrow vu (Pio.Pu)y Right eye Vi (Pis.Pu)x Mouse 1(P3.Pu),Right eyebrow 2(P10,P3, R ight eye Vis (Pu:Pis)y Chin 3 Facil expression recogn ition based cial expression recognition accuracy We propose a fur ther mprovement,an LSVM classifier for facial exp res- on local SVM sion recognition,with its oots in the KNN-SVM!0 Effective facial expression recognition is a key classifier,but KNN-SVM decouples the nearest-neigh- problem in automated facial expression analysis The bor search from the SVM leaming algorithm.Once the KNNI201 and SVMI21 classifiers have been successfully K-nearest neighbors have been identified,the SVM al- applied to facial expression recognition and mprove fa- gorithm comp letely ignores their sm ilarities to the given 1994-2009 China Academic Journal Electronie Publishing House.All rights reserved.http://www.cnki.netFig. 3 Facial characteristic points 2. 2 Fac ia l character istic po ints m odel The shape information extracted by ASM from a face image is used to compute a set of distance parame2 ters that describe the appearance of facial features. ASM extracts 68 facial points, however some of these don ’t reflect changes in facial exp ressions. The first step is the selection of the 20 op timal key facial points, those which change the most with changes in exp res2 sion. These key points P are defined as the facial char2 acteristic points (FCPs, Fig. 3) , which were derived from the Kobayashi & Hara model [ 2 ] . In the second step the FCPs are converted into some distance param2 eters. This parameterization has the advantage of p rovi2 ding the classifier with data that encode the most im2 portant aspects of the facial exp ressions. The distance parameters are computed as the imp licit fixed Euclide2 an distances between key points. The comp lete list of such distance parameters is given in Table 1. In Table 1, ( Pi , Pj ) x rep resents the horizon distance between points Pi and Pj , ( Pi , Pj ) y rep resents the vertical dis2 tance between points Pi and Pj . Because when facial exp ressions change, most movement is in the vertical direction, most of the distance parameters compute ver2 tical distance. We extracted the differences between the last and the first frame’s distance parameters as the geometric features. The geometric features cap ture the subtle changes in facial exp ression which varied over the video sequence. Let Vend be the distance parameter of the last frame, Vbegin be the distance parameter of the first frame, xi = Vend - Vbegin , i ∈ { 1, 2, …, N }. (3) W here xi is the geometric feature of the i2th video se2 quence, which is defined as the difference between static features of the first frame and the last frame. The dimension of the geometric feature xi is 18. Table 1 The set of d istance param eters vi meaning V isual feature vi meaning V isual feature vi meaning V isual feature v1 ( P0 , P1 ) y Left eyebrow v7 ( P7 , P9) y Left eye v13 ( P14 , P16 ) y Mouse v2 ( P0 , P2 ) y Left eyebrow v8 ( P6 , P8 ) y Left eye v14 ( P15 , P18 ) y Mouse v3 ( P3 , P4 ) y Right eyebrow v9 ( P6 , P9 ) y Left eye v15 ( P14 , P15 ) y Mouse v4 ( P3 , P5 ) y Right eyebrow v10 ( P11 , P13 ) y Right eye v16 ( P14 , P17 ) y Mouse v5 ( P0 , P14 ) y Left eyebrow v11 ( P10 , P12 ) y Right eye v17 ( P15 , P17 ) x Mouse v6 ( P3 , P14 ) y Right eyebrow v12 ( P10 , P13 ) y Right eye v18 ( P14 , P19 ) y Chin 3 Fac ia l expression recogn ition ba sed on loca l SVM Effective facial exp ression recognition is a key p roblem in automated facial exp ression analysis. The KNN [ 20 ] and SVM [ 21 ] classifiers have been successfully app lied to facial exp ression recognition and imp rove fa2 cial exp ression recognition accuracy. W e p ropose a fur2 ther imp rovement, an LSVM classifier for facial exp res2 sion recognition, with its roots in the KNN2SVM [ 10 ] classifier, but KNN2SVM decoup les the nearest2neigh2 bor search from the SVM learning algorithm. Once the K2nearest neighbors have been identified, the SVM al2 gorithm comp letely ignores their sim ilarities to the given 第 5期 孙正兴 ,等 :基于局部 SVM分类器的表情识别方法 ·459·