·464· 智能系统学报 第3卷 ance of SVMNN is the difficulty of choosing the right 0.95 number of nearest neighbors (K)when the number of 0.85 training examples is sall We observed that the LSVM algorithm consistently outperomed nonlinear SVM and 0.75 KNN-SVM for the six basic facial expressions We also 0.65 found both suprise and happ iness recognized with higher accuracy than other facial expressions,with the 0.55 SVM exception of the KNN classifier Fig 6 shows the ROC 0.45 curves of the four classifiers It demonstrates that the 0.02 0.04 0.06 0.08 0.10 LSVM classifier outperfoms the SVM,KNN-SVM and Fig 6 Roc curves of the four classifiers KNN classifiers Table 3 Clssifica tion accuracies (%for SV,KNN,KNN-SVM and LSVM Inputs Results/% Happy Surprise Disgust Fear Sad Anger A verage accuracy LSVM 91.32 9248 8832 89.64 8638 8654 89.11 SVM 8809 8867 85.86 85.71 8241 79.62 85.06 SVMNN 8755 87.92 8423 8468 8497 8085 8503 N 8209 7838 7936 8062 77.4175.92 7896 Fig 7 shows the six basic facial expression recog- frame shows an expression with great intensity In the nition results from our proposed system.Our method last frame,we extract geometric features and classify recognizes facial expressions from video sequences The the exp ression using LSVM. first frame shows a neutral expression while the last T02:40:41:04 Fig 7 Facial expression recogniton results in our poposed system 5 Conclusion ger,disgust,fear,joy,sadness and surprise We tracked the facial feature points using ASM and extrac- In this paper,we proposed an automatic method ted geometric features from video sequences To i- for recognizing protyp ical exp ressions that include an- prove facial exp resson recognition accuracy,we pres- 1994-2009 China Academic Journal Electronic Publishing House.All rights reserved.http://www.cnki.netance of SVM2NN is the difficulty of choosing the right number of nearest neighbors ( K) when the number of training examp les is small. W e observed that the LSVM algorithm consistently outperformed nonlinear SVM and KNN2SVM for the six basic facial exp ressions. W e also found both surp rise and happ iness recognized with higher accuracy than other facial exp ressions, with the excep tion of the KNN classifier. Fig. 6 shows the ROC curves of the four classifiers. It demonstrates that the LSVM classifier outperform s the SVM, KNN2SVM and KNN classifiers. Fig. 6 Roc curves of the four classifiers Table 3 C la ssifica tion accurac ies ( %) for SVM , KNN, KNN2SVM and LSVM Inputs Results/% Happy Surp rise D isgust Fear Sad Anger Average accuracy LSVM 91. 32 92. 48 88. 32 89. 64 86. 38 86. 54 89. 11 SVM 88. 09 88. 67 85. 86 85. 71 82. 41 79. 62 85. 06 SVM2NN 87. 55 87. 92 84. 23 84. 68 84. 97 80. 85 85. 03 KNN 82. 09 78. 38 79. 36 80. 62 77. 41 75. 92 78. 96 Fig. 7 shows the six basic facial exp ression recog2 nition results from our p roposed system. Our method recognizes facial exp ressions from video sequences. The first frame shows a neutral exp ression while the last frame shows an exp ression with great intensity. In the last frame, we extract geometric features and classify the exp ression using LSVM. Fig. 7 Facial exp ression recognition results in our p roposed system 5 Conclusion In this paper, we p roposed an automatic method for recognizing p rototyp ical exp ressions that include an2 ger, disgust, fear, joy, sadness and surp rise. We tracked the facial feature points using ASM and extrac2 ted geometric features from video sequences. To im2 p rove facial exp ression recognition accuracy, we p res2 ·464· 智 能 系 统 学 报 第 3卷