第3卷第5期 智能系统学报 Vol 3 No 5 2008年10月 CAA I Transactions on Intelligent Systems 0ct2008 基于局部SM分类器的表情识别方法 孙正兴，徐文晖 (南京大学计算机软件新技术国家重点实验室，江苏南京210093) 摘要：提出了一种新的视频人脸表情识别方法.该方法将识别过程分成人脸表情特征提取和分类2个部分，首先采用 基于点跟踪的活动形状模型(ASM)从视频人脸中提取人脸表情几何特征：然后，采用一种新的局部支撑向量机分类器对 表情进行分类.在Cohn-Kanade数据库上对N、SM、NN-SM和LSM4种分类器的比较实验结果验证了所提出方 法的有效性 关键字：人脸表情识别：局部支撑向量机活动形状模型；几何特征 中图分类号：IP391文献标识码：A文章编号：1673-4785(2008)050455-12 Facal expression recogn ition based on local SVI classifiers SUN Zheng-xing,XU Wen-hui (State Key Lab or Novel Sofware Technobgy,Nanjing University,Nanjing 210093,China) Abstract:This paper presents a novel technique developed for the identification of facial expressions in video sources The method uses two steps facial expression feature extraction and expression classification Firstwe used an active shape model (ASM)based on a facial point tracking system to extract the geometric features of facial ex- pressions in videos Then a new type of local support vecpormachine (LSVM)was created to classify the facial ex- pressions Four different classifiers using KNN,SVM,KNN-SVM,and LSVM were compared with the new LSVM. The results on the Cohn-Kanade database showed the effectiveness of our method Keywords:facial expression recognition;bcal SVM;active shape model,geometry feature Automatic facial expression recogniton has attrac-erature to automate the recognition of facial expressions ted a lot of attention in recent years due o its potential in mug shots or video sequences Early methods used ly vital role in applications,particularly those using mug shots of expressions that captured characteristic human centered interfaces Many applications,such as mages at the apex However,according o psy virtual reality,video-conferencing.user pofiling.and chobgists,analysis of video sequences produces customer satisfaction studies for broadcast and web more accurate and robust recognition of facial expres- services,require efficient facial expresson recognition sions These methods can be categorized based on the in order to achieve their desired results Therefore,the data and features they use,as well as the classifiers mpact of facial expression recognition on the above-created for expression recognition In summary,the mentoned applications is constantly growing classifiers include Nearest Neighbor classifier,Neu- Several app oaches have been reported in the lit ral Neworks,SVM,Bayesian Neworks,Ada- Boost classifie and hidden Markov mode The 收稿日期：2008-07-11 data used for automated facial expresson analysis 基金项目：National Hig-Technolgy Research and Develpment Program (863)of China (2007AA01Z334);National Natural Science (AFEA)can be geometric features or texture features, Foundaton of China(69903006,60373065,0721002). 通信作者：孙正兴.Email:sx@nju.edu.cn for each there are different feature extracton methods Though facial expression recognition has made remark- 1994-2009 China Academic Journal Electronic Publishing House.All rights reserved.http://www.cnki.net第 3卷第 5期 智 能 系 统 学 报 Vol. 3 №. 5 2008年 10月 CAA I Transactions on Intelligent System s Oct. 2008 基于局部 SVM分类器的表情识别方法 孙正兴 ,徐文晖 (南京大学 计算机软件新技术国家重点实验室 ,江苏 南京 210093) 摘 要 :提出了一种新的视频人脸表情识别方法. 该方法将识别过程分成人脸表情特征提取和分类 2个部分 ,首先采用 基于点跟踪的活动形状模型 (ASM)从视频人脸中提取人脸表情几何特征 ;然后 ,采用一种新的局部支撑向量机分类器对 表情进行分类. 在 Cohn2Kanade数据库上对 KNN、SVM、KNN2SVM和 LSVM 4种分类器的比较实验结果验证了所提出方 法的有效性. 关键字 :人脸表情识别 ;局部支撑向量机 ;活动形状模型 ;几何特征 中图分类号 : TP391 文献标识码 : A 文章编号 : 167324785 (2008) 0520455212 Fac ial expression recogn ition based on local SVM classifiers SUN Zheng2xing, XU W en2hui ( State Key Lab for Novel Software Technology, Nanjing University, Nanjing 210093, China) Abstract: This paper p resents a novel technique developed for the identification of facial exp ressions in video sources. The method uses two step s: facial exp ression feature extraction and exp ression classification. Firstwe used an active shape model (ASM) based on a facial point tracking system to extract the geometric features of facial ex2 p ressions in videos. Then a new type of local support vectormachine (LSVM) was created to classify the facial ex2 p ressions. Four different classifiers using KNN, SVM, KNN2SVM, and LSVM were compared with the new LSVM. The results on the Cohn2Kanade database showed the effectiveness of our method. Keywords: facial exp ression recognition; local SVM; active shape model; geometry feature 收稿日期 : 2008207211. 基金项目 : National High Technology Research and Development Program (863) of China ( 2007AA01Z334) ; National Natural Science Automatic facial exp ression recognition has attrac2 ted a lot of attention in recent years due to its potential2 ly vital role in app lications, particularly those using human centered interfaces. Many app lications, such as virtual reality, video2conferencing, user p rofiling, and customer satisfaction studies for broadcast and web services, require efficient facial exp ression recognitio Foundation of China (69903006, 60373065, 0721002) . 通信作者 :孙正兴. E2mail: szx@nju.edu.cn n in order to achieve their desired results. Therefore, the impact of facial exp ression recognition on the above2 mentioned app lications is constantly growing. Several app roaches have been reported in the lit2 erature to automate the recognition of facial exp ressions in mug shots or video sequences. Early methods used mug shots of exp ressions that cap tured characteristic images at the apex [ 122 ] . However, according to p sy2 chologists [ 3 ] , analysis of video sequences p roduces more accurate and robust recognition of facial exp res2 sions. These methods can be categorized based on the data and features they use, as well as the classifiers created for exp ression recognition. In summary, the classifiers include Nearest Neighbor classifier [ 4 ] , Neu2 ral Networks [ 5 ] , SVM [ 6 ] , Bayesian Networks [ 7 ] , Ada2 Boost classifier [ 6 ] and hidden Markov model [ 8 ] . The data used for automated facial exp ression analysis (AFEA) can be geometric features or texture features, for each there are different feature extraction methods. Though facial exp ression recognition has made remark2