·456· 智能系统学报 第3卷 able progress,recognizing facial expressions with high active shape model (ASM)based tracking In each accuracy is a difficult problem!AFEA and its effec- video sequence,the first frame shows a neutral exp res- tive use in computing presents a number of difficult sion while the last frame shows an expression with max- challenges In general,wo main processes can be dis-mum intensity For each frame,we extract geometric tinguished in tackling the problem:1)ldentification of features as a static feature vector,which represents fa- features that contain useful infomation and reduction of cial contour infomation during changes of expression the dmensions of feature vectors in order to design bet- At the end,by subtracting the static features of the first ter classifiers 2)Design and mplementation of robust frame from those of the last,we get dynam ic geometric classifiers that can leam the underlying models of facial infomation or classifier input Then an LSVM classifi- expressons er is used for classification into the six basic expression We propose a new classifier for facial expression types recognition,which comes from the ideas used in the The rest of the paper is organized as follows Sec- KNN-SVM algorithm.Ref [10 proposed this algo- tion 2 reviews facial expression recognition studies In rithm for visual object recognition This method com- Secton 3 we briefly describe our facial point tracking bines SVM and KNN classifiers and iplements accu- system and the features extracted for classification of rate local classification by using KNN for selecting rele- facial expressions Section 4 describes the Local SVM classifier used for classifying the six basic facial ex- vant training data for the SVM.In order to classify a pressions in the video sequences Experments,per sample x,it first selects k training samples nearest to fomance evaluations,and discussions are given in sec- the sample x,and then uses these k samples o train an tion 5.Finally,section 6 gives conclusions about our SVM model which is then used to make decisions work KNN-SVM builds a maxmal margin classifier in the neighborhood of a test sample using the feature space 1 Rela ted work induced by the SVM's kemel function But this classifi- Psychological studies have suggested that facial er discards nearest-neighbor searches from the SVM motion is fundamental to the recognition of facial ex- leaming algorithm.Once the K-nearest neighbors have pression Expermnents conducted by Bassili demon- been identified,the SVM algorithm completely ignores strated that humans do a better job recognizing exp res- their si ilarities to the given test example So we pres- sions from dynam ic mages as opposed to mug shots ent a new classifier based on KNN-SVM,called bcal Facial expressions are usually described in to ways SVM (LSVM),which incorporates neighborhood infor as combinations of action units,or as universal expres- mation into SVM leaming The principle behind LSVM sions The facial action coding system (FACS)was is that it reduces the mpact of support vectors located devebped to describe facial exp ressions using a combi- far away from a given test example nation of action units (AU)Each action unit co In this paper,a system for automatically recogniz responds to specific muscular activity that produces ing the six universal facial expressions anger,dis- momentary changes in facial appearance Universal ex- gust,fear,joy,sadness,and surprise)in video se- pressions are studied as a complete representation of a quences using geometrical feature and a novel class of specific type of intemal emotion,without breaking up SVM called LSVM is proposed The system detects expressions into muscular units Most commonly stud- frontal faces in video sequences and then geometrical ied universal expressions include happ iness,anger, features of some key facial points are extracted using sadness,fear,and disgust In this study,universal 1994-2009 China Academic Journal Electronic Publishing House.All rights reserved.http://www.cnki.netable p rogress, recognizing facial exp ressions with high accuracy is a difficult p roblem [ 9 ] . AFEA and its effec2 tive use in computing p resents a number of difficult challenges. In general, two main p rocesses can be dis2 tinguished in tackling the p roblem: 1) Identification of features that contain useful information and reduction of the dimensions of feature vectors in order to design bet2 ter classifiers. 2) Design and imp lementation of robust classifiers that can learn the underlyingmodels of facial exp ressions. W e p ropose a new classifier for facial exp ression recognition, which comes from the ideas used in the KNN2SVM algorithm. Ref. [ 10 ] p roposed this algo2 rithm for visual object recognition. This method com2 bines SVM and KNN classifiers and imp lements accu2 rate local classification by using KNN for selecting rele2 vant training data for the SVM. In order to classify a samp le x, it first selects k training samp les nearest to the samp le x, and then uses these k samp les to train an SVM model which is then used to make decisions. KNN2SVM builds a maximal margin classifier in the neighborhood of a test samp le using the feature space induced by the SVM’s kernel function. But this classifi2 er discards nearest2neighbor searches from the SVM learning algorithm. Once the K2nearest neighbors have been identified, the SVM algorithm comp letely ignores their sim ilarities to the given test examp le. So we p res2 ent a new classifier based on KNN2SVM, called local SVM (LSVM) , which incorporates neighborhood infor2 mation into SVM learning. The p rincip le behind LSVM is that it reduces the impact of support vectors located far away from a given test examp le. In this paper, a system for automatically recogniz2 ing the six universal facial exp ressions ( anger, dis2 gust, fear, joy, sadness, and surp rise) in video se2 quences using geometrical feature and a novel class of SVM called LSVM is p roposed. The system detects frontal faces in video sequences and then geometrical features of some key facial points are extracted using active shape model (ASM ) based tracking. In each video sequence, the first frame shows a neutral exp res2 sion while the last frame shows an exp ression with max2 imum intensity. For each frame, we extract geometric features as a static feature vector, which rep resents fa2 cial contour information during changes of exp ression. A t the end, by subtracting the static features of the first frame from those of the last, we get dynam ic geometric information for classifier input. Then an LSVM classifi2 er is used for classification into the six basic exp ression types. The rest of the paper is organized as follows. Sec2 tion 2 reviews facial exp ression recognition studies. In Section 3 we briefly describe our facial point tracking system and the features extracted for classification of facial exp ressions. Section 4 describes the Local SVM classifier used for classifying the six basic facial ex2 p ressions in the video sequences. Experiments, per2 formance evaluations, and discussions are given in sec2 tion 5. Finally, section 6 gives conclusions about our work. 1 Rela ted work Psychological studies have suggested that facial motion is fundamental to the recognition of facial ex2 p ression. Experiments conducted by Bassili [ 11 ] demon2 strated that humans do a better job recognizing exp res2 sions from dynam ic images as opposed to mug shots. Facial exp ressions are usually described in two ways: as combinations of action units, or as universal exp res2 sions. The facial action coding system ( FACS) was developed to describe facial exp ressions using a combi2 nation of action units (AU) [ 12 ] . Each action unit cor2 responds to specific muscular activity that p roduces momentary changes in facial appearance. Universal ex2 p ressions are studied as a comp lete rep resentation of a specific type of internal emotion, without breaking up exp ressions into muscular units. Most commonly stud2 ied universal exp ressions include happ iness, anger, sadness, fear, and disgust. In this study, universal ·456· 智 能 系 统 学 报 第 3卷