·458· 智能系统学报 第3卷 For each input video sequence,an AdaBoost Where,B is a regulating parameter usually set beteen based face detector is applied to detect frontal and I and 3 according to the desired degree of flexibility in near-frontal faces in the first frame Inside detected the shape model m is the number of retained eigen- faces,our method identifies some mportant facial vectors,and is the eigenvalues of the covariance landmarks using the active shape model (ASM).ASM matrix The intensity model is constructed by compu- automatically bcalizes the facial feature points in the ting the second order statistics of nomalized mage gra- first frame and then trackes the feature points through dients,sampled at each side of the landmarks,perpen- the video frames as the facial expression evolves dicular to the shape's contour,hereinafter referred to as through tme The first frame shows a neutral expres-the profile In other words,the profile is a fixed-size sion while the last frame shows an expression with the vector of values in this case,pixel intensity values) greatest intensity For each frame,we extract distance smpled alng the pependicular o the conour such parameters beween some key facial points At the that the contour passes right through the middle of the end,by subtracting distance parameters from the first perpendicular The matching procedure is an altema- frame from those of the last frame,we get the geomet-tion of mage driven landmark disp lacements and statis- ric features for classification Then a LSVM classifier is tical shape constraining based on the PDM.It is usual- used for classification into the six basic expression ly perfomed in a multi-resolution fashion in order to types enhance the capture range of the algorithm.The land- 2 1 ASM based loca tng and tracking mark displacements are individually detem ined using AM is empboyed to extract shape infomation the intensity model,by m inm izing the Mahalanobis on specific faces in each frame of the video sequence distance beteen the candidate gradient and the model's The use of a face detection algorithm as a prior step has mean the advantage of speeding up the search for the shape To extract facial feature points in case of expres- parameters during ASM based processing ASM is built sion variation,we trained an active shape model from from sets of prom inent points known as landmarks, the JAFFE (Japanese female facial expression)data- computing a point distribution model (PDM)and a ba,which contains219 mages from 10 individual cal mage intensity model around each of those points Japanese females For each subject there are six basic The PDM is constructed by app lying PCA to an aligned facial expressions (anger,disgust,fear,happ iness, set of shapes,each represented by landmarks The o- sadness,suprise)and a neutral face 68 landmarks riginal shapes and their model representation b,(i=1.are used to define the face shape,as shown in Fig 2 2..N)are related by means of the mean shape u and the eigenvector matrixφ： b=φT(u-d,w,=u+中b. (1) To reduce the dmensions of the representation,it is possible to use only the eigenvectors corresponding to the largest eigenvalues Therefore,Equ (1)becomes an approxmation,with an error depending on the mag- nitude of the excluded eigenvalues Furthemore,un- der Gaussian assumptions,each component of the b, vectors is constrained to ensure that only valid shapes are represented,as follows |1≤BNn,1≤i≤N,1≤m≤M2) Fig 2 ASM training smple 1994-2009 China Academic Journal Electronic Publishing House.All rights reserved.http://www.cnki.netFor each input video sequence, an AdaBoost based face detector is app lied to detect frontal and near2frontal faces in the first frame. Inside detected faces, our method identifies some important facial landmarks using the active shape model (ASM). ASM automatically localizes the facial feature points in the first frame and then trackes the feature points through the video frames as the facial exp ression evolves through time. The first frame shows a neutral exp res2 sion while the last frame shows an exp ression with the greatest intensity. For each frame, we extract distance parameters between some key facial points. A t the end, by subtracting distance parameters from the first frame from those of the last frame, we get the geomet2 ric features for classification. Then a LSVM classifier is used for classification into the six basic exp ression types. 2. 1 ASM ba sed loca ting and tracking ASM [ 19 ] is emp loyed to extract shape information on specific faces in each frame of the video sequence. The use of a face detection algorithm as a p rior step has the advantage of speeding up the search for the shape parameters during ASM based p rocessing. ASM is built from sets of p rom inent points known as landmarks, computing a point distribution model (PDM) and a lo2 cal image intensity model around each of those points. The PDM is constructed by app lying PCA to an aligned set of shapes, each rep resented by landmarks. The o2 riginal shapes and their model rep resentation bi ( i = 1, 2, …, N ) are related by means of the mean shape …u and the eigenvector matrixφ: bi =φT ( ui - …u) , ui = …u +φbi . (1) To reduce the dimensions of the rep resentation, it is possible to use only the eigenvectors corresponding to the largest eigenvalues. Therefore, Equ. (1) becomes an app roximation, with an error depending on the mag2 nitude of the excluded eigenvalues. Furthermore, un2 der Gaussian assump tions, each component of the bi vectors is constrained to ensure that only valid shapes are rep resented, as follows: | b m i | ≤β λm , 1 ≤ i ≤N, 1 ≤m ≤M. (2) W here, β is a regulating parameter usually set between 1 and 3 according to the desired degree of flexibility in the shape model. m is the number of retained eigen2 vectors, and λm is the eigenvalues of the covariance matrix. The intensity model is constructed by compu2 ting the second order statistics of normalized image gra2 dients, samp led at each side of the landmarks, perpen2 dicular to the shape’s contour, hereinafter referred to as the p rofile. In other words, the p rofile is a fixed2size vector of values ( in this case, p ixel intensity values) samp led along the perpendicular to the contour such that the contour passes right through the m iddle of the perpendicular. The matching p rocedure is an alterna2 tion of image driven landmark disp lacements and statis2 tical shape constraining based on the PDM. It is usual2 ly performed in a multi2resolution fashion in order to enhance the cap ture range of the algorithm. The land2 mark disp lacements are individually determ ined using the intensity model, by m inim izing the Mahalanobis distance between the candidate gradient and the model’s mean. To extract facial feature points in case of exp res2 sion variation, we trained an active shape model from the JAFFE (Japanese female facial exp ression) data2 base [ 19 ] , which contains 219 images from 10 individual Japanese females. For each subject there are six basic facial exp ressions ( anger, disgust, fear, happ iness, sadness, surp rise) and a neutral face. 68 landmarks are used to define the face shape, as shown in Fig. 2. Fig. 2 ASM training samp le ·458· 智 能 系 统 学 报 第 3卷