Facial Expressions Recognition in a Single Static as well as Dynamic Facial Images Using Tracking and Probabilistic Neural Networks

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1 Facal Expressons Recognton n a Sngle Statc as well as Dynamc Facal Images Usng Trackng and Probablstc Neural Networks Had Seyedarab 1, Won-Sook Lee 2, Al Aghagolzadeh 1, and Sohrab Khanmohammad 1 1 Faculty of Electrcal and Computer Engneerng Unversty of Tabrz,Tabrz, Iran 2 School of Informaton Technology and Engneerng (SITE) Faculty of Engneerng, Unversty of Ottawa, Canada {seyedarab, aghagol, khan}@tabrzu.ac.r, wslee@uottawa.ca Abstract. An effcent, global and local mage-processng based extracton and trackng of ntransent facal features and automatc recognton of facal expressons from both statc and dynamc 2D mage/vdeo sequences s presented. Expresson classfcaton s based on Facal Acton Codng System (FACS) a lower and upper face acton unts (AUs), and dscrmnaton s performed usng Probablstc Neural Networks (PNN) and a Rule-Based system. For the upper face detecton and trackng, we use systems based on a novel two-step actve contour trackng system whle for the upper face, crosscorrelaton based trackng system s used to detect and track of Facal Feature Ponts (FFPs). Extracted FFPs are used to extract some geometrc features to form a feature vector whch s used to classfy nput mage or mage sequences nto AUs and basc emotons. Expermental results show robust detecton and trackng and reasonable classfcaton where an average recognton rate s 96.11% for sx basc emotons n facal mage sequences and 94% for fve basc emotons n statc face mages. Keywords: Actve contours, Acton Unts, Facal Expressons, Probablstc Neural Networks. 1 Introducton Automated facal expresson analyss usng computer vson work could brng facal expressons nto man-machne nteracton. Most computer-vson based approaches to facal expresson analyss attempt to recognze only prototypc emotons. These prototypc emotonal seem to be unversal across human ethnctes and cultures and comprse happness, sadness, fear, dsgust, surprse, and anger. In everyday lfe, however, such prototypc expressons occur relatvely nfrequently. Instead, emoton s communcated by changes n one or two dscrete features. In order to make the recognton procedure more standardzed, a set of facal muscle movements (known as Acton Unts) that produce each facal expresson, was L.-W. Chang, W.-N. Le, and R. Chang (Eds.): PSIVT 2006, LNCS 4319, pp , Sprnger-Verlag Berln Hedelberg 2006

2 Facal Expressons Recognton n a Sngle Statc as well as Dynamc Facal Images 293 created by psychologsts as Facal Acton Codng System (FACS) [1]. Table 1 shows AUs used n ths work that occur n the lower and upper face and are more mportant n descrbng facal expressons. In recent years, there has been extensve research on facal expresson analyss and recognton. Pantc and Rothkrantz [2] proposed an expert system for automatc analyss of facal expressons from statc face mages. Ther system conssts of two major parts, the frst one forms a frame work for hybrd facal feature detecton and the second part of the system converts low level face geometry nto hgh level facal actons. Table 1. Some of FACS AUs used n ths work AU (Upper Face) FACS descrpton AU (Lower Face) FACS descrpton 1 Rased nner brows 12 Mouth corners pulled up 2 Rased outer brows 15 Mouth corners pulled downwards 4 Lowered brows 17 Rased chn 5 Rased upper ld 20 Mouth stretched 6 Rased cheek 23 Lps tghtened 7 Rased lower ld 24 Lp pressed 9 Wrnkled nose 25 Lps parted Jaw dropped Mouth stretched Len et al. [3] developed a facal expressons recognton system that was senstve to subtle changes n the face. The extracted feature nformaton, usng a wavelet moton model, was fed to dscrmnaton classfers or hdden markov models that classfed t nto FACS acton unts. The system was tested on mage sequences from 100 subjects of vared ethncty. Average recognton accuracy for 15 AUs n the brow, eye and mouth regons was 81-91%. Valstar et al. [4] used temporal templates whch were 2D mages, constructed from mage sequences and showed where and when moton n the mage sequences has occurred. A K-Nearest Neghbor algorthm and a rule-based system performed the recognton of 15 AUs occurrng alone or n combnaton n an nput face mage sequences. Ther proposed method acheved an average recognton rate of 76.2% on the Cohn-Kanade face mage database. Bartlett et al. [5] used Gabor flters usng AdaBoost for feature selecton technque followed by classfcaton wth Support Vector Machnes. The system classfed 17 AUs wth a mean accuracy of 94.8%. The system was traned and tested on Cohn- Kanade face mage database. In ths paper we develop an automatc facal expressons analyss and classfcaton systems. Estmated postons of lps, eyes and eyebrows are determned by usng a knowledge based system.

3 294 H. Seyedarab et al. In the frst frame of mage sequences, 25 Facal Feature Ponts (FFPs) are automatcally detected, usng actve contours for the lower face and gray level projecton method for the upper face. A hybrd trackng system s used to track these FFPs n subsequent frames. An enhanced verson of the conventonal actve contour trackng system s used for lp trackng and a cross-correlaton based trackng system s used to track FFPs around eye, eyebrow and nose. Some geometrc features are extracted, based on the poston of FFPs n the frst and the last frames. Ths features form a feature vector whch s used for classfyng of nput mage sequences nto 16 AUs usng Probablstc Neural Networks (PNN). A rule-based decson makng system s appled to AUs to classfy nput mages nto basc emotons. Proposed features and feature extracton method can also be appled to statc mages (except features for wrnkled nose). Last frame n mage sequences whch represents peak of facal expressons s used to tran and test of statc mages recognton system. A local reference parameter s used to normalze extracted geometrc features. Most of the facal expresson recognton systems use manually located FFPs n the frst frame [3-5]. Our proposed system uses automatcally detectng and trackng of feature ponts. Proposed hybrd trackng system shows robust trackng results both n upper and lower face, whch only needs the rough estmated poston of eye, eyebrow and mouth. The system s traned and tested on 180 mage sequences, consstng sx basc facal expressons on Cohn-Kanade face mage database [6]. 2 Intal Poston of Facal Features Among the facal features, eye, eyebrow and mouth have mportant role n expressng facal emotons. There are three steps n an automatc facal expresson recognton system: - Face detecton - Facal feature extracton - Facal expresson classfcaton Our proposed algorthm uses four ponts on top, down, left and rght of the face as landmarks and determnes automatcally the ntal poston for facal features based on the face heght and wdth usng a knowledge-based system. Knowledge-based system s formed from eyes, eyebrows and mouth poston on 97 subjects n Cohn-Kanade face database. Based on ths system, three rectangles are located on the face as the ntal poston for the mouth, the left and rght eyes and eyebrows as shown n Fg. 1. These rectangles are bg enough to assure that they cover these features n dfferent facal mages. Addtonal process s used for automatcally detectng of accurate feature postons and extractng of 25 FFPs. Fg. 1 shows rectangles for ntal postons as well as 25 upper and lower face FFPs.

4 Facal Expressons Recognton n a Sngle Statc as well as Dynamc Facal Images 295 Fg. 1. Intal facal features poston and 25 upper and lower face FFPs 2.1 Actve Contours for Lp Localzaton The actve contour model algorthm, frst ntroduced by Kass et al. [7], deforms a contour to lock onto features of nterest wthn an mage Usually the features are lnes, edges, and/or object boundares. An actve contour s an ordered collecton of n ponts n the mage plane: V = { v1, v2,..., vn} v = ( x, y ), = 1,2,... n The ponts n the contour teratvely approach the boundary of an object through the soluton of an energy mnmzaton problem. For each pont n the neghborhood of v, an energy term s computed: (1) E = E ( v ) + E ( v ) (2) nt where E nt ( v ) s an energy functon dependent on the shape of the contour and E ext ( v ) s an energy functon dependent on the mage propertes, such as the gradent and near pont v. The nternal energy functon used heren s defned as follows: ext E ( v ) = ce ( v ) + be ( v ) (3) nt con where E con ( v ) s the contnuty energy that enforces the shape of the contour and E bal ( v ) s a balloon force that causes the contour to grow or shrnk, c and b provde the relatve weghtng of the energy terms. The external energy functon attracts the deformable contour to nterestng features, such as object boundares, n an mage. Image gradent and ntensty are bal

5 296 H. Seyedarab et al. obvous characterstcs to look at. The external energy functon used heren s defned as follows: E v ) = me ( v ) + ge ( v ) (4) ext ( mg grad where E mg ( v ) s an expresson that attracts the contour to hgh or low ntensty regons and E grad ( v ) s an energy term that moves the contour towards edges. Agan, the constants, m and g, are provded to adjust the relatve weghts of the terms Two-Step Lp Actve Contour We develop a lp shape extracton and lp moton trackng system, based on a novel two-step actve contours model. Four energy terms are used to control moton of control ponts. The ponts n the contour teratvely approach the outer mouth edges through the soluton of a two-step energy mnmzaton problem. One of the advantages of the proposed method s that we do not need to locate the ntal snake very close to lp edges. At the frst step actve contour locks onto stronger upper lp edges by usng both hgh threshold Canny edge detector and balloon energy for contour deflaton. Then usng lower threshold mage gradent as well as balloon energy for nflaton, snake nflates and locks onto weaker lower lp edges. In ths stage upper control ponts were fxed and only lower ponts nflates to fnd lower lp edges. Fg. 2 and Fg. 3 show flowchart of proposed two- step algorthm and results. Fg. 2. Two-step actve contour algorthm Fg. 3. Two-step lp trackng a) deflatng ntal snake and fndng upper strong edges b) ntal and fnal snake at the end of the frst step c) fxng 14 upper control ponts and nflatng 14 lower ponts to fnd lower weak edges d) fnal lp contour

6 Facal Expressons Recognton n a Sngle Statc as well as Dynamc Facal Images 297 In mage sequences two-steps actve contour s appled n the frst frame (whch s supposed that mouth s not open) and then the fnal snake s used as an ntal snake n the next frame. Fg. 4 shows some results of trackng n mage sequences. Fg. 4. Lp trackng n mage sequences usng proposed algorthm 2.2 Detectng FFPs n Eye and Eyebrow We used gray-level projecton method to separate eye, eyebrow and possble har regons. Usng local Mn-Max methods on the gray-level, proper thresholds are determned to separate eye, eyebrow and har regons n the ntally located rectangle. By usng horzontal projecton and selectng a proper threshold, eye and eyebrow regons n left and rght upper faces can be separated. Also har regon s removed usng vertcal projecton and gray level threshold for har regon. After determnng eyes and eyebrows and usng horzontal Sobel edge detecton as well as horzontal and vertcal scannng methods, 4 FFPs n eye corners and 3 FFPs n eyebrows are detected. Fg.5 shows vertcal and horzontal gray-level projectons, thresholds for har regon and eye-eyebrow separaton and detected FFPs. Fg. 5. Vertcal and horzontal gray-level projecton 3 Hybrd Trackng System We used our enhanced two-step verson of actve contours to track lower face FFPs and cross correlaton-based trackng system for upper face FFPs n mage sequences. Among the facal expressons, mouth has hgh flexblty and hard to track ts shape and deformaton. The use of actve contours s approprate especally when the feature

7 298 H. Seyedarab et al. shape s hard to represent wth a smple template. Nevertheless, the ntal actve contour must be located very close to the desred feature. Ths problem s removed usng a novel two-step actve contour algorthm. In the frst frame, the ntal actve contour s located usng an estmated mouth poston and lock on the outer mouth edges usng two-step actve contours. Ths contour s used as ntal contour n subsequent frames (Fg. 4). Because of openness of mouth n some statc mages, the proposed two-step actve contour could not be appled and we used the tradtonal and one-step contours to detect mouth shape n statc mages. Actve contours method has some problems to use n upper face features. Contours are very senstve to shadows around eyes and eyebrows. We used a cross-correlaton based trackng system for upper face features. Each upper face FFP s consdered as the center of a flow wndow that ncludes horzontal and vertcal flows. Cross-correlaton of wndow n the frst frame wth a search wndow at the next frame s calculated and the poston by maxmum cross-correlaton value for two wndows, were estmated as the poston of the feature pont for the next frame [8] [9]. Fg.6 shows detectng and trackng of upper face FFPs for surprse and dsgust expressons. Fg. 6. Trackng of upper face FFPs a) Surprse expresson b) Dsgust expresson 4 Feature Vector Extracton Extracted feature ponts are used to extract some geometrc feature ponts to form a feature vector for upper and lower face features. The followng features are extracted for lower face: - Openness of mouth: average vertcal dstance of ponts and (Fg. 1). - Wdth of mouth: horzontal dstance of ponts 17 and Chn rse: vertcal dstance of pont 22 from orgn. - Lp corners dstance: average vertcal dstance of ponts 17 and 20 from orgn - Normalzed quadratc curvature parameters for ponts 15, 16 and Normalzed quadratc curvature parameters for ponts 17, 21 and 22. To calculate and normalze curvature parameters, orgn s transferred to pont 17 that reduces curvature parameters from 3 to 2, also horzontal dstance of ponts 17 and 22, s normalzed to one.

8 Facal Expressons Recognton n a Sngle Statc as well as Dynamc Facal Images 299 Calculated features form a 8 1 feature vector whch s used for classfcaton of lower face acton unts. The followng features are extracted for upper face: - Openness of eye: vertcal dstance of ponts 9-10 and Heght of eyebrow 1: vertcal dstance of ponts 1 and 4 from orgn. - Heght of eyebrow 2: vertcal dstance of ponts 2 and 5 from orgn. - Inner eyebrow dstance: horzontal dstance of ponts 1 and 4. - Nose wrnkle: vertcal dstance of ponts 7-24 and Normalzed quadratc curvature parameters for ponts 1, 2 and 3. - Normalzed quadratc curvature parameters for ponts 4, 5 and 6. To calculate and normalze curvature parameters, orgn s transferred to pont 2 and 5 n left and rght eyebrow that reduces curvature parameters from 3 to 2; also horzontal dstance of ponts 1-3 and 4-6, s normalzed to one. Ponts 24 and 25 are determned by a square wth two vertces on ponts 7 and 11. These two ponts are used to track nose wrnkle whch s a dscrmnant feature for dsgust expresson (Fg. 6). Ths feature can not be calculated n statc mages and our proposed system can not detect dsgust expresson n statc mages. As t has been shown n Fg.7, wthout recognzng wrnkled nose (AU9), dsgust expresson s very close to anger or sad expressons. Fg. 7. Comparng Dsgust wth Sad and Anger expressons Calculated features form a 9 1 feature vector n mage sequences and a 8 1 feature vector n statc mages whch are used for classfcaton of upper face acton unts. Md-Pont between nner eye corners s determned as orgn. In the mage sequences, calculated features (except curvature parameters) n the frst and the last frames are normalzed usng the followng equaton: Norm _ fature= ( Last _ frame Frst _ frame) / Frst _ frame (5) Last frame n mage sequences whch represents peak of facal expressons s used to extract curvature parameters. In the statc mages, dstance of nner eye corners (dstance of ponts 7 and 11 n Fg. 1) s used as a local reference to normalze extracted geometrc features to remove the effect of subject-camera dstance.

9 300 H. Seyedarab et al. 5 PNN Classfer Probablstc Neural Networks (PNN) s a varant of the Radal Bass Functon Neural Networks (RBFNN) and attempts have been carred out to make the learnng process n ths type of classfcaton faster than normally requred for the mult-layer feed forward neural networks. The constructon of PNN nvolves an nput layer, a hdden layer and an output layer wth feed forward archtecture. The nput layer of ths network s a set of R unts, whch accept the elements of an R-dmensonal nput feature vector. The nput unts are fully connected to the hdden layer wth Q hdden unts (RBF unts). Q s the number of nput/target tranng pars. Each target vector has K elements. One of these elements s 1 and the rest are 0. Thus, each nput vector s assocated wth one of K classes. When an nput vector s presented n the nput layer, the hdden layer computes dstances from the nput vector to the tranng nput vectors, and produces a vector whose elements ndcate how close the nput s to a tranng nput. The output layer sums these contrbutons for each class of nputs to produce ts net output as a vector of probabltes. Fnally, a compete transfer functon on the output of the output layer pcks up the maxmum of these probabltes, and produces a 1 for that class and 0 for the other classes [9]. 6 Expermental Results The Cohn-Kanade database conssts of expresson sequences of subjects, startng from a neutral expresson and endng wth the peak of the facal expresson. Subjects sat drectly n front of the camera and performed a seres of the facal expressons that ncluded the sx prmary and also some sngle AUs. We used a subset of 180 mage sequences contanng sx basc emotons for 30 subject emotons. Those AUs whch are mportant to the communcaton of the emoton and were occurred at least 25 tmes n our database are selected. Ths frequency crteron ensures suffcent data for tranng and testng. For each person there are on average of 12 frames for each expresson (after elmnatng alternate frames). Image sequences for the frontal vews are dgtzed nto pxel array wth 8 bts grayscale [6]. 6.1 Recognton of Upper and Lower Face AUs n Image Sequences We used the sequence of 144 (80%) subjects as tranng sequences, and the sequence of the remanng 36 (20%) subjects as test sequences. Ths test s repeated fve tmes, each tme leavng dfferent subjects out. The number of the nput layer unts n the lower face PNN classfer s equal to 8, the number of extracted features, the number of the hdden layer unts equals to 144 9, the number of tranng pars and that of the output layers s 9, whch corresponds to selected 9 lower face AUs. The number of the nput layer unts n the upper face PNN classfer s equal to 9, the number of the hdden layer unts equals to144 7, and that of the output layers s 7, whch corresponds to the selected 7 upper face AUs. Table 2 shows the recognton rate of lower and upper face AUs.

10 Facal Expressons Recognton n a Sngle Statc as well as Dynamc Facal Images 301 Table 2. Recognton results for lower and upper face AUs n mage sequences Lower Face AUs Upper Face AUs AU12 31/35 %88.57 AU1 52/65 % 80 AU15 27/29 %93.10 AU2 35/39 %89.74 AU17 73/82 %89.02 AU4 77/91 % AU20 26/30 %86.67 AU5 30/32 %93.75 AU23 24/29 %82.76 AU6 31/38 % AU24 23/32 %71.88 AU7 51/56 % AU25 52/59 %88.14 AU9 30/31 % AU26 6/10 % AU27 20/22 % Average 282/328 %85.98 Average 306/352 %86.93 Comparng to some related works [10, 11], results are encouragng. 6.2 Recognton of Sx Basc Facal Emotons n Image Sequences After classfyng facal expressons nto AUs, we tred to classfy them to basc emotons whch comprse happness, sadness, fear, dsgust, surprse, and anger. There s no unque AUs combnaton for these emotons. Based on manual FACS codes for Cohn-Kanade database, a rule-base s constructed to classfy facal expressons based on analyzed lower and upper face AUs. Table 3 shows ths rulebases and Table 4 shows classfcaton results. Table 3. Rule-bases for basc emotons classfcaton IF (AU23=1 OR AU24 =1) AND AU9=0 AU9=1 (AU20=1 AND AU25 =1) OR (AU20=1 AND AU26 =1) AU12=1 AU15=1 AND AU17 =1 AU27=1 OR (AU5=1 AND AU26 =1) THEN Anger Dsgust Fear Happness Sadness Surprse Table 4. Recognton rate of sx basc emotons n mage sequences Anger 27/30 90% Dsgust 30/30 100% Fear 30/30 100% Happness 30/30 100% Sadness 26/ % Surprse 30/30 100% Average 173/ % Comparng to some related works [11, 12, 13], results are encouragng.

11 302 H. Seyedarab et al. 6.3 Recognton of Upper and Lower Face AUs n Statc Images Our proposed system can not detect wrnkled nose (AU9) and dsgust expresson n statc mages. Last frame n mage sequences whch represents peak of facal expressons s used to tran and test of statc mages recognton system. We left out nput mages for dsgust expresson. We used the mages of 120 (80%) subjects as tranng sequences, and the remanng 30 (20%) subjects as test mages. Ths test s repeated fve tmes, each tme leavng dfferent subjects out. The number of the nput layer unts n the lower face PNN classfer s equal to 8, the number of extracted features, the number of the hdden layer unts equals to 120 9, the number of tranng pars and that of the output layers s 9, whch corresponds to selected 9 lower face AUs. The number of the nput layer unts n the upper face PNN classfer s equal to 8, the number of the hdden layer unts equals to120 6, and that of the output layers s 6, whch corresponds to the selected 6 upper face AUs. Table 5 shows the recognton rate of lower and upper face AUs. Comparng to some related works [14], results are resonable. Table 5. Recognton results for lower face AUs n statc mages Lower Face AUs Upper Face AUs AU12 31/35 %88.57 AU1 45/65 % AU15 26/29 %89.66 AU2 29/39 %74.36 AU17 46/60 %76.67 AU4 43/64 % AU20 18/30 %60.00 AU5 26/32 %81.25 AU23 18/26 %69.23 AU6 14/31 % AU24 18/30 %60.00 AU7 24/33 % AU25 42/55 % AU26 7/10 % AU27 22/22 % Average 228/297 %76.77 Average 181/264 % Recognton of Fve Basc Facal Emotons n Statc Images Table 6 shows recognton results for fve basc expressons (leavng out the dsgust expresson) usng the same rule-base (Table3) and lower and upper face AUs n statc mages. Table 6. Recognton rate of fve basc emotons n statc mages Anger 30/30 100% Fear 23/ % Happness 30/30 100% Sadness 29/ % Surprse 29/ % Average 141/ %

12 Facal Expressons Recognton n a Sngle Statc as well as Dynamc Facal Images Concluson In ths paper we developed an automatc facal expressons analyss and classfcaton systems wth hgh success rate. Our mage and vdeo analyss ncludes automatc feature detecton, trackng and the results are drectly used for facal emoton classfcaton based on AUs analyss and classfcaton. An average recognton rate of 96.11% was acheved for sx basc emotons n facal mage sequences. In the frst frame, 25 Facal Feature Ponts (FFPs) were automatcally detected, usng actve contours for lower face and gray level projecton method for upper face. A hybrd trackng system was used to track these FFPs n subsequent frames. An enhanced verson of actve contour trackng system was used for lp trackng whle a cross-correlaton based trackng system was used to track FFPs around eyes and eyebrows. Some geometrc features were extracted, based on the poston of FFPs n the frst and the last frames. Ths features formed a feature vector whch was used for classfcaton of nput mage sequences nto 16 AUs, usng PNN. A rule-based decson makng system was appled to AUs to classfy nput mages nto sx basc emotons. Proposed features and feature extracton method can also be appled to statc mages (except features for wrnkled nose) usng a local reference to normalze these features n order to remove the effect of subject-camera dstance. An average recognton rate of 94% was acheved for fve basc emotons n statc face mages. Whle most of the facal expresson recognton systems use manually located FFPs n the frst frame, our proposed system used automatcally detecton and trackng of feature ponts. Proposed hybrd trackng system showed robust trackng results both n upper and lower face, whch only needed the rough estmated poston of eye, eyebrow and mouth. Our proposed new features mproved AUs recognton rate as well as sx basc emotons recognton rate. Acknowledgements. The authors apprecate the support of Iranan Mnstry of Scence, Research and Technology and Iranan Telecommuncaton Research Center (ITRC) for ths research and would lke to thank J.F. Cohn and T. Kanade from Pttsburgh Unversty for ther kndly provdng the mage database. References 1. P. Ekman and W.V. Fresen, FACIAL ACTION CODING SYSTEM (FACS). Consultng Psychologsts Press, Inc., (1978). 2. M. Pantc and L.J.M. Rothkrantz, Expert system for automatc analyss of facal expressons, Journal of Image and Vson Computng 18(2000), , 3. J. J. Len, T. Kanade, J. F. Cohn and C. C. L Detecton, trackng and classfcaton of acton unts n facal expresson, Journal of Robotcs and Autonomous Systems 31 (2000), M. Valstar, I. Partas and M. Pantc, Facal Acton Unt Recognton Usng Temporal Templates, Proc. Of the IEEE nt. workshop on Robot and Human Interactve Communcaton, Japan (2004),

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