Automatic Facial Expression Recognition Using Neural Network

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1 Automatic Facial Epression Recognition Using Neural Network Behrang Yousef Asr Langeroodi, Kaveh Kia Kojouri Electrical Engineering Department, Guilan Universit, Rasht, Guilan, IRAN Electronic Engineering Department, Islamic Azad Universit of Nowshahr, Nowshahr, Mazandaran, IRAN Abstract - Computer intelligent reaction is a wa that human can use computer as an assistant. For mutual reaction between computer and human, the computer should learn human skills. An important skill is epression recognition (Sadness, Happiness, Surprise and Angr). Automatic recognition of facial epressions can be an important component of humanmachine interfaces; it ma also be used in behavioral science. This paper presents an automated facial epression recognition sstem using neural network classifiers. First all pictures have normalized manuall then we use the vertical and horizontal projections, cann edge detector, mathematical morpholog and point contour detection method to etract 30 facial characteristic points. Finall four facial epressions have been classified with a back propagation feed forward network. This sstem accurac rate is 9.5%. Kewords: Facial Epression; Feature Etraction; Face feature localization; Neural Network; Template Matching; Face projection. 1 Introduction Automatic facial epression recognition has man potential applications in areas such as human-computer interaction (HCI), emotion analsis, interactive video, indeing and retrieval of image and video data, image understanding, and snthetic face animation. Due to the increasing importance of computers in ever da life [1], HCI has become ver important in toda s societ. Most of the current HCI techniques rel on modalities such as, ke press, mouse movement, or speech input, and therefore do not provide natural humanto-human-like communication. The information contned in facial epressions, ee movement, hand movement, etc., is usuall ignored. Developing a sstem which could detect the presence of humans (using face detection), determine their identit (using face, voice, or audio-visual person recognition), and understand their behavior (using facial epression analsis, audiovisual speech recognition, etc.) in order to respond to their needs or requests, would significantl improve performance of HCI sstems. Automatic facial epression analsis is an important part of such a sstem. Human faces contn significant information about emotions and the mental state of a person that can be utilized in order to enable non-verbal communication with computers. Scientific stud of facial epressions began with the team led b Ekman []. The analzed si facial epressions, which included surprise, fear, disgust, anger, happiness, and sadness. Each epression was summarized b distinctive clues in the appearance of the eebrows, ees, mouth, jaw, etc. These facial epression clues are further investigated and encoded into the so-called Facial Action Coding Sstem (FACS) describe all visuall distinguishable facial movements. FACS enumerates Action Units (AUs) of a face that cause facial movements. In FACS, there are 46 AUs that account for changes in facial epression. The combination of these action units results in a large set of possible facial epressions. This paper proposes an automated facial epression recognition sstem using neural network. The rest of this paper is organized as follows In Section feature etraction modules for facial epression recognition are introduced. First, we briefl use vertical and horizontal projection to obtn the boundaries of the facial features including eebrow, ee, and mouth. In the sequel, the cann and morphological edge detector for precise local estimation of the ees and the mouth is introduced. Facial characteristic points of a face and normalization are then introduced. Net facial epression classifier, which is implemented b a feed forward back propagation network, is proposed using these 30 point inputs. The data is presented in Section 4. Finall, the conclusion is given in Section 5. Feature etraction Facial feature etraction is d on the observation that facial features differ from the rest of the face [3]. Therefore, facial features are determined b searching for minimum in the projection of piel gra values. There are two kinds of projection, X-projection and Y-projection that are computed b considering the average of piel values of the segmented face region along the vertical direction (columns) and along the horizontal direction (rows), respectivel. It is assumed that each facial feature generates a minimum in Y-projection and has particular X-projection characteristics. In Fig.1 Y-projection is shown each minimum is related to a facial feature. Also corresponding X-projections are shown. Note that nose

2 line is the line that passes through the nose and it acts as the Y ais for the face. It is epected that the first significant minimum on Y-Relief correspond to the eebrows, the second minimum correspond to the ees, third to nostrils, fourth to mouth, and the last to chin [4]. The position of a minimum in Y-projection along with the shape of corresponding X-projection is used to etract of facial feature. In Y-projection, usuall the number of minima is greater than the number of features. Thus, for robustness, we used a minimum distance between minimum point and previous maimum in Y-projection. mouth and eebrows. The vertical size of rectangles is obtned eperimentall. Net 30 facial feature points are etracted from face (8 point for each ee, 3 point for each eebrow and 8 point for mouth). Before performing feature etraction, the edge image b the morphological technique is generated. Two operations is used a dilation and an erosion. Eq.1 shows morphological edge detection: Edge Dilation( I ) Erosion( I) (1) Furthermore relative positions of facial features with respect to each other are used as well. After etensive eperimentation with a trning set of face images, a characteristic (tpical) X-projection is derived for each facial feature. But in X-projection for ees and mouth, edge image (cann detector) is used to obtn more accurac, then distance between two maimums with a constant margin determine horizontal area of ees and mouth. Result is shown in Fig.. [5] Figure 3. Segmentation result for eebrows, ees and mouth. The result of this operation on image has shown in Fig.4. Figure 4. Morphological edge detection Figure 1. Y- and X-projections for a sample image. Figure. X-projection for mouth and ees Fig. 3 shows segmentation result for eebrows, ees and mouth. White rectangles are drawn around ees, As mentioned above 8 feature points is assumed for each ee to etracting points from ees we choose four points on the upper side and four points on the lower side of rectangle of ee on the edge image. Intensit of each point is taken zero, it means black piel. Each of the four landmark points on the upper boundar will move downward graduall to the position which gives the maimum intensit difference. The lower four landmark points will be relocated in a similar manner. Threshold value is chosen 75. We choose eight points for mouth and three points for each eebrow and continue in similar manner. Location of points is determined with facial characteristic points that are shown in Fig. 5; these points are chosen to pa attention to movement in epression face and to reach best curve fitness. a i is a vector defining the coordinate of the i-th FCP (facial characteristic point), i.e., is described as ( X, Y ) i 1,,...,30 () a i a i

3 As shown in Fig. 5, the a a coordinate sstem is the absolute coordinate sstem, with its origin being chosen as a point on the length,, downward the mid point between the left and right ees, which is almost the same center used for image alignment in [6]. Parameter describe the distance between the two ees and is given b: ( a a 1) + ( a a 1) (3) Let (0, 0) be the coordinate of the mid point between the left and right ees, namel, o o + a + a. (5) From Fig. 6, the origin of the new coordinate sstem is then calculated as O O sin( θ ) cos( θ ) (6) The coordinate (, ) of an FCP is transformed into the " " coordinate sstem, first b translating the origin to the coordinates of Eq. (6) above and then rotating an angle of orientation. The relationships to implement this affine transform are given b successivel eecuting the following two sets of equations: Figure 5. Location of feature points It is a length normalization factor so that each image length will be normalized b to make ever face image instance have a length of unit between the two ees. This normalization factor is also emploed in [7] [8]. Furthermore, to offset the 3-D pose of the head in a picture, we introduce a coordinate rotation angle, which is the inclination of the face and is specified b the line of the two ees with respect to the horizontal, defined b 1 θ tan a (4) a cos( θ ) + sin( θ ) sin( θ ) + cos( θ ) Finall to normalize the input face image, dividing (, ) b gives i 1,,...,30 3 Classif of facial epression b neural network In this paper feed forward propagation network was used to classif four epressions (angr, surprised, happ and neutral). (7) (8) This Network has contned of two laers, the mid laer has 10 neurons and the output laer has 4 neurons. Sigmoid function was used for mid laer Eq. 9 and Fig.7 shows this function. [9][10] out e NET (9) Finall learning algorithm is supposed to Levenberg- Marquart and the number of epochs was determined Figure 6. New coordinate sstem

4 Fig.8 shows network structure in MATLAB. As ou see size of input vector is 60 it causes of 30 normalized feature points on face( and coordinate). Output laer has four neurons that each neuron shows an epression. Table 1 shows Assignments. Table 1. Epression Assignment Neuron Epression Angr Happ Surprised Neutral Figure7. Sigmoid function Figure 9. Simulation using Matlab Figure 8. Neural network Simulation result using Matlab is shown in Fig. 9. Charts in Fig.9 show classifier output. Number of bars is related to epression according to Table 1. 4 Data In this paper Japanese female face epression data (JAFFE) has used to epression recognition. The resolution of images was piels. There were 115 face images collected in our face image data, which includes ten different female persons and each person poses for several neutral, anger, surprised and happiness epressions. Sample of data are shown in Fig Conclusion Figure 10. Data sample First of all we used vertical integral projection to localize vertical region of eebrows, ees and mouth with hierarchical algorithm then using of horizontal projection and edge detection we found horizontal area of features. After that with the m of morphological edge detection, minimizing error and interpolation, 30 feature points etracted. Net feature points rotated and normalized in

5 new coordination sstem. Finall four epressions classified using neural classifier. In the conclusion the sstem accurac has shown in Table. In comparison our sstem is faster than some sstems that use Gabor wavelet as feature because we use principle point without using of so man inputs to network and PCA. Table. Accurac rate Epression Accurac rate Angr 94.4% Happ 94.4% Surprised 86.6% Neutral 94.4% 6 References [1] B.Fasel and J.Luettin, Automatic Facial Epression Analsis: A Surve, Pattern Recognition, vol. 36,no.,003,pp [] P. Ekman and W.V. Friesen, "The Facial Action Coding Sstem", San Francisco: Consulting Pschologist Press, [3] Sobottka, K., Pitas, I., "Etraction of facial regions and features using color and shape information", in: 13 th International Conference on Pattern Recognition, Vienna, Austria, August 1996, pp [4] Sharmne V. Cerez, "Facial Feature Detection using a Geometric Face Model", CMSC 190 Special Problem, Institute of Computer Science, ICS Universit of the Philippines Los Banos, 007 [5] Gonzalez, R.C., and Woods, R.E., Digital Image Processing (Addison Wesle, Reading, MA, 199). [6] H. Kobaashi, and F. Hara, Recognition of Si Basic Facial Epressions and Their Strength b Neural Network, IEEE International Workshop on Robot and Human Communication, New York, NY., pp , 199. [7] C.L. Huang and C.W. Chen, Human Facial Feature Etraction for Face Interpretation and Recognition, Pattern Recognition, Vol. 5, No. 1, pp , 199. [8] Zhang, Z., Feature-Based Facial Epression Recognition: Sensitivit Analsis and Eperiments with a Multilaer Perceptron, International Journal of Pattern Recognition and Artificial Intelligence, Vol. 13, No. 6, pp , [9] H. Kobaashi and F. Hara, Analsis of the neural network recognition characteristics of si basic facial epressions, 3 rd IEEE International Workshop on Robot and Human Communication, New York, NY., pp. -7, [10] Anil K. Jn, K. M. Mohiuddin and Jiangchang Mao Artificial Neural Networks a Tutorial IEEE TRANSACTION, pp 31-44, 1996.