DETECTING FACIAL EXPRESSION IN IMAGES

Journal for Research | Volume 02 | Issue 02 | April 2016 ISSN: 2395-7549

Detecting Facial Expression in Images Ghazaala Yasmin M.Tech. Student Department of Computer Science & Engineering University of Calcutta

Prof. Samir K. Bandyopadhyay Professor Department of Computer Science & Engineering University of Calcutta

Abstract Now days, the task of face recognition is widely used application of image analysis as well as pattern recognition. In biometric area of the research, automatically face & face expression recognition attracts researcher’s interest. For classifying facial expressions into different categories, it is necessary to extract important facial features which contribute in identifying proper and particular expressions. Recognition and classification of human facial expression by computer is an important issue to develop automatic facial expression recognition system in vision community. In this paper the facial expression recognition system is proposed.

Keywords: Facial feature detection, Template matching and Face position detection _______________________________________________________________________________________________________ I.

INTRODUCTION

Facial expression recognition [1] [2] is another fruitfulness of computer vision research. Computer vision is the way to electronically represent the human vision with the help of some data analysis techniques. In a human computer interaction (HCI) system, the communication between human and computer can take place through different aspects (like verbal, non-verbal) of a human. Here we are considering only the non-verbal aspects of human being like, facial expression, body movement etc. In this paper we are mainly concern about the facial expression recognition process, which needs a face image on which we should apply our facial expression recognition algorithm. Now once we have the face image data, we need to apply some processing techniques with the help of pattern recognition, artificial intelligence, mathematics, computer science, electronics or any kind of scientific concept. Hence there are huge numbers of applications in computer vision research, but we will discuss only face recognition and facial expression.

There are many applications, where facial expression detection process plays an important role. Researches in the field of social psychology show that facial expression are more natural in nature than the speaker’s spoken words and truly reflects the emotion of a person. According to statistical reports verbal part of a message contributes only for 7 percent to the effect of the message as a whole. The vocal part contributes for 38 percent, while facial expression of the speaker contributes for 55 percent to the effect of the spoken message. The facial expression recognition system was introduced in 1978 by Suwa et. al [4]. The main issue of building a facial expression recognition system is face detection [3] and alignment, image normalization, feature extraction, and classification.

The analysis of the human face via image (and video) is one of the most interesting and focusing research topics in the last years for the image community. From the analysis (sensing, computing, and perception) of face images, much information can be extracted, such as the sex/gender, age, facial expression, emotion/temper, mentality/mental processes and behaviour/psychology, and the health of the person captured. According to this information, many practical tasks can be performed and completed; these include not only person identification or verification (face recognition), but also the estimation and/or determination of person's profession, hobby, name (recovered from memory), etc.

Research on face image analysis has been carried out and is being conducted around various application topics, such as (in alphabetical order) age estimation, biometrics, biomedical instrumentations, emotion assessment, face recognition, facial expression classification, gender determination, human-computer/human-machine interaction, human behaviour and emotion study, industrial automation, military service, psychosis judgment, security checking systems, social signal processing, surveillance systems, sport training, tele-medicine service, etc.

Therefore facial expressions are the most important information for emotions perception in face to face communication. This paper explains about an approach to the problem of facial feature extraction from a non-l frontal posed image For face portion segmentation basic image processing operation like morphological dilation, erosion, reconstruction techniques with disk structuring element are used. Six permanent Facial features like eyebrows(left and right), eye (left and right) , mouth and nose are extracted using facial geometry, edge projection analysis and distance measure and feature vector is formed considering height and width of left eye, height and width of left eyebrow, height and width of right eye, height and width of right eyebrow, height and width of nose and height and width of mouth along with distance between left eye and eyebrow, distance between right eye and eyebrow and distance between nose and mouth.

Human face detection has drawn considerable attention in the past decades as it is one of the fundamental problems in computer vision. Given a single image, the ideal face detection should identify and locate all faces regardless of its threedimensional position, orientation, and lighting conditions. The existing face detection techniques can be classified into four

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categories, namely, knowledge-based methods, feature invariant approaches, template matching methods, appearance based methods.

Human face detection and segmentation is an active research area until recently. This field of research plays an important role in many applications such as face identification system, face tracking, video surveillance and security control system, and human computer interface.

Those applications often require segmented human face which is ready to be processed. There are many factors that influence the success of human face detection and segmentation. Those factors include complex colour background, condition of illumination, change of position and expression, rotation of head, and distance between camera and subject.

Face detection is a sub branch of object detection. The human face is a dynamic object and has a high degree of variability in its appearance, which makes face detection a difficult problem in computer vision.

Images containing faces are essential to intelligent vision-based human computer interaction, and research efforts in face processing include face recognition, face tracking, pose estimation, and expression recognition. However, many reported methods assume that the faces in an image or an image sequence have been identified and localized. To build fully automated systems that analyse the information contained in face images, robust and efficient face detection algorithms are required.

Given a single image, the goal of face detection is to identify all image regions which contain a face regardless of its threedimensional position, orientation, and lighting conditions. Such a problem is challenging because faces are non- rigid and have a high degree of variability in size, shape, colour, and texture. Numerous techniques have been developed to detect faces in a single image.

Face detection and localization is the task of checking whether the given input image contains any human face, and if so, returning the location of the human face in the image. The wide variety of applications and the difficulty of face detection have made it an interesting problem for the researchers in recent years.

Face detection is difficult mainly due to a large component of non-rigidity and textural differences among faces. The great challenge for the face detection problem is the large number of factors that govern the problem space. The long list of these factors include the pose, orientation, facial expressions, facial sizes found in the image, luminance conditions, occlusion, structural components, gender, ethnicity of the subject, the scene and complexity of image’s background.

The scene in which the face is placed ranges from a simple uniform background to highly complex backgrounds. In the latter case it is obviously more difficult to detect a face. Faces appear totally different under different lighting conditions. Not only do different persons have different sized faces, faces closer to the camera appear larger than faces that are far away from the camera Basic emotions are emotions that have been scientifically proven to have a certain facial expression associated with it. Different emotional stages are indicated as follows.

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Facial Feature Detection system is fast becoming a familiar feature in ‘apps’ and on websites on different purposes. Human face identification and detection is often the first step in applications such as video surveillance, human computer interface, and face recognition and image database management [1]. Furthermore facial feature characteristics are very much effective in both biometric identification which automatically identifies a person from a digital image or a video image. Facial expressions are used not only to express our emotions, but also to provide important communicative cues during social interaction, such as our level of interest [2]. Facial features, eye feature has more application domain. It is reported that facial expressions have considerable effects on listener, near about 55 % effect of the spoken words depend on eye movements and facial expressions of the speaker.

Facial expressions play a major role in Face Recognition Systems and image processing techniques of Human Machine Interface. There are several techniques for facial features selection like Principal Component Analysis, Distance calculation among face components, Template Matching. This algorithm describes a simple template matching based facial feature selection technique and detects facial expressions based on distances between facial featuresusing a set of image databases. The process for facial expression recognition system involves three stages: Pre Processing, Facial Feature Extraction and classification facial expressions.

II. REVIEW WORKS Human face detection has drawn considerable attention in the past decades as it is one of the fundamental problems in computer vision. Given a single image, the ideal face detection should identify and locate all faces regardless of its three-dimensional position, orientation, and lighting conditions. The existing face detection techniques can be classified into four categories, namely, knowledge-based methods, feature invariant approaches, template matching methods, appearance based methods.

The use of colour information has been introduced to the face-locating problem in recent years. Most publications [1-5] have shown that colour is a powerful descriptor that has practical use in the extraction of the face detection.

Modelling skin colour requires choosing an appropriate colour space and identifying a cluster associated with skin colour in this space. YIQ colour space is used in commercial colour television broadcasting. YCbCr space is a hardware orientated model and is used in most video standards. So an effective use of the chrominance information for modelling human skin colour can be achieved in these colour space. Second, this format is typically used in video coding, and therefore the use of the same format for segmentation will avoid the extra computation required in conversation. Many research studies [6- 8] assume that chrominance components of skin-tone colour are independent of the luminance component. In fact, the skin-tone colour is non-linearly dependent on luminance. Researcher found that although skin colours of different people appear to vary over a wide range, they differ less in chrominance than brightness, specially the skin colours from a compact area in the YCbCr plane [9-10].

Human-like robots and machines that are expected to enjoy truly intelligent and transparent communications with human can be created using automatic facial expression recognition with a set of specific desired accuracy and performance requirements.

Expression recognition involves a variety of subjects such as perceptual recognition, machine learning, affective computing etc.

One case study uses skin colour range of human face to localize face area. After face detection, various facial features are identified by calculating the ratio of width of multiple regions in human face. Finally the test image is partitioned into a set of

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Detecting Facial Expression in Images (J4R/ Volume 02 / Issue 02 / 006)

sub-images and each of these sub-images is matched against a set of sub-pattern training set. Partitioning is done using AwSpPCA algorithm. Given as input any emotion of face, this pattern training set will classify the particular emotion [1].

Face component extraction by dividing the face region into eye pair and mouth region and measurement of Euclidean distance among various facial features is also adopted by a case study. Similar study is done by Neha Gupta to detect emotions. This research includes four steps: pre-processing, edge detection, feature extraction and distance measurement among the features to classify different emotions. This type of approach is classified as Geometric Approach [3].

Another research includes Face detection method using segmentation technique. First, the face area of the test image is detected using skin colour detection. RGB colour space is transformed into yCbCr colour space in the image and then skin blocks quantization is done to detect skin colour blocks. As next step, a face cropping algorithm is used to localize the face region. Then, different facial features are extracted using segmentation of each component region (eyes, nose, mouth). Finally, vertical & angular distances between various facial features are measured and based on this any unique facial expression is identified. This approach can be used in any biometric recognition system [2].

A template matching based facial feature detection technique is used in a different case study [4,7-8]. Different methods of face detection and their comparative study are done in another review work. Face detection methods are divided into two primary techniques: Feature based & View based methods [5, 9].

Gabor filters are used to extract facial features in another study. This approach is called Appearance based approach. This classification based facial expression recognition method uses a bank of multilayer perceptron neural networks. Feature size reduction is done by Principal Component Analysis (PCA) [6, 10]. Thus existing works primarily focused in detecting facial features and they are served as input to emotion recognition algorithm. In this study, a template based feature detection technique is used for facial feature selection and then distance between eye and mouth regions is measured.

III. FACIAL EXPRESSION RECOGNITION SYSTEM AND RESULTS In recent technology we have seen that how the advance image processing techniques with the help of pattern recognition and artificial intelligence can be effectively used in automatic detection and classification of various facial signals. Among them face recognition and facial expression recognition are the best ones to describe the concept of man-machine interaction efficiently. In both of these techniques we are doing patter recognition. For example, in face recognition we consider two patterns ‘known’ and ‘unknown’ whereas in facial expression recognition we consider five patterns ‘neutral’, ‘happy’, ‘sad’, ‘angry’, ‘disgust’ etc.

Facial expression recognition can be used in behavior monitor system and medical system. In this paper we will show how the concept of face recognition with the help of neural network can be used in facial expression recognition process. The following figure shows the concept of a typical facial expression recognition system.

Figure 1: Block diagram of a typical facial expression recognition system.

At first we need to acquire the image on which we will apply our facial expression recognition techniques. Input image can be captured by any kind of imaging system. If the Input Image is colour (RGB), then convert it to Gray scale image.

The input image can be of different size, format, colour (RGB or gray) etc. Hence we should preprocess the input image, so that we can efficiently apply our algorithm to get better result. In the preprocessing technique we use some compression technique like 2D-DCT to compress the data, because an image consists a large number of data, which increases the computation time. Also, we can apply some filtering techniques to remove the noise from the input image, because the presence of any artifacts can lead to false detection of facial features, which could produce wrong output.

As in all imaging process, artifacts can occur, resulting in degraded quality of image which can compromise imaging evaluation. An artifact is a feature appearing in an image that is not present in the original object. Artefacts remain a problematic area and it affects the quality of the image. Pre-processing (artefact removal) techniques are used to improve the detection of the unwanted portion from the given images.

Algorithm for Artefact Removal: 1) Step 1. Grayscale facial images are taken as input.

2) Step 2.Threshold value of the image is calculated using the standard deviation technique described above.

3) Step 3. The image is binarized using the threshold value. i.e. pixels having value greater than the threshold is set to 1 and pixels less than the threshold are set to 0.

4) Step 4. The binarized image is labelled and areas of connected components are calculated using equivalence classes.

5) Step 5. The connected component with the maximum area and the connected component with the second highest area are found out.

6) Step 6. The ratio of the maximum area to that of second maximum area are calculated.

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Detecting Facial Expression in Images (J4R/ Volume 02 / Issue 02 / 006)

7) Step 7. On the basis of the ratio if ratio is high only the component with highest area is kept and all others are removed otherwise if ratio is low the component with the highest and second highest area are kept and all others are removed.

8) Step 8. A convex hull is calculated for the one pixel in the image and all regions within the convex hull are set to one.

9) Step 9.Now the above obtained image matrix is multiplied to the original image matrix to obtain an image consisting of only medical image without any artefact.

In RGB images each pixel has a particular colour; that colour is described by the amount of red, green and blue in it. If each of these components has a range 0–255, this gives a total of 256^3 different possible colours. Such an image is a “stack” of three matrices; representing the red, green and blue values for each pixel. This means that for every pixel there correspond 3 values.

Whereas in greyscale each pixel is a shade of gray, normally from 0 (black) to 255 (white). This range means that each pixel can be represented by eight bits, or exactly one byte. Other greyscale ranges are used, but generally they are a power of 2.so,we can say gray image takes less space in memory in comparison to RGB images.

Edge detection refers to the process of identifying and locating sharp discontinuities in an image. The discontinuities are abrupt changes in pixel intensity which characterize boundaries of objects in a scene. Edges characterize boundaries and are therefore a problem of fundamental importance in image processing and an important tool for image segmentation. The concept of edge is highly useful in dealing with regions and boundaries as an edge point is transition in gray level associated with a point with respect to its background. Edges typically occur on the boundary between two regions. The following algorithm is used for edge detection as pre-processing step.

Algorithm for Edge Detection: Basic functions are defined which are used in algorithms: Parent (i) Return ⌊i/2⌋ Left (i) Return 2i Right (i) Return 2i+1 Total number of nodes in a Complete Binary Tree tNode (h) Return 2h-1 The number of terminal nodes (leaf nodes) in a Complete Binary Tree lNode (h) Return 2h-1 The number of internal nodes (non-leaf nodes) in a Complete Binary Tree iNode (h) Return tNode (h) – lNode (h) Algorithms for: Storing Original Colour Space at Leaf Nodes of Tree ORIGINAL-HISTOGRAM (Image, height, width) Loop x← 1 to height Do Loop y← 1 to width Do Intensity ← Image [x, y] Tree [(iNode (h) + 1) + Intensity].count ← Tree [(iNode (h) + 1) + Intensity].count + 1 x←x +1 y←y +1 Return Tree Algorithms for: generate quantize colour spaces in different level of tree LEVEL-HISTOGRAM (Tree) Loop x ⟵ iNode (h) + 1 To tNode (h) Do Lcount ⟵ Tree (x).count Loop y ⟵ Parent (x) down To 0 Do If x mod 2 ≠ 0 Then Tree [y].intensity ⟵ Tree [x].intensity Tree [y].count ⟵ Tree [y].count + Lcount Else If Tree [y].count ˂ Tree [x].count ThenTree [y].intensity ⟵ Tree [x].intensity Tree [y].count ⟵ Tree [y].count + Lcount x⟵y

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Detecting Facial Expression in Images (J4R/ Volume 02 / Issue 02 / 006)

y ⟵ Parent (x) x⟵x+1 Return Tree Algorithms to: Calculate the Average Bin Distance BIN-DISTANCE (Tree, h1) TotBin ⟵ 0 TotBinDist ⟵ 0 Loop x ⟵ iNode (h1) + 2 to tNode (h1) Do TotBin ⟵ TotBin + 1 TotBinDist ⟵TotBinDist + (Tree [x] .intensity - Tree[x - 1] .intensity) x⟵x+1 AvgBinDist ⟵ TotBinDist / TotBin Return AvgBinDist Algorithms for: Calculation of MDT by Identifying the Prominent Bins and Truncate the Non-Prominent Bins CALCULATE-MDT (Tree, h1) Tree [iNode (h1) + 1].prominent ⟵1 TotPrmBin ⟵ 0 TotPrmBinDist ⟵ 0 Loop x ⟵ iNode (h1) + 2 to tNode (h1) Do If Tree [x] .intensity - Tree [x - 1] .intensity ≥ AvgBinDist ThenTree[x].prominent ⟵ 1 TotPrmBin ⟵ TotPrmBin + 1 TotPrmBinDist ⟵ TotPrmBinDist + (Tree [x] .intensity - Tree [x - 1] .intensity) Else Tree[x].prominent ⟵ 0 x⟵x+1 MDT ⟵ TotPrmBinDist / TotPrmBin Return MDT Algorithms for: Redraw the Image Using Truncated Histogram REDRAW - IMAGE (Image, height, width, Tree, h1, h) Loop x ⟵ 1 to height Do Loop y ⟵ 1 to width Do NewIntensity ⟵ (Image [x, y] / (tNode (h) / tNode (h1))) + 1 If Tree[iNode (h1) + NewIntensity + 1].prominent ≠1 Then While Tree [iNode (h1) + NewIntensity + 1].prominent ≠1 Do NewIntensity ⟵ NewIntensity – 1 NewImage [x, y] ⟵ NewIntensity y⟵y+1 x⟵x+1 Return NewImage Algorithms for: derive the Horizontal Edge of the image HozEdgeMap (NewImage, height, width, MDT) Loop x ⟵ 1 to height Do flag⟵ 1 Loop y ⟵ 1 to width Do If Flag = 1 Then NewIntensity ⟵NewImage [x, y] NxtNewIntensity ⟵NewImage [x, y] If |NewIntensity – NxtNewIntensity |≥ MDT Then Flag ⟵ 1 HozEdgeMapImage [x, y] ⟵ BLACK Else Flag ⟵ 0 HozEdgeMapImage [x, y] ⟵ WHITE y ⟵y + 1 x⟵ x + 1 Return HozEdgeMapImage All rights reserved by www.journalforresearch.org

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Detecting Facial Expression in Images (J4R/ Volume 02 / Issue 02 / 006)

Algorithms for: derive the Edge of the image EDGEMAP (HozEdgeMapImage, VerEdgeMapImage, height, width) Loop x ⟵ 1 to height Do Loop y ⟵ 1 to width Do EdgeMapImage [x, y] ⟵HozEdgeMapImage [x, y] OR VerEdgeMapImage [x, x⟵x+1 Return EdgeMapImage The results obtained are shown Figure 2.

(a) Fig. 2: (a) Template of the Face

(b) (b) Edge Detection of Side Face

y]

y⟵y+1

(c) (c) Edge Detection of Front Face

Once we acquire the face image, we need to extract the facial features from the background of the image. In this paper we use skin colour based face detection technique, which uses RGB and HSV colour model. There are another colour model (YCbCr) that can also be used to detect skin colour region.

We use 2D-DCT to compress the extracted facial feature, which can make our processing faster. As our algorithm uses an image database, we have to apply the compression technique in all the images in the database. An example image is shown in the figure 5 on which we want to execute our proposed algorithm.

Fig. 3: Original image containing a single frontal viewed face.

Fig. 4: Image in HSV colour space Fig. 5: Extracted skin colour region from the image

Fig. 6: Binary image showing region boundary Fig. 7: input Image The following algorithm extracts facial features.

Fig. 8: Detected Face Region

Facial_Feature_Detection (Input Image, Template Images) Step1. Start Step2. Read Input Human Face Image.

If the Input Image is colour (RGB), then convert it to Gray scale Image and save the pixel values to a 2D array let gface.

Else save the pixel values of the input image to a 2D array let gface.

Step3. Read Left eye template image.

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Detecting Facial Expression in Images (J4R/ Volume 02 / Issue 02 / 006)

If the template image is colour (RGB), then convert it to Gray scale Image and save the pixel values to a 2D array let gleft.

Else save the pixel values of the input image to a 2D array let gleft.

Step4. Read Right eye template image.

If the template image is colour (RGB), then convert it to Gray scale Image and save the pixel values to a 2D array let gright.

Else save the pixel values of the input image to a 2D array let gright.

Step5. Read Nose template image.

If the template image is colour (RGB), then convert it to Gray scale Image and save the pixel values to a 2D array let gnose.

Else save the pixel values of the input image to a 2D array let gnose.

Step6. Read Mouth template image.

If the template image is colour (RGB), then convert it to Gray scale Image and save the pixel values to a 2D array let gmouth.

Else save the pixel values of the input image to a 2D array let gmouth.

Step7. Declare 4 2D Array C1, C2, C3 & C4 of size m*n where m*n is the size of gface.

Step8. Calcualte C1[][] = 2D_norm_crosscorr(gleft,gface) C2[][]= 2D_norm_crosscorr(gright,gface) C3[][] = 2D_norm_crosscorr(gnose,gface) C4[][] = 2D_norm_crosscorr(gmouth,gface) Step9. Call (x11,y11,w1,h1) = Find_max(C1) (x21,y21,w2,h2) = Find_max(C2) (x31,y31,w3,h3) = Find_max(C3) (x41,y41,w4,h4) = Find_max(C4) where (x11,y11,w1,h1), (x21,y21,w2,h2), (x31,y31,w3,h3), (x41,y41,w4,h4) are top – left pixel coordinate, width, height of the matched rectangular area around left eye, right eye, nose and mouth respectively.

Step10. Calculate x12 = x11 + w1 & y12 = y11 + h1 x22 = x21 + w2 & y22 = y21 + h2 x32 = x31 + w3 & y32 = y31 + h3 x42 = x41 + w4 & y42 = y41 + h4 where (x12,y12), (x22,y22), (x32,y32), (x42,y42) are bottom right pixel coordinate of the matched rectangular area around left eye, right eye, nose and mouth respectively.

Step11. Draw Boundary Rectangle around left eye in gface with top left, top right, bottom left and bottom right pixel coordinates as (x11,y11), (x12,y11), (x11,y12) & (x12,y12) respectively.

Draw Boundary Rectangle around right eye in gface with top left, top right, bottom left and bottom right pixel coordinates as (x21,y21), (x22,y21), (x21,y22) & (x22,y22) respectively.

Draw Boundary Rectangle around nose in gface with top left, top right, bottom left and bottom right pixel coordinates as (x31,y31), (x32,y31), (x31,y32) & (x32,y32) respectively.

Draw Boundary Rectangle around mouth in gface with top left, top right, bottom left and bottom right pixel coordinates as (x41,y41), (x42,y41), (x41,y42) & (x42,y42) respectively.

Calculate middle point pixel coordinate (x1mid,y1mid) of the boundary rectangle around Left eye as x1mid = (x11+x12)/2 and y1mid = (y11+y12)/2.

Step12. Calculate Euclidian Distance between middle point pixel coordinate (x1mid,y1mid) of the boundary rectangle around left eye and top – left pixel coordinate (x41,y41) of the boundary rectangle around mouth as: Dist1 = √{( x1mid – x41)2 + ( y1mid - y41)2} unit.

Step13. Calculate middle point pixel coordinate (x2mid,y2mid) of the boundary rectangle around right eye as x2mid = (x21+x22)/2 and y2mid = (y21+y22)/2.

Step14. Calculate Euclidian Distance between middle point pixel coordinate (x2mid,y2mid) of the boundary rectangle around right eye and top – right pixel coordinate (x42,y41) of the boundary rectangle around mouth as: All rights reserved by www.journalforresearch.org

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Detecting Facial Expression in Images (J4R/ Volume 02 / Issue 02 / 006)

Dist2 = √{( x2mid – x42)2 + ( y2mid - y41)2} unit.

Step15. Write the value of Dist1 and Dist2 in a output text file for comparison.

Step16. Repeat step 1 to 15 for another same human face but with smiling facial expression.

Step17. Compare both input face images according the distances measured between eyes & mouth. The image with larger distance is considered as Happy face or smiling face, in general,.

Step18. Exit 2D_Norm_Crosscorr (Template Gray scale Image, Input Gray scale Image) Step1. Start Step2. Perform 2D Cross Correlation between Template Image and Input Image pixel values and return 2D array C of size m*n with values of the corresponding Cross correlation where m*n is the size of the Input Image.

Step3. End Find_Max(C[ ][ ]) Step1. Start Step2. Find Maximum Value of 2D Array C[ ][ ] and determine the corresponding rectangular region where the maximum value is found.

Step3. Find top – left position coordinate (x,y), width (w) and height (h) of the rectangular region and return the values.

Step4. End Facial_expression_recognition (Input Image, 3 Training Image Databases) Step19. Start Step20. Read Input Human Face Image and Store the pixel values to an array let face.

Step21. Call Processed_Face = imPreprocess(face) Step22. Set i=1 Step23. Repeat Step 6 to 9 for every Image of Train_Neutral_Other Image Database Step24. Read the Image from the Database and Store the pixel values to an array let t.

Step25. Call t1= imPreprocess(t) Step26. Store t1 into image cell Train_Neutral_Other_Cell as Train_Normal_Other_Cell(1,i)=t1 Step27. Set i=i+1 Step28. Set i=1 Step29. Repeat Step 12 to 15 for every Image of Train_Smiling_Other Image Database Step30. Read the Image from the Database and Store the pixel values to an array let t.

Step31. Call t1= imPreprocess(t) Step32. Store t1 into image cell Train_Smiling_Other_Cell as Train_Smiling_Other_Cell(1,i)=t1 Step33. Set i=i+1 Step34. Set i=1 Step35. Repeat Step 18 to 21 for every Image of Train_Angry_Sad Image Database Step36. Read the Image from the Database and Store the pixel values to an array let t.

Step37. Call t1= imPreprocess(t) Step38. Store t1 into image cell Train_Angry_Sad_Cell as Train_Angry_Sad_Cell(1,i)=t1 Step39. Set i=i+1 Step40. Create 3 2D Array of size (no_of_images * mn) for 3 Training Databases where no_of_images refers to the total number of images in the corresponding training databases respectively and m,n refers to the predefined size mentioned in Impreprocess function. Let traindata1 (n1 *mn), traindata2 (n2*mn) and traindata3 (n3*mn) are 3 arrays for Train_Neutral_Other, Train_Smiling_Other and Train_Angry_Sad Training Image Databases respectively, with n1, n2 and n3 are number of images in the corresponding databases.

Step41. Initialize all elements of traindata1, traindata2 and traindata3 array to 0.

Step42. Set i=1 Step43. Repeat step 26 to27 for n1 times Step44. Set traindata1(i,:)= Train_Normal_Other_Cell(1,i)

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Detecting Facial Expression in Images (J4R/ Volume 02 / Issue 02 / 006)

Step45. Set i=i+1 Step46. Set i=1 Step47. Repeat step 30 to31 for n2 times Step48. Set traindata2(i,:)= Train_Smiling_Other_Cell(1,i) Step49. Set i=i+1 Step50. Set i=1 Step51. Repeat step 34 to35 for n3 times Step52. Set traindata3(i,:)= Train_Angry_Sad_Cell(1,i) Step53. Set i=i+1 Step54. Create 3 1D Arrays, namely class1, class2 and class3 of size n1,n2 and n3 respectively corresponding to Train_Neutral_Other, Train_Smiling_Other and Train_Angry_Sad Training Image Databases respectively.

Step55. Set i=1 Step56. Repeat Step 39 to 40 for all images of Train_Neutral_Other Image Database Step57. If ith image of Train_Neutral_Other is of Neutral expression then set class1(i)= 1 else set class1(i)= -1 Step58. Set i=i+1 Step59. Set i=1 Step60. Repeat Step 43 to 44 for all images of Train_Smiling_Other Image Database Step61. If ith image of Train_Smiling_Other is of Smiling expression then set class2(i)= 2 else set class2(i)= -2 Step62. Set i=i+1 Step63. Set i=1 Step64. Repeat Step 47 to 48 for all images of Train_Angry_Sad Image Database Step65. If ith image of Train_Angry_Sad is of Angry expression then set class3(i)= 3 else set class3(i)= -3 Step66. Set i=i+1 Step67. Call SVMTrained1=SVM_Training(traindata1,class1) SVMTrained2=SVM_Training(traindata2,class2) SVMTrained3=SVM_Training(traindata3,class3) Step68. Call result1=SVM_Classify(SVMTrained1, Processed_Face) result2=SVM_Classify(SVMTrained2, Processed_Face) result3=SVM_Classify(SVMTrained3, Processed_Face) Step69. Call FinalExpression=Recognize_Expression(result1,result2,result3) Step70. Display FinalExpression as output Step71. Exit Impreprocess (Image_Pixel_Array) Step1. Start Step2. Convert Image_Pixel_Array to its corresponding double format let Image_Pixel_Array_Double.

Step3. If Image_Pixel_Array_Double is of format a*b*3, then convert it to Corresponding Gray scale and save the pixel values to a 2D array let gImage.

Else save the pixel values of the input image to a 2D array let gImage.

Step4. Resize gImage to a Predefined size say m*n & save the pixel values to a 2D array let gImage_Resized.

Step5. Reshape gImage_Resized Array to a 2D array of size 1*(mn) & save the pixel values to a 2D array let gImage_Reshaped.

Step6. Return the Array gImage_Reshaped.

Step7. End SVM_Training (Training_Data, Group_Membership_Class) Step1. Start Step2. Train Linear Support Vector Machine with Training_Data and Group_Membership_Class and store the value in an array let SVM1.

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Detecting Facial Expression in Images (J4R/ Volume 02 / Issue 02 / 006)

Step3. Return the array SVM1 Step4. End SVM_Classify (SVM_Trained, Img_Array) Step1. Start Step2. Classify Img_Array in one of the classes with SVM_Trained and SVM Binary Classifier and store the value in a variable let Classifier1 Step3. Return the value Classifier1 Step4. End Recognize_Expression (Val1, Val2, Val3) Step1. Start Step2. If Val1=1 Set Expression=Neutral Else if Val1= -1 and Val2=2 Set Expression=Smiling Else if Val1= -1 and Val2= -2 and Val3=3 Set Expression=Angry Else if Val1= -1 and Val2= -2 and Val3= -3 Set Expression=Sad Step3. Return the value of Expression Step4. End The following figures describes the output of the proposed system

Figure 9 Test image

Fig. 12: Detected Face Region

Figure 10 Templates of this image

Fig. 13: Neutral face

Fig. 11 Left eye template

Fig. 14: Neutral face template matching

Fig. 15: Smiling face

IV. CONCLUSIONS Facial expression recognition or emotion detection system has numerous applications in image processing domains, security applications domain or any type of biometric system.

REFERENCES [1] [2]

Farah Azirar, ‘Facial Expression Recognition’, Bachelor Thesis, School of Electronics and Physical Sciences, Department of Electronic Engineering, University of Surrey, 2004.

L. Franco and A. Treves. A Neural Network Face Expression Recognition System using Unsupervised Local Processing. Proceedings of the Second International Symposium on Image and Signal Processing and Analysis (ISPA 01), Croatia, pp. 628-632, June 2001.

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Detecting Facial Expression in Images (J4R/ Volume 02 / Issue 02 / 006) Angel Noe Martinez-Gonzalez and Victor Ayala-Ramirez, “Real Time Face Detection Using Neural Networks”, 2011 10th Mexican International Conference on Artificial Intelligence.

[4] M. Suwa, N. Sugie, and K. Fujimora. A preliminary note on pattern recognition of human emotional expression. In International Joint Conference on Pattern Recognition, pages 408–410, 1978.

[5] H.A. Rowley, S. Baluja and T.Kanade “Rotation Invariant Neural Network-Based Face Detection“, Proc IEEE Conf. Computer Vision and Pattern Recognition, 1998, pp 38-44.

[6] C.H. Lee, J.S. Kim, K.H. Park, “Automatic Human Face Location in a Complex Background Using Motion and Colour Information”, Pattern Recognition, vol. 29, no. 11, 1996, pp. 129-140.

[7] K. Sobottka, I. Pitas, “A Novel Method for Automatic Face Segmentation, Facial Feature Extraction and Tracking”, Signal Process. Image Communication, vol. 12, no. 3, 1998, 263-281.

[8] Hsu, Rein-Lien, Abdel-Mottaleb, Mohamed, Jain, and Anil K. “Face Detection in Colour Images”. IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 24, no. 5, 2002, pp. 696-706.

[9] A. Pentland and M. Turk. Eigen faces for recognition. Journal of Cognitive Neuroscience, 3(1):71-86, 1991.

[10] H. A. Rowley. Neural Network-Based Face Detection. PhD thesis, Carnegie Mellon University, Pitsburgh, 1999.

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