# INTRODUCTION

HE motion or positions of the muscles in the skin of a human face convey the emotional state of the individual to observers. These emotional states are a form of nonverbal communication. The recognition of emotional state of a human face has attracted increasing notice in pattern recognition, humancomputer interaction and computer vision. A method for automatic recognition of facial expressions from face images by providing Discrete Wavelet Transform DWT) features to a bank of five parallel neural networks is presented in [1]. Each Neural Network (NN) is trained to recognize a particular facial expression, so that it is most sensitive to that expression.

A new approach to facial expression recognition based on Stochastic Neighbor Embedding (SNE) is presented in [2]. SNE is used to reduce the high dimensional data of facial expression images into a relatively low dimension data and Support Vector Machine (SVM) is used for the expression classification. A new approach for the 3D human facial expressions analysis is presented in paper [3]. The methodology is based on 2Dand 3D wavelet transforms, which are used to estimate multi-scale features from real a face acquired by a 3D scanner. The different feature extraction techniques with advantage and disadvantage and find the recognition rate by using JAFFE databases is studied in [4]. The Adaboost classifier is used to classify the facial expression and from the JAFFE databases 60% data are used for the training and 40% data are used for the testing purpose.

Various feature representation and expression classification schemes to recognize seven different facial expressions, such as happy, neutral, angry, disgust, sad, fear and surprise, in the JAFFE database is investigated in [5]. A facial expression recognition system based on Gabor feature using a novel Local Gabor filter bank is proposed in [6]. A two-stage classifier for the elastic bunch graph matching based recognition of facial expressions is proposed in [7]. The distinctive similarity between image patterns are obtained by applying optimal weights to responses from different Gabor kernels and those from different fiducial points.

An algorithm based on Gabor filter and SVM is proposed for facial expression recognition in [8]. First, the features of facial expression emotion are represented by Gabor filter. Then the features are used to train the SVM classifier. Finally, the facial expression is classified by the SVM. A new method of facial expression recognition based on local binary patterns (LBP) and Local Fisher Discriminant Analysis (LFDA) is presented in [9]. The LBP features are firstly extracted from the original facial expression images. Then LFDA is used to produce the low dimensional discriminative embedded data representations from the extracted high dimensional LBP features with striking performance improvement on facial expression recognition tasks.

The performance of different feature extraction methods for facial expression recognition based on the higher-order local auto correlation (HLAC) coefficients and Gabor wavelet is investigated in [10]. An experiment on feature-based facial expression recognition within an architecture based on a two-layer perceptron is reported in [11]. The geometric positions of a set of points on a face, and a set of multi-scale and multiorientation Gabor wavelet coefficients' at these points are used as features. A method of facial expression recognition based on Eigen spaces is presented in [12]. In this paper, an automatic classification of human emotion based on UWT and KNN classifier is presented. The remainder of this paper is organized as follows: The methodologies and proposed method is described in sections 2 and 3 respectively. The comparative study between DWT and UWT is given in section 4. Finally, conclusion is given in section 5. 


# EXPERIMENTAL RESULTS

The JAFFE database [13] is used to evaluate the performance of the proposed system. The database contains 213 images of 7 facial expressions. The facial expressions in this database are happiness, sadness, surprise, anger, disgust, fear and neutral. The images in the database are grayscale images of size 256x256 in the tiff format. The heads of the subjects in the images are in frontal pose. The eyes are roughly at the same position with a distance of 60 pixels in the final images. 


# Proposed model

The proposed model is planning to apply the emotion captured by means of DWT method been applied to Korts Spiral Learning model, thereby to achieve the maximum learning comfort to the learners and teachers. Figure 3a : Proposed model relating phases of learning to emotions in Figure 2 The student ideally begins in Quadrant I or II: they might be curious and fascinated about a new topic of interest (Quadrant I) or they might be puzzled or get confused (Quadrant II). In either case, they are in the top half of the space, if their focus is on learning.

At this point it is not uncommon for the student to move down into the lower half of the diagram (Quadrant III) where emotions may be negative and the cognitive focus changes to eliminating some misconception. As she consolidates her knowledgewhat works and what does not-with awareness of a sense of making progress, she may move to Quadrant IV. Getting a fresh idea propels the student back into the upper half of the space, most likely Quadrant I. Thus, a typical learning experience involves a range of emotions, moving the student around the space as they learn.  3a and 3b for each axis in Figure 4, then at any given instant, the student might be in multiple Quadrants with respect to different axes. They might be in Quadrant II with respect to feeling frustrated; and simultaneously in Quadrant I with respect to interest level. It is important to recognize that a range of emotions occurs naturally in a real learning process. We do not foresee trying to keep the student in Quadrant I, but rather to help him see that the cyclic nature is natural in learning process, and that when he lands in the negative half, it is only part of the cycle. Our aim is to help them to keep orbiting the loop, teaching them how to propel themselves especially after a setback.

In Quadrant I, anticipation and expectation are high, as the learner builds ideas and concepts and tries them out. Emotional mood decays over time either from boredom or from disappointment. In Quadrant II, the rate of construction of working knowledge diminishes, and negative emotions emerge as progress flags. In Quadrant III, the learner discards misconceptions and ideas that didn't work out, as the negative affect runs its course. In Quadrant IV, the learner recovers hopefulness and positive attitude as the knowledge set is now cleared of unworkable and unproductive concepts, and the cycle begins anew. In building a complete and correct mental model associated with a learning opportunity, the learner may experience multiple cycles around the phase plane until completion of the learning exercise. Each orbit represents the time evolution of the learning cycle. Note that the orbit doesn't close on itself, but gradually moves up the knowledge axis.

V.


# Conclusion

As E-learning are in the threshold of taking big leap in terms of volume and its reach, it is natural for the E-learning provider to go after the cognitive domain, but this paper strongly propose for the E-learning providers to have paradigm shift towards the affective aspect of the education and learning, which will instead of pushing the students, will pull the students towards effective learning in e-learning environment. The capturing of these affective aspects in e-learning environment can be done effectively by means of DWT method. Hence, applying this technology we can ensure Optimal E-Learning Environment (OELE) in E-learning scenarios. 
1![Fig. 1 : (a) Sample image from JAFFE database (b) 2-D Wavelet transformed image at level 1 III.](image-2.png "Fig. 1 :")
2![Fig. 2 : Block Diagram of OELE model We can achieve Optimal E-Learning Environment(OELE) in e-learning only if could capture](image-3.png "Fig. 2 :")
4![Figure 4 : Emotion sets possibly relevant to learning If one visualizes a version of Figures3a and 3bfor each axis in Figure4, then at any given instant, the student might be in multiple Quadrants with respect to different axes. They might be in Quadrant II with respect to feeling frustrated; and simultaneously in Quadrant I with respect to interest level. It is important to recognize that a range of emotions occurs naturally in a real learning process. We do not foresee trying to keep the student in Quadrant I, but rather to help him see that the cyclic nature is natural in learning process, and that when he lands in the negative half, it is only part of the cycle. Our aim is to help them to keep orbiting the loop,](image-4.png "Figure 4 :")
1The proposed system is implemented in MATLABversion 7.10. Many computer simulations andexperiments with JAFFE images are performed.All the images in the JAFFE database areconsidered for the emotion recognition test. Among the213 images 140 images from 7 facial expressions areused for training the classifier and remaining 73 imagesare used for testing the classifier. The averageclassification rate obtained by the proposed emotionrecognition system is shown in Table 1.AverageL Level ofrecognitiondecompositionrate (%)DWT173.58275.00373.55477.28580.08684.02784.72emotion recognition systemIV.
			© 2012 Global Journals Inc. (US)
		
		
			

				


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