Emotion classification using neural network

Neural networks have found profound success in the area of pattern recognition. By repeatedly showing a neural network inputs classified into groups, the network can be trained to discern the criteria used to classify, and it can do so in a generalized manner allowing successful classification of new inputs not used during training. With the explosion of research in emotion in recent year, the application of pattern recognition technology to emotion detection has become increasingly interesting. Since emotion has become an important interface for the communication between human and machine, it plays a basic role in rational decision-making, learning, perception, and various cognitive tasks. Human´s emotion can be detected based on the physiological measurements, facial expression and vocal recognition. Since human shows the same facial muscles when expressing a particular emotion, therefore the emotion can be quantified. In this study, six primary emotions such as anger, disgust, fear, happiness, sadness and surprise were classified using Neural Network. Real dataset of facial expression images were captured and processed to prepare for Neural Network training and testing. The dataset was tested on Multilayer Layer Perceptron with Backpropagation learning algorithm and Regression analysis. The experimental results reveal that Neural Network has a misclassification rate of 2.5% while Regression analysis yields a misclassification rate of 33.33%.