Improving the Classification Accuracy of Emotion Recognition using Facial Expressions

The science of image processing helps to recognize the human gesture for general life applications. Human gestures can be identified by observing the different movements of eyes, mouth, nose and hands. The face is a rich source of information about human behavior. The proposed method of facial expression recognition system is based on PCA and Neural Networks, to recognize the facial expression from a well captured image by means of extracting the features of face. This paper presents classification accuracy of neural network with principal component analysis (PCA) for feature selections in emotion recognition using different facial expressions. Dimensionality reduction of a feature set is a common preprocessing step used for pattern recognition and classification applications. From the experimental results it is concluded that, neural networks with PCA is effective in emotion recognition using facial expressions, in which it is attained a recognition rate of approximately 85% when testing six emotions on benchmark image data set.