Cognitive Model - Based Emotion Recognition From Facial Expressions For Live Human Computer Interaction

Human emotions are deeply intertwined with cognition. Emotions direct cognitive processes and processing strategies of humans. The goal of this work is to design a model with the capability of classifying the uncertainty, contradiction and the cognitive nature of the emotions. For achieving this, 3D cognitive model is designed. This model enhances our vision of classification of emotions produced by reinforcing stimuli. In this model the dimensions represent the positive reinforcers, the negative reinforcers and the emotion content present. The positive reinforcer increases the probability of emission of a response on which it is contingent, whereas the negative reinforcer increases the probability of emission of a response that causes the reinforcer to be omitted. This model increases the number of emotions, that can be classified. Presently this model can classify 22 emotions subject to the presence of a facial expression database. It has the flexibility to increase upon the number of emotions. For emotion (pattern) identification, the pose and illumination factor are removed using Gabor wavelet transforms and the size is reduced by finding its principle components (PCA). This component vector is used for training the neural network. The test result shows the recognition accuracy of 85.7% on The Cohn-Kanade Action Unit Coded Facial Expression Database. The real time processing for identification, aids in applying emotions to real time audio player. An environment, that is all pervasive or ubiquitous, that would sense one's mental state and play the appropriate musical track to maintain the positive emotional state or ease from a negative emotional state