Effective facial expression recognition system based on hybrid classification technique

In our daily life, facial expression recognition has possible functions in various sectors but still it is not understand, because the absence of efficient expression identification methods. Many methods are used to develop the effectiveness of the identification through indicating issues in face detection and extraction aspects in identification expressions. In the first phase, the noise is eliminating from the image by using of preprocessing techniques and to obtain the quality image in order to decrease the computational complexity. The following phase is feature extraction phase. In this phase, we are extracting related features like eyes, mouth and nose. The shape feature of eye part can be extracted by active appearance model (AAM). The texture feature of nose and mouth can be extracted using grey-level co-occurrence matrix (GLCM). Then in the final phase, we have to categorise a facial expression for this categorisation process by introducing an adaptive genetic fuzzy classifier (AGFC) and neural network (NN). Finally, score level fusion of this two classification result will be done to obtain the face emotions.