Face is the main emphasis of consideration in social dealings, which also plays a more important role in transmission of personality and feelings. Although the capability to infer intellect or character from facial look is dubious, the human skill to identify face is astonishing. We can recognize thousands of faces learnt all through our lifespan and detect acquainted faces at a single glimpse even after so many years of separation. The main issues found that there are three main factors to construct a Facial Expression Recognition system, namely the face identification, facial feature extraction, and reaction or feeling cataloguing. In this paper work we will propose a computational model of face recognition, which is quick, sensibly simple, and accurate in constrained environments such as an office or a household using k-MEAN clustering for divide the data in the cluster form, SIFT and IGA in which feature divide into cluster form with the help of K-mean algorithm, feature extraction is done by SIFT, feature optimization is done by IGA and classification is done using Feed Forward Neural Network. Then finally measure the performance using the following metrics called False Acceptance Rate, False Rejection Rate, and Accuracy. swiss replica watches

Keywords: Emotion Recognition System; Facial Expression; Neural Networld