Emotion Recognition plays a key role in interpersonal relationships. The ability to interpret facial expressions in the social environment allows people to anticipate intentions or situations and respond appropriately. The face recognition technology attracts more and more attention with people’s growing interesting in expression information. Face recognition has practical Significance; it has very broad application prospects, such as user-friendly interface between man and machine. Recently, ubiquitous healthcare systems have attracted a lot of researchers due to their prominent application the field of human computer Interaction (HCI). The objective of this project is to analyze, interpret and propose an efficient model for emotion recognition. Emotion recognition from facial expressions is generally performed in three steps: face detection, features extraction and classification of expressions. In this project we implements face recognition techniques using Principal Component analysis (PCA) and Linear Discriminative Analysis (LDA).