This paper aims to incorporate facial expression recognition needs and applications. The expression of the face is the mode of non-verbal communications between the verbals and nonverbs.. It reflects or fills a human perspective and its mental state. A major research initiative was conducted over two decades to develop human computer interaction. This paper comprises introduction to the recognition method for facial emotions, implementation, comparison of common techniques of facial expression recognition and phases for automated systems for facial identification. The Paper aims to Sight faces from any image, extract facial expression (eyes and lips) and classify them into six emotions (happy, fear, anger, disgust, neutral and sadness. The Coaching information is skillful a series of filters and processes and is eventually characterized through a Support Vector Machine (SVM), refined victimization Grid Search. Facial Expression are most commonly used for interpretation of humans emotion. There is a range of different emotions in two categories: positive emotion and negative emotion. There are four types of generally using system: Face detection, extraction, classification and recognition. In Existing System it is not so much identify extract emotion of a person. An summary of existing phases, methods and data sets of Facial Emotion Recognition (FER). FER is recognised in the sphere of computer vision and machine learning for decades and is an essential topic. In most applications such as healthcare, teaching, criminal research, human-robot interface (HRI), etc, automated FERs are useful.