Introduction

Our ambitions become strong with the advent of modern technology and that it binds no boundaries. A significant research work is taking effect in the field of digital image analysis and processing in the current era. The way forward would be incremental and continues to evolve. In todays world, Image Processing is a vast research area and its applications are widespread.

Image processing is the field where images are always input and output. Face expression identification is among the most important applications in image processing. The movements in our face reveal the sentiment. Throughout effective communication, facial expressions play a significant role. Face expression is indeed a non-verbal empirical gesture conveyed by emotions in the face. Automatic facial expression recognition plays a vital role in artificial intelligence and robotics and therefore is a generation need. Most features related to this include personal identification and access control, teleconferencing and videophone, HCI (Human Computer Interaction ) and Surveillance.

It is easy to pin down human facial expressions into Seven base emotions: joyful, unhappy, surprise anxiety, outrage, disdain, and indifferent. Through stimulating different sets of jaw muscles, our facial expressions get conveyed. These signs in an expression often implicit yet complex, mostly provide an array of information regarding the mental state. Via facial emotion detection, through an effective and minimal-cost procedure we can measure the influence that ads and products have on the viewer / customers. Retailers, for example, use these indicators to quantify consumer's desire to participate. Medical professionals can also provide better customer service via the use of extra information on emotional patients. Entertainment industry could also use this to analyze viewership engagement in content to constantly create more accurate and desired content.

Recognition of facial expression is a process undertaken out by human beings as well as computer systems, comprised of: 1. Detecting facial features in the scene, 2. Extracting facial features from the activated area of the face (e.g. detecting the silhouette of facial elements or outlining the texture of the skin in a facial area;, 3. Evaluating the movement of facial characteristics and/or improvements in the appearance of facial characteristics and categorizing this data into several categories of facial expression, including smile or frown, happiness or anger etc.

The aim of this project is to implement a system for recognition of facial expression which can take human facial still images containing some expression as input and recognize and classify it into different expressions like I. Happy II. Fear III. Sad IV. Angry V. Disgust VI. Neutral VII. Suprise. Several projects have been carried out on this field and our goal is not only to develop an Facial Expression Detection system, but also to compress and transmit the media across multiple medias, thus portraying the effective use of this system in Multimedia Communications.