Abstract-Problems with multiple objectives can be solved by using Pareto optimization techniques in evolutionary multiobjective optimization algorithms. Many applications involve multiple objective functions and the Pareto front may contain a very large number of points. Selecting a solution from such a large set is potentially intractable for a decision maker. Previous approaches to this problem aimed to find a representative subset of the solution set. Clustering techniques can be used to organize and classify the solutions. Implementation of this methodology for various applications and in a decision support system is also discussed.

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Gestures are a major form of human communication. Hence gestures are found to be an appealing way to interact with computers, as they are already a natural part of how we communicate. A primary goal of gesture recognition is to create a system which can identify specific human gestures and use them to convey information for device control and by implementing real time gesture recognition a user can control a computer by doing a specific gesture in front of a video camera linked to the computer. A primary goal of gesture recognition research is to create a system which can identify specific human gestures and use them to convey information or for device control. This project covers various issues like what are gesture, their classification, their role in implementing a gesture recognition system, system architecture concepts for implementing a gesture recognition system, major issues involved in implementing a simplified gesture recognition system, exploitation of gestures in experimental systems, importance of gesture recognition system, real time applications and future scope of gesture recognition system.The algorithm used in this project are Finger counting algorithm,X-Y axis(To recognize the thumb).

With the Objective of making communication process easy between handicap people and computer this paper describes a very basic method for recognizing the alphabets from hand motion trajectory. This method uses Artificial Neural Network (ANN) for alphabet recognition from gesture path (motion trajectory). This is done in three main stages; preprocessing on video input, feature extraction and classification. In first stage, preprocessing, sticker of particular colour placed on users hand is detected using color information. After detection of required colour to be traced, its motion trajectory which is also called as gesture path will be determined by tracking the sticker placed on hand. In the second stage, features are extracted from hand motion track which can be further used for training & testing ANN. In the final stage alphabet is recognized by using extracted features of gesture path.