Hand gesture recognition

In this master study, the purpose is to classify different hand gestures in our database. The database consists of 6 types of gesture class and each gesture class has 50 hand images. Each type of gesture symbolizes a number from 0 to 5. The hand gesture recognition system consists of four main stages: Image enhancement, segmentation, feature extraction and classification. In the image enhancement stage, median filter is used to get rid of high frequency components. After the image enhancement stage, hand region in the image needs to be separated for the next stage. In order to extract hand data from the image, regions that are similar to skin color are found using a color threshold process, and then contour data of the hand which will represent the hand region is selected by finding the longest inner contour of the longest outer contour in the existing regions that are similar to skin color. In the feature extraction stage, useful features for the classification stage are obtained using of the shape features such as convexity defects of the contour. Classifier of the system consists of simple conditional expressions and intersection arc. Depending on the features, the classifier decides which gesture corresponds to the input of the system. The system has a ninety nine percent success rate.