Hand gesture recognition using neural networks

Visual Interpretation of gestures can be useful in accomplishing natural Human Computer Interactions (HCI). In this paper we proposed a method for recognizing hand gestures. We have designed a system which can identify specific hand gestures and use them to convey information. At any time, a user can exhibit his/her hand doing a specific gesture in front of a web camera linked to a computer. Firstly, we captured the hand gesture of a user and stored it on disk. Then we read those videos captured one by one, converted them to binary images and created 3D Euclidian Space of binary values. We have used supervised feed-forward neural net based training and back propagation algorithm for classifying hand gestures into ten categories: hand pointing up, pointing down, pointing left, pointing right and pointing front and number of fingers user was showing. We could achieve up to 89% correct results on a typical test set.