HAND GESTURERECOGNITION

USING NEURAL NETWORK IN MATLAB

Kuldeep SinghVinod Kumar

Rajesk Kumar

OBJECTIVESImplementation of pattern recognition using

Neural Network into MATLAB. The implemented system should able to

perform classification correctly. The implemented application should be user

friendly enough for anyone to use.System should be able to get static image

through the webcam and perform the classification.

INTRODUCTIONOur project aims on Vision based analysis

application to perform hand gesture recognition of sign language.

Computer Recognition of hand gestures may provide a more natural-computer interface.

Vision based interfaces provide more user friendly man-machine interaction.

Goal of our project is that it should not involve any external part/hardware except computer equipped with webcam. This is to keep the cost minimum and everyone able to own and use this application easily.

WHY MATLAB?There are few software can perform hand gesture

recognition such as MATLAB, MS Visual C#, MS Visual C++, and MS VB  

But the most common software are MATLAB and Visual C#.

MATLAB is chosen over MS C# because MATLAB is perfect for speeding up development process , it allows user to work faster and concentrate on the results rather on the design of the programming. 

MATLAB has Toolboxes which allow us to learn and apply specialized technology. In this project, we used 2 toolboxes which are Neural Network and Images Processing. 

METHODOLOGY• IMAGE DATABASE

The image database base will be used for Artificial Neural Network training and testing.

• PERCEPTRON PROCESS

Perceptron is one of the Neural Network to ‘learns’ concept. The next part will be based on Perceptron learning rule to train the network to perform pattern recognition.

• TESTING PERCEPTRON NETWORK IMPLEMENTATION

After training network testing is done by inputting images by webcam and then processing by perceptron .At last network shows matched output.

IMAGE PROCESSINGThe images in database can be of any image

file format such as ‘.jpg’, ‘.tif’, ‘.bmp’ and many more. Those images are converted to grayscale.

These images will go through a transformation called Transformation T . This transformation will convert an image into a feature vector, which will be then compared with other feature vectors of a training set of gestures.

This figure shows how pattern recognition works.

PERCEPTRON PROCESSING

TESTING PERCEPTRON NETWORK IMPLEMENTATION

APPLICATIONS• Hand gesture recognition has several of

applications such as computer games, gaming machines, as mouse replacement and machinery control (e.g. crane, surgery machines).

• Controlling computers via hand gestures can make many applications work more intuitive than using mouse, keyboard or other input devices.

THANK YOU