Simple and intelligent system to recognize the expression of speech-disabled person

The objective of this work is to recognize 40 basic hand gestures. The main features used are centroid in the hand, presence of thumb and number of peaks in the hand gesture. The algorithm is based on the shape based features by keeping in the mind that shape of human hand is same for all human beings except in some situations. The hand gestures are captured and stored in the disk. The stored images converted into binary images and then pre-processing is performed to eliminate noise using Otsu's method. The features are extracted using vision based hand gesture recognition techniques. On the basis of these features a five bit binary sequence is generated. The classification is performed by rule based classification approach. The algorithm is tested for 40 different hand gestures with the database of 200 images taken from a simple camera of 3.2 mega pixels.