Feature Extraction Technique for Static Hand Gesture Recognition

The goal of static hand gesture recognition is to classify the given hand gesture data represented by some features into some prede ned nite number of gesture classes. The main objective of this e ort is to explore the utility of two feature extraction methods, namely, hand contour and complex moments to solve the hand gesture recognition problem by identifying the primary advantages and disadvantages of each method. Arti cial neural network is built for the purpose of classi cation by using the back-propagation learning algorithm. The proposed system presents a recognition algorithm to recognize a set of six speci c static hand gestures, namely: Open, Close, Cut, Paste, Maximize, and Minimize. The hand gesture image is passed through three stages, namely, pre-processing, feature extraction, and classi cation. In the pre-processing stage some operations are applied to extract the hand gesture from its background and prepare the hand gesture image for the feature extraction stage. In the rst method, the hand contour is used as a feature which treats scaling and translation problems (in some cases). The complex moments algorithm is, however,