Classification of Hand Gestures Using Gabor Filter with Bayesian and Naïve Bayes Classifier

Abstract

A hand Gesture is basically the movement, position or posture of hand used extensively in our daily lives as part of non-verbal communication. A lot of research is being carried out to classify hand gestures in videos as well as images for various applications. The primary objective of this communication is to present an effective system that can classify various static hand gestures in complex background environment. The system is based on hand region localized using a combination of morphological operations. Gabor filter is applied to the extracted region of interest (ROI) for extraction of hand features that are then fed to Bayesian and Naive Bayes classifiers. The results of the system are very encouraging with an average accuracy of over 90\%.