Wavelet Enhanced Image Preprocessing and Neural Networks for Hand Gesture Recognition

This paper presents a novel approach for hand gesture recognition based on wavelet enhanced image preprocessing and supervised Artificial Neural Networks (ANNs). Six different hand gestures are tested. The image preprocessing handles the hand gesture contour segmentation. This research includes three contributions: (1) it provides two dimensional hand gesture contour images to one dimensional signal conversion using reference points, (2) it implements wavelet decomposition for the 1D signals converted from 2D hand gesture contour images, and (3) it extracts 4 statistical features of the wavelet coefficients. The experimental results are provided to validate our proposed framework. The ANN is built to classify different hand gestures. There are totally 1240 images used for training and 240 images are used for testing. By using the proposed framework, our approach can provide classification accuracy of 97% and is fast in feature extraction and computation.