Context-Aware Hand Pose Classifying Algorithm Based on Combination of Viola-Jones Method, Wavelet Transform, PCA and Neural Networks

In this paper we propose a novel context-aware algorithm for hand poses classifying. The proposed algorithm based on Viola-Jones method, wavelet transforms, PCA and neural networks. At first, the Viola-Jones method is used to find the location of hand pose in images. Then the features of hand pose are extracted using combination of wavelet transform and PCA. Finally, these extracted features are classified by multi-layer feedforward neural networks. In this proposed algorithm, for each training hand pose we create one neural network, which will determine whether an input hand pose is training hand pose or not. In order to test the proposed algorithm, we use known Cambridge Gesture database and divide it into 5 parts with difference light contrast conditions. The experimental results show that the proposed algorithm effectively classifies the hand pose in difference light contrast conditions and competes with state-of-the-art algorithms.