Dynamic hand gesture recognition using a CNN model with 3D receptive fields
Ho-Joon Kim, Joseph S. Lee, Jin-Hui Park · 2008
In this paper, a pattern recognition model for dynamic hand gesture recognition is proposed. The proposed model combines a convolutional neural network (CNN) with a weighted fuzzy min-max (WFMM) neural network; each module performs feature extraction and feature analysis, respectively. The data representation proposed in this research is a spatiotemporal template which is based on the motion information of the target object. To process the data, we develop a modified CNN model by extending the receptive field to a three-dimensional structure. To increase the efficiency of the pattern classifier, we use a feature analysis technique utilizing the WFMM algorithm. The experimental results show that the proposed method can minimize the influence caused by the spatial and temporal variation of the feature points. The recognition performance using only the selected features for the classification process is evaluated.