A Method of Gesture Recognition Using CNN-SVM Model with Error Correction Strategy

The gesture recognition methods based on artificial feature extraction are time-consuming and low recognition rate. The generalization ability of hand gesture recognition using convolution neural network is not strong. Therefore, this paper combines the advantages of CNN and SVM to propose a hybrid model to automatically extract the features and improve the generalization ability, in addition, we use an error correction strategy to reduce the error recognition rate of confusing gestures. First, the segmentation preprocessing of gesture data collected by Kinect. Then, the hybrid model automatically extracts features from the data and generates the predictions. Finally, using the error correction strategy to adjust the prediction result. We get a recognition rate of 95.81% without error correction strategy on our database, the average recognition rate of 97.32% with error correction strategy.