Abstract:Aiming at the problems of low recognition accuracy and poor anti-interference ability of traditional machine vision gesture recognition methods, a static gesture recognition method based on Support Vector Machine (SVM) gesture segmentation and transfer learning is proposed. This study uses SVM and transfer learning method to build a new gesture recognition model, uses SVM to segment the sample gesture, uses the Inception-v3 model as the basis of Convolutional Neural Network (CNN) model, carries out fine tuning on the network parameters, imports the sample processed by gesture segmentation into the model training, adjusts the super parameters using fine-tuning to get the new optimal gesture recognition model. The test results, obtained in disturbed environment, show that the recognition accuracy and real-time feedback efficiency of this method are higher than those of traditional methods, which can effectively recognize gesture and meet the practical application requirements.