Abstract
To solve the problem of low gesture image recognition rate, we propose a transfer learning based image recognition method called Mobilenet-RF. We combine the two models of MobileNet convolutional network with Random Forest to further improve image recognition accuracy. This method firstly transfers the model architecture and weight files of MobileNet to gesture images, trains the model and extracts image features, and then classifies the features extracted by convolutional network through the Random Forest model, and finally obtains the classification results. The test results on the Sign Language Digital dataset, Sign Language Gesture Image dataset and Fingers dataset showed that the recognition rate was significantly improved compared with Random Forest, Logistic Regression, Nearest Neighbor, XGBoost, VGG, Inception and MobileNet.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
