Research on gesture image recognition method based on transfer learning

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. (c) 2021 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0) Peer-review under responsibility of the scientific committee of the International Conference on Identification, Information and Knowledge in the internet of Things, 2020.