A Hand Gesture Recognition Algorithm Based on Multi-scale Hybrid Features

Abstract

A human hand gesture recognition algorithm based on multi-scale hybrid features is pro-posed. A large number of gesture data images are organized and annotated by automatic im-age annotation algorithm. Based on dense sampling, the multi-scale Gaussian pyramid is taken into account for enhancing the image details; it can realize the recognition of both large and small target gestures. Meanwhile, according to the aggregation and distribution of gestures, statistical analysis is used to optimize the size and aspect ratio of anchors, which can further improve the accuracy of recognition. The experiment results show that the mean average precisions of this algorithm can be improved to 99.7% and 93.3% for the simple gesture and complex gesture, respectively, which are higher than that obtained by these traditional methods, such as faster region-convolution neural network (Faster R-CNN), You Only Look Once (YOLO), and SSD.