Static gesture recognition based on feature clustering representation learning

• 1、Department of Computer Science and Technology，School of Machanical Electronic and Information Engineering,China University of Mining and Technology

Abstract：Human gestures contain rich semantic information and are currently a hot application in the field of human-computer interaction. Static gestures are affected by the difference of the actor\'s hand type and the degree of subjective action, which leads to poor correspondence between features and labels when using deep network to train the recognition model, and the boundaries between gesture categories are unclear, which ultimately leads to low accuracy of the recognition model. This paper proposes a static gesture feature representation learning method based on clustering feature evaluation to solve the problem of unclear feature distribution boundaries caused by poor correspondence between gesture features, improve the aggregation degree of network extracted features, and improve the accuracy of gesture recognition. The effectiveness of the method proposed in this paper is proved by the test on the public data set and the comparative analysis with the method before the improvement.

Keywords： deep learning static gesture feature clustering degree of clustering