Method for improving the accuracy of 3D light field interaction based on a small dataset of hand key points using an MLP network

To address the issues of low recognition rate, slow recognition speed, and the need for large amounts of data samples in current 3D light field gesture interaction, this paper proposes a method based on a small dataset of hand key points using a multi-layer perceptron (MLP) network to improve the accuracy of 3D light field interaction, with recognition speed reaching the millisecond level. In the process of collecting hand key points, there are significant differences in the three-dimensional data of the same type of hand gesture collected from different locations. In order to eliminate these differences, this paper proposes a method of normalizing the same gesture through pose transformation of the simplified gesture model in the same right-hand Cartesian coordinate system using displacement and Rodrigues rotation formula. An MLP neural network is utilized to extract hand features from the normalized hand key points transition relationships. Experimental results show that the proposed method has a recognition rate of above 95% for simple gestures in 3D light field interaction, and a recognition rate of above 90% for complex gestures. Furthermore, the proposed method demonstrates excellent performance under training with a small dataset, meeting the requirements of both accurate and fast gesture recognition. Finally, this paper presents a successful application of the proposed method to a 3D light field interaction scenario.