Two-Dimensional Parallel Spatio-Temporal Pyramid Pooling for Hand Gesture Recognition

Abstract

Hand Gesture Recognition (HGR) plays a crucial role in user-friendly interactions between humans and computers. In recent years, using the Convolutional Neural Network (CNN) has improved the accuracy of image processing problems. Inspired by the high recognition rate of CNN and its efficiency, we propose a model for hand gesture recognition based on CNN and evaluate the results using images with plain and complex backgrounds. Recognizing different hand signs by Two-Dimensional Parallel Spatio-Temporal Pyramid Pooling (2DPSTPP) features with deep learning methods reduces the size of the map, minimizes training complexity, and by paying attention to more details, improves detection performance. The effectiveness of the proposed method is evaluated using regular cross-validation tests on six datasets, namely American Sign Language (ASL), the NUS hand posture dataset I, the NUS hand posture dataset II, the digits dataset, the hand gesture dataset, and the leap gesture recognition dataset.