CFAENet: A Lightweight Convolutional Neural Network Based on Cross-Dimensional Feature Aggregation for Hand Gesture Recognition

Vision-based hand gesture recognition methods enable a natural and efficient human-robot interaction process. However, gesture images tend to have interfering factors such as complex environmental backgrounds, varying light intensities, and skin-like pixels, which makes it difficult for existing gesture recognition networks to weigh the relationship between recognition accuracy and computational cost. To this end, a lightweight cross-dimensional feature adaptive enhancement network (CFAENet) is proposed in this paper. The network efficiently integrates convolutional and attention modules from the perspective of information transfer flow, and proposes a space and channel information consistency enhancement (SCICE) module. In addition, this paper proposes a lightweight spatial attention guidance (SAG) and a channel attention guidance (CAG) to achieve cross-dimensional feature aggregation within a module, and employs differential processing of spatial information to construct an efficient multi-view feature aggregation (MFA) bottleneck. Compared with some gesture recognition networks on multiple gesture benchmark datasets, the proposed CFAENet shows better performance in terms of recognition accuracy and computational cost, and achieves a good interaction experience in human-robot interaction experiments.