A Multiorder Attentional Spatial Interactive Convolutional Neural Network (MoAS-CNN) for Low-Resolution Haptic Recognition

In haptic recognition, pressure information is usually represented as an image, and then used for feature extraction and classification. Deep learning that processes haptic information in end-to-end manner has attracted attention. This study proposes a multiorder attentional spatial interactive convolutional neural network (MoAS-CNN) for haptic recognition. The asymmetric dual-stream all convolutional neural network with integrated channel attention module is applied for automatic first-order feature extraction. Later on, the spatial interactive features based on the overall feature map are computed to improve the second-order description capability. Finally, the multiorder features are summed to improve the feature utilization efficiency. To validate the MoAS-CNN, we construct a haptic acquisition platform based on three-scale pressure arrays and collect haptic letter-shape (A–Z) datasets with complex contours. The recognition accuracies are 95.73% for 16 × 16, 98.37% for 20 × 20 and 98.65% for 32 × 32, which significantly exceeds the traditional first- and second-order CNNs and local SIFT feature.