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

Abstract

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.