Duodepth: Static Gesture Recognition Via Dual Depth Sensors

TL;DR

Duodepth introduces dual depth sensor methods for static gesture recognition, significantly reducing misclassification caused by occlusion and pose variations, using point cloud fusion and dual PointNet architectures.

Contribution

The paper proposes two novel approaches utilizing synchronized dual depth cameras, including point cloud fusion and dual PointNet, to improve static gesture recognition accuracy.

Findings

39.2% reduction in misclassification with fused point clouds

53.4% reduction with dual PointNet architecture

Effective handling of occlusion and pose variation in gesture recognition

Abstract

Static gesture recognition is an effective non-verbal communication channel between a user and their devices; however many modern methods are sensitive to the relative pose of the user's hands with respect to the capture device, as parts of the gesture can become occluded. We present two methodologies for gesture recognition via synchronized recording from two depth cameras to alleviate this occlusion problem. One is a more classic approach using iterative closest point registration to accurately fuse point clouds and a single PointNet architecture for classification, and the other is a dual Point-Net architecture for classification without registration. On a manually collected data-set of 20,100 point clouds we show a 39.2% reduction in misclassification for the fused point cloud method, and 53.4% for the dual PointNet, when compared to a standard single camera pipeline.