Shape Matching and Object Recognition Using Shape Contexts
IEEE Transactions on Pattern Analysis and Machine Intelligence
Integral Invariants for Shape Matching
IEEE Transactions on Pattern Analysis and Machine Intelligence
Vision-based hand-gesture applications
Communications of the ACM
Dynamic Hand Pose Recognition Using Depth Data
ICPR '10 Proceedings of the 2010 20th International Conference on Pattern Recognition
LIBSVM: A library for support vector machines
ACM Transactions on Intelligent Systems and Technology (TIST)
Robust hand gesture recognition based on finger-earth mover's distance with a commodity depth camera
MM '11 Proceedings of the 19th ACM international conference on Multimedia
Time-of-Flight Cameras and Microsoft Kinect(TM)
Time-of-Flight Cameras and Microsoft Kinect(TM)
Leafsnap: a computer vision system for automatic plant species identification
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part II
Robust 3d action recognition with random occupancy patterns
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part II
Motion capture of hands in action using discriminative salient points
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part VI
Fast and adaptive deep fusion learning for detecting visual objects
ECCV'12 Proceedings of the 12th international conference on Computer Vision - Volume Part III
Hi-index | 0.00 |
Depth data acquired by current low-cost real-time depth cameras provide a very informative description of the hand pose, that can be effectively exploited for gesture recognition purposes. This paper introduces a novel hand gesture recognition scheme based on depth data. The hand is firstly extracted from the acquired depth maps with the aid also of color information from the associated views. Then the hand is segmented into palm and finger regions. Next, two different set of feature descriptors are extracted, one based on the distances of the fingertips from the hand center and the other on the curvature of the hand contour. Finally, a multi-class SVM classifier is employed to recognize the performed gestures. The proposed scheme runs in real-time and is able to achieve a very high accuracy on depth data acquired with the Kinect.