Details

Autor(en) / Beteiligte

• Dominio, Fabio
• Donadeo, Mauro
• Marin, Giulio
• Zanuttigh, Pietro
• Cortelazzo, Guido Maria

Titel

Hand gesture recognition with depth data

Ist Teil von

• Proceedings of the 4th ACM/IEEE international workshop on Analysis and retrieval of tracked events and motion in imagery stream, 2013, p.9-16

Ort / Verlag

New York, NY, USA: ACM

Erscheinungsjahr

2013

Link zum Volltext

Quelle

ACM Digital Library Complete

Beschreibungen/Notizen

• 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.