Title :
Kernel based hand gesture recognition using kinect sensor
Author :
Ramírez-Giraldo, Daniela ; Molina-Giraldo, Santiago ; Álvarez-Meza, Andrés M. ; Daza-Santacoloma, Genaro ; Castellanos-Domínguez, Germán
Author_Institution :
Signal Process. & Recognition Group, Univ. Nac. de Colombia sede Manizales, Manizales, Colombia
Abstract :
Category 4. A machine learning based methodology is proposed to recognize a predefined set of hand gestures using depth images. For such purpose, a RGBD sensor (Microsoft kinect) is employed to track the hand position. Thus, a preprocessing stage is presented to subtract the region of interest from depth images. Moreover, a learning algorithm based on kernel methods is used to discover the relationships among samples, properly describing the studied gestures. Proposed methodology aims to obtain a representation space which allow us to identify the dynamic of hand movements. Attained results show how our approach presents a suitable performance for detecting different hand gestures. As future work, we are interested in recognize more complex human activities, in order to support the development of human-computer interface systems.
Keywords :
gesture recognition; human computer interaction; learning (artificial intelligence); Microsoft kinect; RGBD sensor; depth image; hand position tracking; human-computer interface system; kernel based hand gesture recognition; kinect sensor; machine learning; representation space; Cameras; Computer vision; Humans; Image recognition; Kernel; Machine learning; Trajectory; Depth sensor; human motion; kernel methods;
Conference_Titel :
Image, Signal Processing, and Artificial Vision (STSIVA), 2012 XVII Symposium of
Conference_Location :
Antioquia
Print_ISBN :
978-1-4673-2759-6
DOI :
10.1109/STSIVA.2012.6340575
