3D hand gesture recognition based on Polar Rotation Feature and Linear Discriminant Analysis

Author

Yiding Wang ; Lin Zhang

Author_Institution

Coll. of Inf. Eng., North China Univ. of Technol., Beijing, China

fYear

2013

fDate

9-11 June 2013

Firstpage

215

Lastpage

219

Abstract

A new method based on Polar Rotation Feature and Linear Discriminant Analysis for hand gesture recognition is proposed in this paper. The gesture images in our system are derived from a 3D laser scanner which generates depth data. Hand area segmentation, hole-filling and normalization are done first, then a feature of the polar rotation distance is extracted via polar-coordinate transformation. Utilized PCA+LDA as the classifier. Experiences show our algorithm is robust and accurate. Finally we achieve 96.67% recognition rates under a set of six kinds of hand gestures.

Keywords

feature extraction; gesture recognition; image classification; optical scanners; principal component analysis; 3D hand gesture recognition; 3D laser scanner; PCA+LDA; classifier; depth data; linear discriminant analysis; polar rotation distance; polar rotation feature; polar-coordinate transformation; Cameras; Conferences; Eigenvalues and eigenfunctions; Face; Feature extraction; Gesture recognition; Lasers; 3D Hand Gesture; LDA; Polar Rotation Feature; hole filling;

fLanguage

English

Publisher

ieee

Conference_Titel

Intelligent Control and Information Processing (ICICIP), 2013 Fourth International Conference on

Conference_Location

Beijing

Print_ISBN

978-1-4673-6248-1

Type

conf

DOI

10.1109/ICICIP.2013.6568070

Filename

6568070