Static hand sign recognition using linear projection methods

Author

Gamage, Nuwan ; Chow, Kuang Ye ; Akmeliawati, Rini

Author_Institution

Dept. of Electr. & Comput. Syst. Eng., Monash Univ., Bandar Sunway

fYear

2009

fDate

10-12 Feb. 2009

Firstpage

403

Lastpage

407

Abstract

Shape matching is one of the more significant research topics in the fields of computer vision, pattern recognition and machine learning. Successful shape matching algorithms/ methods has a high potential for a wide variety of practical applications. In this paper, we present our effort on using linear projection methods for static hand sign recognition in Malaysian sign language. PCA and LPP methods have been used with a database of 240 hand shapes.

Keywords

image matching; object recognition; principal component analysis; Malaysian sign language; PCA methods; computer vision; linear projection methods; locality preserving projections; machine learning; pattern recognition; principal component analysis; shape matching algorithms; static hand sign recognition; Application software; Computer vision; Databases; Handicapped aids; Machine learning; Machine learning algorithms; Pattern matching; Pattern recognition; Principal component analysis; Shape; LPP; PCA; hand signs; shape matching;

fLanguage

English

Publisher

ieee

Conference_Titel

Autonomous Robots and Agents, 2009. ICARA 2009. 4th International Conference on

Conference_Location

Wellington

Print_ISBN

978-1-4244-2712-3

Electronic_ISBN

978-1-4244-2713-0

Type

conf

DOI

10.1109/ICARA.2000.4803982

Filename

4803982