Deakin University
Browse

File(s) under permanent embargo

Hand gesture recognition and spotting in uncontrolled environments based on classifier weighting

conference contribution
posted on 2015-01-01, 00:00 authored by Y Yao, Chang-Tsun LiChang-Tsun Li
© 2015 IEEE. Pure appearance based Hand Gesture Recognition and Spotting in uncontrolled environments are challenging tasks due to the uncontrolled scene settings include: multiple hand regions in the scene; background moving objects; scale, speed and location variations of the gesture trajectories; changing lighting conditions and frontal occlusions. An appearance based method based on a novel classifier weighting scheme is proposed in this paper for hand gesture recognition and spotting in uncontrolled environments. The method is capable of producing decent performance with the presence of all the aforementioned challenges. Two databases are used for evaluating the proposed method, the Palm Graffiti Digits Database and the Warwick Hand Gesture Database. The experimental results demonstrate that the proposed method can deal with the challenges from uncontrolled environments without any prior knowledge and enhance the performance of the initial classifier.

History

Event

Image Processing. International Conference (2015 : Quebec City, Canada)

Pagination

3082 - 3086

Publisher

IEEE

Location

Quebec City, Canada

Place of publication

Piscataway, N.J.

Start date

2015-09-27

End date

2015-09-30

ISSN

1522-4880

ISBN-13

9781479983391

Language

eng

Publication classification

E1.1 Full written paper - refereed

Copyright notice

2015, IEEE

Title of proceedings

ICIP 2015 : Proceedings of the IEEE International Conference on Image Processing

Usage metrics

    Research Publications

    Categories

    No categories selected

    Exports

    RefWorks
    BibTeX
    Ref. manager
    Endnote
    DataCite
    NLM
    DC