Comparison study of different feature classifiers for hand posture classification

Jeonghyun Baek, Jisu Kim, Euntai Kim

Research output: Chapter in Book/Report/Conference proceedingConference contribution

4 Citations (Scopus)

Abstract

Hand posture classification has attracted much attention in Human-Computer Interaction (HCI). In hand posture classification, vision based approach is popularly used. However, it has difficulty of dealing with illumination change and pose variation. In this paper, we compare the performance of combination with features, which are HOG, LBP, and classifiers, which are SVM and Neural Network for hand posture classification. Experiments are performed with Cambridge hand gesture dataset.

Original languageEnglish
Title of host publicationICCAS 2013 - 2013 13th International Conference on Control, Automation and Systems
Pages683-687
Number of pages5
DOIs
Publication statusPublished - 2013 Dec 1
Event2013 13th International Conference on Control, Automation and Systems, ICCAS 2013 - Gwangju, Korea, Republic of
Duration: 2013 Oct 202013 Oct 23

Other

Other2013 13th International Conference on Control, Automation and Systems, ICCAS 2013
CountryKorea, Republic of
CityGwangju
Period13/10/2013/10/23

    Fingerprint

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence
  • Computer Science Applications
  • Control and Systems Engineering
  • Electrical and Electronic Engineering

Cite this

Baek, J., Kim, J., & Kim, E. (2013). Comparison study of different feature classifiers for hand posture classification. In ICCAS 2013 - 2013 13th International Conference on Control, Automation and Systems (pp. 683-687). [6703956] https://doi.org/10.1109/ICCAS.2013.6703956