Skip to main navigation Skip to search Skip to main content

Human hand detection using robust local descriptors

  • Beihang University
  • Nokia Research Center

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

To date, human hand detection in images remains a challenging task due to the variable lighting conditions, hand appearances and background noise. In this paper, we present an effective strategy based on feature fusion for detecting hands with cluttered surroundings. To form the fusions, we propose three novel noise invariant features, namely: 1) NCHOG (Noise Compensated Histogram of Oriented Gradients), 2) NCLBP (Noise Compensated Local Binary Patterns), and 3) HPCP (Histograms of Pairs of Circumference Pixels). We show the superior performance of the NCHOG and the NCLBP descriptors over their existing traditional counterparts, i.e., HOG and LBP. Merging our novel features with existing features in different permutations, and applying Partial Least Squares (PLS) based feature weighting, yields excellent detection results on our own dataset of hand images with variegated and complex backgrounds.

Original languageEnglish
Title of host publicationElectronic Proceedings of the 2013 IEEE International Conference on Multimedia and Expo Workshops, ICMEW 2013
DOIs
StatePublished - 2013
Event2013 IEEE International Conference on Multimedia and Expo Workshops, ICMEW 2013 - San Jose, CA, United States
Duration: 15 Jul 201319 Jul 2013

Publication series

NameElectronic Proceedings of the 2013 IEEE International Conference on Multimedia and Expo Workshops, ICMEW 2013

Conference

Conference2013 IEEE International Conference on Multimedia and Expo Workshops, ICMEW 2013
Country/TerritoryUnited States
CitySan Jose, CA
Period15/07/1319/07/13

Keywords

  • HPCP
  • Hand detection
  • NCHOG
  • NCLBP
  • PLS

Fingerprint

Dive into the research topics of 'Human hand detection using robust local descriptors'. Together they form a unique fingerprint.

Cite this