Hand gesture recognition using Histogram of Oriented Gradients and Partial Least Squares regression

Arindam Misra, Abe Takashi, Takayuki Okatani, Koichiro Deguchi

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

18 Citations (Scopus)

Abstract

In this paper we propose a real-time hand gesture recognition system that employs the techniques developed for pedestrian detection to recognize a small vocabulary of human hand gestures. Our feature set comprises of grids of Histogram of Oriented Gradient (HOG) descriptors, with fine orientation binning and multi-level spatial binning for getting descriptors at the small as well as large scale. The overlapping descriptor blocks, which are contrast normalized to handle illumination changes, have a high degree of multicollinearity, resulting in a feature set of high dimensionality (more than 8000 dimensions), rendering it unsuitable for classification using the classical machine learning algorithms. Thus, we employ Partial Least Squares (PLS) regression as a 'class aware' method of dimensionality reduction, to project the feature vectors on to a lower dimensional space of 10 dimensions. We examine the results obtained by PLS as well as Principal Component Analysis (PCA) which show, that PLS outperforms PCA, and gives a better projection which preserves significant discriminative information.

Original languageEnglish
Title of host publicationProceedings of the 12th IAPR Conference on Machine Vision Applications, MVA 2011
Pages479-482
Number of pages4
Publication statusPublished - 2011 Dec 1
Event12th IAPR Conference on Machine Vision Applications, MVA 2011 - Nara, Japan
Duration: 2011 Jun 132011 Jun 15

Publication series

NameProceedings of the 12th IAPR Conference on Machine Vision Applications, MVA 2011

Other

Other12th IAPR Conference on Machine Vision Applications, MVA 2011
CountryJapan
CityNara
Period11/6/1311/6/15

ASJC Scopus subject areas

  • Computer Vision and Pattern Recognition

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