Gesture recognition using HLAC features of PARCOR images and HMM based recognizer

Takio Kurita, Satoru Hayamizu

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

23 Citations (Scopus)

Abstract

The paper proposes a gesture recognition method which uses higher order local autocorrelation (HLAC) features extracted from PARCOR images. To extract dominant information from a sequence of images, the authors apply a linear prediction coding technique to the sequence of pixel values and PARCOR images are constructed from the PARCOR coefficients of the sequences of the pixel values. From the PARCOR images, HLAC features are extracted and the sequences of the features are used as the input vectors of the hidden Markov model (HMM) based recognizer. Since HLAC features are inherently shift-invariant and computationally inexpensive, the proposed method becomes robust to changes of shift of the person's position and makes real-time gesture recognition possible. Experimental results of gesture recognition are shown to evaluate the performance of the proposed method.

Original languageEnglish
Title of host publicationProceedings - 3rd IEEE International Conference on Automatic Face and Gesture Recognition, FG 1998
PublisherIEEE Computer Society
Pages422-427
Number of pages6
ISBN (Print)0818683449, 9780818683442
DOIs
Publication statusPublished - 1998
Externally publishedYes
Event3rd IEEE International Conference on Automatic Face and Gesture Recognition, FG 1998 - Nara, Japan
Duration: 1998 Apr 141998 Apr 16

Publication series

NameProceedings - 3rd IEEE International Conference on Automatic Face and Gesture Recognition, FG 1998

Conference

Conference3rd IEEE International Conference on Automatic Face and Gesture Recognition, FG 1998
Country/TerritoryJapan
CityNara
Period98/4/1498/4/16

ASJC Scopus subject areas

  • Computer Vision and Pattern Recognition

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