Gait style and gait content: Bilinear models for gait recognition using gait re-sampling

Chan Su Lee, Ahmed Elgammal

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

47 Scopus citations

Abstract

Human Identification using gait is a challenging computer vision task due to the dynamic motion of gait and the existence of various sources of variations such as viewpoint, walking surface, clothing, etc. In this paper we propose a gait recognition algorithm based on bilinear decomposition of gait data into time-invariant gait-style and time-dependent gait-content factors. We developed a generative model by embedding gait sequences into a unit circle and learning nonlinear mapping which facilitates synthesis of temporally-aligned gait sequences. Given such synthesized gait data, bilinear model is used to separate invariant gait style which is used for recognition. We also show that the recognition can be generalized to new situations by adapting the gait-content factor to the new condition and therefore obtain corrected gait-styles for recognition.

Original languageEnglish (US)
Title of host publicationProceedings - Sixth IEEE International Conference on Automatic Face and Gesture Recognition FGR 2004
Pages147-152
Number of pages6
DOIs
StatePublished - 2004
EventProceedings - Sixth IEEE International Conference on Automatic Face and Gesture Recognition FGR 2004 - Seoul, Korea, Republic of
Duration: May 17 2004May 19 2004

Publication series

NameProceedings - Sixth IEEE International Conference on Automatic Face and Gesture Recognition

Other

OtherProceedings - Sixth IEEE International Conference on Automatic Face and Gesture Recognition FGR 2004
Country/TerritoryKorea, Republic of
CitySeoul
Period5/17/045/19/04

All Science Journal Classification (ASJC) codes

  • Engineering(all)

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