Open AccessProbabilistic gait modelling and recognition
Author(s) -
Hong Sungjun,
Lee Heesung,
Kim Euntai
Publication year - 2013
Publication title -
iet computer vision
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.38
H-Index - 37
eISSN - 1751-9640
pISSN - 1751-9632
DOI - 10.1049/iet-cvi.2011.0234
Subject(s) - silhouette , probabilistic logic , gait , biometrics , computer science , artificial intelligence , benchmark (surveying) , pattern recognition (psychology) , bernoulli distribution , statistical model , mixture model , computer vision , random variable , mathematics , statistics , physiology , geodesy , biology , geography
Biometric researchers have recently found considerable applicability of gait recognition in visual surveillance systems. This study proposes a probabilistic framework for gait modelling that is applied to gait recognition. The basic idea of this framework is to consider the silhouette shape as a multivariate random variable and model it in a full probabilistic framework. The Bernoulli mixture model is employed to model silhouette distribution and recursive algorithms are provided for silhouette image and sequence classification. Finally, the proposed probabilistic method is applied to benchmark databases and its validity is demonstrated through experiments.