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access icon free Probabilistic gait modelling and recognition

© The Institution of Engineering and Technology
Received 05/12/2011, Accepted 07/10/2012, Revised 25/08/2012, Published

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.

Inspec keywords: recursive estimation; image sequences; video surveillance; image classification; visual databases

Other keywords: probabilistic gait modelling; visual surveillance systems; Bernoulli mixture model; silhouette distribution model; benchmark databases; silhouette image; multivariate random variable; recursive algorithms; sequence classification; gait recognition

Subjects: Other topics in statistics; Other topics in statistics; Spatial and pictorial databases; Video signal processing; Image recognition; Computer vision and image processing techniques

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