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Multilinear Modeling for Robust Identity Recognition from Gait

Multilinear Modeling for Robust Identity Recognition from Gait

Fabio Cuzzolin
Copyright: © 2010 |Pages: 20
ISBN13: 9781605667256|ISBN10: 1605667250|ISBN13 Softcover: 9781616924041|EISBN13: 9781605667263
DOI: 10.4018/978-1-60566-725-6.ch008
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MLA

Cuzzolin, Fabio. "Multilinear Modeling for Robust Identity Recognition from Gait." Behavioral Biometrics for Human Identification: Intelligent Applications, edited by Liang Wang and Xin Geng, IGI Global, 2010, pp. 169-188. https://doi.org/10.4018/978-1-60566-725-6.ch008

APA

Cuzzolin, F. (2010). Multilinear Modeling for Robust Identity Recognition from Gait. In L. Wang & X. Geng (Eds.), Behavioral Biometrics for Human Identification: Intelligent Applications (pp. 169-188). IGI Global. https://doi.org/10.4018/978-1-60566-725-6.ch008

Chicago

Cuzzolin, Fabio. "Multilinear Modeling for Robust Identity Recognition from Gait." In Behavioral Biometrics for Human Identification: Intelligent Applications, edited by Liang Wang and Xin Geng, 169-188. Hershey, PA: IGI Global, 2010. https://doi.org/10.4018/978-1-60566-725-6.ch008

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Abstract

Human identification from gait is a challenging task in realistic surveillance scenarios in which people walking along arbitrary directions are viewed by a single camera. However, viewpoint is only one of the many covariate factors limiting the efficacy of gait recognition as a reliable biometric. In this chapter, we address the problem of robust identity recognition in the framework of multilinear models. Bilinear models, in particular, allow us to classify the “content” of human motions of unknown “style” (covariate factor). We illustrate a three-layer scheme in which image sequences are first mapped to observation vectors of fixed dimension using Markov modeling, to be later classified by an asymmetric bilinear model. We show tests on the CMU Mobo database that prove that bilinear separation outperforms other common approaches, allowing robust view- and action-invariant identity recognition. Finally, we give an overview of the available tensor factorization techniques, and outline their potential applications to gait recognition. The design of algorithms insensitive to multiple covariate factors is in sight.

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