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Multilinear Modeling for Robust Identity Recognition from Gait
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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