Cyclostationary processes on shape spaces for gait-based recognition

6Citations
Citations of this article
19Readers
Mendeley users who have this article in their library.

Abstract

We present a novel approach to gait recognition that considers gait sequences as cyclostationary processes on a shape space of simple closed curves. Consequently, gait analysis reduces to quantifying differences between statistics underlying these stochastic processes. The main steps in the proposed approach are: (i) off-line extraction of human silhouettes from IR video data, (ii) use of piecewise-geodesic paths, connecting the observed shapes, to smoothly interpolate between them, (iii) computation of an average gait cycle within class (i.e. associated with a person) using average shapes, (iv) registration of average cycles using linear and nonlinear time scaling, (iv) comparisons of average cycles using geodesic lengths between the corresponding registered shapes. We illustrate this approach on infrared video clips involving 26 subjects. © Springer-Verlag Berlin Heidelberg 2006.

Cite

CITATION STYLE

APA

Kaziska, D., & Srivastava, A. (2006). Cyclostationary processes on shape spaces for gait-based recognition. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 3952 LNCS, pp. 442–453). Springer Verlag. https://doi.org/10.1007/11744047_34

Register to see more suggestions

Mendeley helps you to discover research relevant for your work.

Already have an account?

Save time finding and organizing research with Mendeley

Sign up for free