Automatic extraction and description of human gait models for recognition purposes
Computer Vision and Image Understanding
A bayesian approach to image-based visual hull reconstruction
CVPR'03 Proceedings of the 2003 IEEE computer society conference on Computer vision and pattern recognition
Self-calibrating view-invariant gait biometrics
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics - Special issue on gait analysis
Human identification based on gait paths
ACIVS'11 Proceedings of the 13th international conference on Advanced concepts for intelligent vision systems
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Based on a three-dimensional (3D) linear model and the Bayesian rule, a method is explored to identify human walkers from two-dimensional (2D) motion sequences taken from different viewpoints. Principal component analysis constructs the 3D linear model from a set of Fourier represented examples. The sets of coefficients derived from projecting 2D motion sequences onto the 3D model by means of a maximum a posterior estimate is used as a signature of a walker. Simulating an identification experiment on a set of walking data we show that these signatures show invariance across viewpoints and can be used for viewpoint-independent person identification.