Human Identification using Silhouette Gait Data Rutgers University Chan-Su Lee

Problem of Gait Recognition ● Advantage of gait as human identification – Difficult to disguise – Observable in a distance ● Difficulty of gait recognition – Existance of various source of variation: viewpoint, clothing, walking surface, shoe type, etc. – Spatio-temporal image sequence: Huge data, variation in speed->difficult to compare

Standard Embedding of Gait Cycle ● Dimensionality of gait cycle – One dimensional manifold in 3D space – Half cycle->2D space with cycle – Standard embedding on circles

Bilinear Models for Gait ● Gait Style – Time invariant personalized style of the gait ● Gait Content – Variant factor depend on time and viewpoint, shoes, and so on – Represented by different body pose

Gait recognition algorithm(I) ● Asymmetric Model ● Symmetric Model

Gait recognition algorithm (II) ● Adaptation to new situation – Learn new factor by modifying content vector – Find style factor using new content vector

Experiment Results ● Improvement by normalized gait – 14 peoples – 3 different factors

Demos Original Gait Data(GAR) Different Surface(CAR) Silhouette Images(GAR)Silhouette Images(CAR) Filtered Silhouette Images(GAR) Implicit Function Representation of Silhouette Images(GAR) Normalized Gait Image Sequence(GAR)

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