Human Identification using

Silhouette Gait Data

Rutgers UniversityChan-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)

Others