Date post: | 15-Jan-2016 |
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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