Human Identification using Silhouette Gait Data

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

2. 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

3. 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

4. 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

5. Gait recognition algorithm(I) • Asymmetric Model • Symmetric Model

6. Gait recognition algorithm (II) • Adaptation to new situation • Learn new factor by modifying content vector • Find style factor using new content vector

7. Experiment Results • Improvement by normalized gait • 14 peoples • 3 different factors

8. 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)

9. Others