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Human Identification using Silhouette Gait Data

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Human Identification using Silhouette Gait Data - PowerPoint PPT Presentation


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Rutgers University Chan-Su Lee. Human Identification using Silhouette Gait Data. Problem of Gait Recognition. Advantage of gait as human identification Difficult to disguise Observable in a distance. Difficulty of gait recognition

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Presentation Transcript
problem of gait recognition
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
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
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
Gait recognition algorithm(I)
  • Asymmetric Model
  • Symmetric Model
gait recognition algorithm ii
Gait recognition algorithm (II)
  • Adaptation to new situation
    • Learn new factor by modifying content vector
    • Find style factor using new content vector
experiment results
Experiment Results
  • Improvement by normalized gait
    • 14 peoples
    • 3 different factors
demos
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)