Transcript Microsoft PowerPoint - University of Ulster

PRWGEI: Poisson Random Walk based
Gait Recognition
Pratheepan Yogarajah, Joan V. Condell, Girijesh Prasad
Intelligent Systems Research Centre
School of Computing and Intelligent Systems,
University of Ulster, Northern Ireland, UK.
http://isrc.ulster.ac.uk
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Overview
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The Problem statement
Existing methods
Our solution
Experimental Results
Summary
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The Problem
 Performance of Gait Recognition under different
Covariate Conditions.
● What?
– Gait recognition is recognizing people by
the way they walk.
● Why?
– Gait recognition is non intrusive
– Operates at a distance without subject
cooperation.
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Covariate Conditions?
• Conditions that effect gait.
• Can be divided into two categories
1) Effecting features extracted from gait
- Carrying Condition, Clothing Condition, View etc
2) Effecting gait itself
- Shoes, Time, Injury, Speed etc
Normal
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Carrying Condition
Clothing Condition
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Gait feature representation
1. Appearance-Based Methods：
Appearance-based approaches are widely used in gait
representation. They directly represent human motion
using image information, such as a silhouette, an
edge, and an optical flow.
2. Model-Based Methods：
Model-based approaches represent a gait with body
segments, joint positions, or pose parameters.
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Existing appearance-based works
GEI
PAMI 06
GEnI
PRL 10
MG
PRL 10
EGEI
SP 08
AEI
SP 10
• Even though these gait feature representations show good
recognition rate, their average recognition rates are not that
promising (i.e. less than 75%).
• This indicates that a more robust appearance-based gait
feature representation is needed.
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Our Solution
• Parts structure based GEI (PRWGEI).
• Apply Poisson Random Walk approach to
binary silhouette.
• Extract various properties of shape
analysis
Binary
image
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Transformed
image
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Poisson Equation
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Random walk
Mean time to
hit the boundary
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Poisson based shape representation
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PRWGEI
The columns from left to right represent PRWGEI features for normal, carrying objects
and different clothing covariate factors.
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Recognition
• The
PCA+LDA is applied to reduce the
dimension of the data and also to improve the
discriminative power of the extracted features.
• Then the k-nearest neighbour (k-NN) is applied
to classify the data and make a decision, i.e.
decide the identity of a person.
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Subspace of dimensionality - (PCA)
140 provides better recognition rate
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Results (CASIA-B)
• 124 individuals with normal(6), carrying(2) and clothing(2)
conditions.
• training : 124 individuals with normal(4) – CasiaSetA1
• testing : 124 individuals with normal(2) – CasiaSetA2,
carrying(2) – CasiaSetB and clothing(2) - CasiaSetC
GEI
GEnI
MG
AEI
PRWGEI
CasiaSetA2
99.4%
98.3%
100%
88.7%
98.4%
CasiaSetB
60.2%
80.1%
78.3%
75.0%
93.1%
CasiaSetC
30.0%
33.5%
44.0%
57.3%
44.4%
Overall
63.2%
70.6%
74.1%
73.7%
78.6%
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Summary
• Gait Energy Image representation based on
Poisson random walk approach
• Our proposed novel gait features provide better
results for person identification with CASIA-B
dataset.
• As a future direction, we would like to test our
method with dataset such as USF dataset and
SOTON dataset.
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Thank you
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