gait fustion gait recognition based on fusion of about ...€¦ · fusion rules system overview...

Gait Fustion

About GaitRecognition

Database

Algorithms andFusion RulesSystem Overview

Gait RecognitionAlgorithm

Fusion Rules

Experiments

Conclusions

Gait Recognition Based on Fusion ofMulti-view Gait Sequences

Presented by Shiqi Yu

Yuan Wang1, Shiqi Yu1, Yunhong Wang2, and Tieniu Tan1

1. Institute of Automation, Chinese Academy of Sciences

2. School of Computer Science and Engineering, Beihang University

Jan. 5, 2006. Hong Kong

Gait Fustion


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Outline

1 About Gait Recognition

2 CASIA Gait Database

3 Algorithms and Fusion Rules

System Overview

Gait Recognition Algorithm

Fusion Rules

4 Experimental Results and Analysis

5 Conclusions and Future Work

Gait Fustion


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About Gait Recognition

Gait: A particular way or manner of moving on foot.

Earlier research shows that gait can be used as abiometric for personal identification.

Gait Fustion


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About Gait Recognition: Advantages

Gait has several unique advantages:

Perceivable at a distance.It is the greatest difference compared with other biometrics. Itmakes gait usable in visual surveillance.Non-invasiveNon-contact

Figure: VStar visual surveillance platform

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About Gait Recognition: Disadvantages

Disadvantages:

Gait is not stable enough, and can be influenced byweight, health condition, emotion, clothes, shoes etc.

[Chris Kirtley, Psychological Influences on Gait]

Gait Fustion


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Current Problems

Currently, most gait recognition algorithms depend on aspecific view, and can be easily affected by view, clothing,time or other factors.

Fusion of multi-view gait sequences is a solution forimproving the robustness to view change.

But this idea has not been well addressed before.

Gait Fustion


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Our work

Our work focuses on

constructing a gait recognition system based onfusion of multi-view sequences,investigating which fusion rule is suitable forcombining of multi-view sequences,investigating which two views can achieve the bestperformance.

Gait Fustion


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System Overview


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Gait Fustion


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CASIA Gait Database

CASIA Gait Database A was created in 2001.

20 subjects3 views240 video sequences

CASIA Gait Database B was created in 2005.

124 subjects ( 94 males and 30 females)11 views3 conditions (normal walking, walking with a coat andwalking with a bag)13640 (124 × 11 × 10) video sequences

Gait Fustion


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CASIA Gait Database B

Network

Figure: Set-up for gait data collection

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(a) Video from 11 views

(b) Normalwalking

(c) Walking witha coat

(d) Walking witha bag

Gait Fustion


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(e) Video from 11 views

(f) Normal walk-ing

(g) Walking witha coat

(h) Walking witha bag

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CASIA Gait Database

CASIA Gait Database (subset A and B) is in the publicdomain.

For details, please visit:

http://www.cbsr.ia.ac.cn

Gait Fustion


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System Overview


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Gait Fustion


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System Overview

Two views were combined to improve the performance.

Figure: Fusion system

Gait Fustion


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The gait algorithm which we used is described in paper[Yu, ICIG’04], which is named as Key Fourier Descriptors(KFDs).

Each point on the contour can be represented as:

si = xi + j · yi

The contours in a sequence can be represented as a longvector:

[s1, s2, · · · , sN , sN+1, sN+2, · · · , s2N , · · · ]

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We can obtain Fourier descriptors(coefficients) by discreteFourier transform(DFT)

fn =M−1∑m=0

zne−j(2π/M)nm

for n = 0, 1, 2, · · · , N − 1

Key Fourier Descriptor (KFD) feature is defined as:

F =1

|f (T )|[|f (2T )|, |f (3T )|, · · · , |f ((N − 1)T )|]

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Fusion Rules

The output of N systems is X = [x1, x2, · · · , xN ].

Four fusion rules are used:

1. Sum rule

x =N∑

i=1

xi

2. Product rule

x =N∏

i=1

xi

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Fusion Rules

3. Weighted sum rule

x =N∑

i=1

wi · xi

where wi =ERR−1

iPNj=1 ERR−1

j.

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Fusion Rules

4. Dempster-Shafer (D-S) ruleIn this frame of the evidence theory, the bestrepresentation of support is a belief function ratherthan a Bayesian mass distribution. D-S theoryconcerns the combination of degrees of belief and issuitable for fusion.

Gait Fustion


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Gait Fustion


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Experiments

Normal walking sequences were used in the experiments.

Two views were combined to improve the performance.

There are 11 views, and totally C211 = 55 different fusion

pairs.

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Experiments

Normal walking sequences were used in the experiments.Two views were combined to improve the performance.

There are 11 views, and totally C211 = 55 different fusion

pairs.

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Experimental Results

Rule non-fusion Sum Product W-Sum D-SEER 11.49% 9.08% 8.56% 8.85% 3.81%

Table: The average EERs (Equal Error Rates) of the fusionsystems

D-S rule has the best performance.

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Experimental Results

In some cases fusion system can not gain improvementcompared with the best single system.

Rule Sum Product W-Sum D-SNumber 7 6 2 0

Table: The number of experiments failed to gain improvement

The trained rules (W-Sum and D-S) are better than fixedrules (Sum and Product) in multi-view gait recognition.

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Experimental Results: D-S

Improvement of the D-S fusion system

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The improvement of fusion system

Angle 2

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(%)

z: the difference between the EER of fusion system andthe lower EER of single systems.

Gait Fustion


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Experimental Results: D-S

Improvement of the D-S fusion system

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The improvement of fusion system

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If the two views are respectively from range [0◦, 90◦] and[90◦, 180◦], greater improvement can be achieved.

Gait Fustion


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Outline




System Overview


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Gait Fustion


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Conclusions

A gait recognition scheme based on fusion of multi-viewgait sequences is proposed.

Fusion can improve the performance of gaitrecognition.

Dempster-Shafer fusion rule can achieve greaterimprovement than other fusion rules.If the two views are respectively from range [0◦, 90◦]and [90◦, 180◦], greater improvement can beobtained.

Gait Fustion


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Conclusions


Fusion can improve the performance of gaitrecognition.Dempster-Shafer fusion rule can achieve greaterimprovement than other fusion rules.

If the two views are respectively from range [0◦, 90◦]and [90◦, 180◦], greater improvement can beobtained.

Gait Fustion


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Fusion Rules

Experiments

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Conclusions


Fusion can improve the performance of gaitrecognition.Dempster-Shafer fusion rule can achieve greaterimprovement than other fusion rules.If the two views are respectively from range [0◦, 90◦]and [90◦, 180◦], greater improvement can beobtained.

Gait Fustion


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Fusion Rules

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Future Work

Our future work:

Feature level fusion of gait recognitionView invariant gait feature representation

Gait Fustion


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Shiqi YU

Center for Biometrics and Security Research (CBSR)http://www.cbsr.ia.ac.cn

National Laboratory of Pattern Recognition (NLPR),Institute of Automation, Chinese Academy of Sciences(CASIA)http://www.nlpr.ia.ac.cn

sqyu @ nlpr.ia.ac.cn

gait fustion gait recognition based on fusion of about ...€¦ · fusion rules system overview...

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