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PRWGEI: Poisson Random Walk based Gait Recognition

Intelligent Systems Research CentreSchool of Computing and Intelligent Systems,

University of Ulster, Northern Ireland, UK.

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Pratheepan Yogarajah, Joan V. Condell, Girijesh Prasad

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The Problem statement Existing methods Our solution Experimental Results Summary

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Overview

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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 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

GEIPAMI 06

EGEISP 08

GEnIPRL 10

MG

PRL 10AEI

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

Binaryimage

Transformedimage

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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)

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%

• 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

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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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