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P RW GEI: Poisson Random Walk based Gait Recognition

P RW GEI: Poisson Random Walk based Gait Recognition. Intelligent Systems Research Centre School of Computing and Intelligent Systems, University of Ulster, Northern Ireland, UK. Pratheepan Yogarajah , Joan V. Condell, Girijesh Prasad. Overview. The Problem statement Existing methods

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P RW GEI: Poisson Random Walk based Gait Recognition

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  1. PRWGEI: Poisson Random Walk based Gait Recognition Intelligent Systems Research Centre School of Computing and Intelligent Systems, University of Ulster, Northern Ireland, UK. PratheepanYogarajah, Joan V. Condell, Girijesh Prasad

  2. Overview • The Problem statement • Existing methods • Our solution • Experimental Results • Summary

  3. 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.

  4. 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

  5. Gait feature representation 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. Model-Based Methods： Model-based approaches represent a gait with body segments, joint positions, or pose parameters.

  6. 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.

  7. Our Solution • Parts structure based GEI (PRWGEI). • Apply Poisson Random Walk approach to binary silhouette. • Extract various properties of shape analysis Binary image Transformed image

  8. Poisson Equation

  9. Random walk Mean time to hit the boundary

  10. Poisson based shape representation

  11. PRWGEI The columns from left to right represent PRWGEI features for normal, carrying objects and different clothing covariate factors.

  12. 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.

  13. Subspace of dimensionality - (PCA) 140 provides better recognition rate

  14. 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

  15. 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.

  16. Thank you

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