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Robust View Transformation Model For Gait Recognition

Robust View Transformation Model For Gait Recognition. Shuai Zheng TNT group meeting 1/12/2011. Outline. Paper Tracking Robust view transformation model for gait recognition. Paper Tracking.

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Robust View Transformation Model For Gait Recognition

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  1. Robust View Transformation Model For Gait Recognition Shuai Zheng TNT group meeting 1/12/2011

  2. Outline • Paper Tracking • Robust view transformation model for gait recognition

  3. Paper Tracking • Context-aware fusion: A case study on fusion of gait and face for human identification in video, 2010, Pattern Recognition. Comments: This paper introduce how to combine multi biometrics in context-aware way. Great summary for the existing work. New trends in long distance biometrics.

  4. Paper Tracking • Effective Unconstrained Face Recognition by Combining Multiple Descriptors and Learned Background Statistics.2010, PAMI. Comments: How to write a experimental paper? That’s a model.

  5. Paper Tracking • Cost-sensitive Face Recognition, Zhi-Hua Zhou, PAMI, 2010. Comments: Good motivation: False identification, false rejection, false acceptance are three different criteria, how to consider the whole cases together? To reduce the expectation of whole cost? Multiclass cost-sensitive KLRseems the point of the paper.

  6. Robust view transformation modelfor gait recognition Shuai Zheng, Junge Zhang, Kaiqi Huang, Tieniu Tan, Ran He.

  7. Robust view transformation model for gait recognition • Motivation • Motivation • Motivation from related work • Introduction • Experimental results • Conclusions and Future work

  8. Motivation • Robust gait representation should be robust to appearance variation caused by the change in viewing angle, carrying or wearing condition.

  9. Motivation from related work • Shared gait representation subspace should be assumed as low-rank. Related Work Handmade Low-Rank Truncated Singular Decomposition (TSVD) seems achieved better than original SVD in recent papers on multi-view gait recognition. Robust low-rank method achieved exciting performance in background modeling, face recognition.

  10. Introduction We present a Robust View Transformation model and Partial Least Square feature selection algorithm for multi-view gait recognition.

  11. Introduction GEI from different views Low-rank appx A + Sparse error E Optimized GEI =

  12. Introduction

  13. Introduction

  14. Introduction GEI

  15. Introduction

  16. Experimental results A Bag? Remove it as noise. A overcoat? Remove it as noise. See? What a impressive results of robust View Transformation model for gait representation!

  17. Experimental results

  18. Experimental results

  19. Experimental results

  20. Experimental results

  21. Experimental results

  22. Conclusions • The proposed method achieves significant performance on the multi-view gait recognition dataset with additional variations caused by wearing or carrying condition change.

  23. Future work sequel • How about the improved low-rank method for other challenge gait recognition dataset? • How about that for visual surveillance system? • Can we achieve super gait recognition? Achieved 99% recognition rates at any viewing angle? How about combine the method with rectified method?

  24. Thanks! No question? no reward!~

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