1. Shuai Zheng TNT group meeting 1/12/2011

2.  Paper Tracking  Robust view transformation model for gait recognition

3.  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.  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.  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 KLR seems the point of the paper.

6. Shuai Zheng, Junge Zhang, Kaiqi Huang, Tieniu Tan, Ran He.

7.  Motivation  Motivation  Motivation from related work  Introduction  Experimental results  Conclusions and Future work

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

9.  Shared gait representation subspace should be assumed as low-rank. 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. Related Work

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

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

14. GEI

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

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

23. 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. No question? no reward!~