Human Identification at a Distance

UMD's Evaluation Results on MIT Data



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The experiments explore the robustness of gait appearance across different days.

Note: For all experiments, the training and gallery sets are the same.

 
MIT AI Data

Experiment #2

A. Day 030101 vs. days 031601, 032201, and 051001

Probe sequences Gallery sequences
Result (HMM Approach) Similarity matrix (image) Similarity matrix (.mat)
Cumulative Match Score Similarity files
Probe sequences Gallery sequences
Result (Algorithm II) Similarity matrix (image) Similarity matrix (.mat)
Cumulative Match Score Similarity files

B. Day 031601 vs. days 030101, 032201, and 051001

Probe sequences Gallery sequences
Result (HMM Approach) Similarity matrix (image) Similarity matrix (.mat)
Cumulative Match Score Similarity files
Probe sequences Gallery sequences
Result (Algorithm II) Similarity matrix (image) Similarity matrix (.mat)
Cumulative Match Score Similarity files

C. Day 032201 vs. days 030101, 031601, and 051001

Probe sequences Gallery sequences
Result (HMM Approach) Similarity matrix (image) Similarity matrix (.mat)
Cumulative Match Score Similarity files
Probe sequences Gallery sequences
Result (Algorithm II) Similarity matrix (image) Similarity matrix (.mat)
Cumulative Match Score Similarity files

D. Day 051001 vs. days 030101, 031601, and 032201

Probe sequences Gallery sequences
Result (HMM Approach) Similarity matrix (image) Similarity matrix (.mat)
Cumulative Match Score Similarity files
Probe sequences Gallery sequences
Result (Algorithm II) Similarity matrix (image) Similarity matrix (.mat)
Cumulative Match Score Similarity files

A note on the results using HMM approach

An element essential to our training/testing philosophy is the HMM model that we build for every subject in the database. With every additional labeled incoming sequence during training, we update the model for that particular subject. Therefore, the number of models built at the end of a training procedure is equal to the of subjects present in that particular experiment. To find a common ground between the experiments designed by MIT and our algorithm, we have reorganized the MIT dataset as follows:

MIT convention:         day --> sequence

Modified convention:  day --> subject --> sequences of the subject

And performed the experiments as designed by MIT (training with data from 3 days and testing with the 4th). 

 

 



Site First Created: 8/3/01 | Last Modified: 04/11/2002