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AUTOMATIC CLASSIFICATION AND RECOGNITION OF

SHOEPRINTS

DAI .WEIYUN

BACKGROUND

match collected impression against known shoeprint database shoeprint impressions as common clues left at a crime scene need to detect criminal and infer the crime scene

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INTRODUCTION emphasize the importance of shoeprint SIGNIFICANT figure---approximately 30% of burglaries leave

shoeprint a typical application-identify a suspect methods of matching impression &limitations ①Manually search through paper catalogues ②semi-automatically using a computer database (why the most serious?) ③a manual classification system difficult and hard to agree on a classification

NOW PROBLEM—“only 3.5% of recovered prints were identified”, Netherlands

THE REASON AND HOW TO IMPROVE SEE NEXT SLIDE

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CURRENT SYSTEM

Existing system --accurate classification & problems ①Limit the number of user ②Hard to deal with the increase in the number of

imprint patterns Are accidental characteristics important in classification??

NEW IDEA--automatic classification & advantages (CORE TODAY)

What? How?

Adv--- no need to manually classify remove amount of training

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AUTOMATIC IMAGE MATCHING FOR A SHOE PRINT DATABASE fractal pattern matching –remove classification process and repla

ce it It involves two main steps 1. fractal decomposition analysis

During this processing, the smallest change is produced MSNE (Mean Square Noise Error)---compute the smallest ch

ange -----determine image with minimum changes with respect to im

age under test

get the best match in practice, BUT not the best.

fractal decomposition analysis

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Get better result in reality --use 3X3 template window

--compare the mean value of each window

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THE SYSTEM

Graphical user interface

the user can control the search the user can control the search

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EXPERIMENT1 decide the best standard method

--MSNE on mean smoothed images to get the best results EXPERIMENT2 determine the fractal technique’s robustness in rotated f

orms of shoe print

SYSTEM RESULTSHOW TO GET IT, JUST DO EXPERIMENTS

Shows number of correctly identified shoe impressions at R (degree) rotation

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EXPERIMENT3 determine it in translated forms of an shoe print

Shows number of correct shoe impressions identified at translation T x :y pixels

NORMALIZATION PROCESS NORMALIZE IMAGE BEFORE ENTERING SHOEPRINTS INTO THE COMPTER

IMPORTANT APPLICATION --identifying partial image

--remove or reconstruct details

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FUTURE WORK

Now:

focus on the matching of partial impressions

Future:

1.try to get efficient partition size

2.an alternative partitioning scheme

3.extend the tests in scale and grey level range

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USEFULL BOOKS TO HELP UNDERSTANDING • 1. Alexandre G, “Computerised classification of the shoeprints of burglars’ soles.”, Forensic Science International, 82 (1996) 59-65• 2. Hamm E, “Track identification: an historical overview.”, J. Forensic Identification, 39 (1989)• 3. Informal conversation with Nick Mitchell of the Surrey Police Force.• 4. Rankin B, “Footwear marks - A step by step review.”, Forensic Science Societv Newsletter April (1998) 3• 5. Geradts Z et al, “The image-database REBEZO for shoeprints with developments on automatic classification of shoe outsole designs.”, Forensic Science International, 82 (1996) 21-31• 6. Belser Ch et al, “Evaluation of the ISAS system after two years of practical experience in forensic police work.”, Forensic Science International, (1996) 53-58

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ANY QUESTIONES???