Upload
david-boyd
View
217
Download
2
Tags:
Embed Size (px)
Citation preview
Automatic Shoeprint Retrieval System Automatic Shoeprint Retrieval System for use in Forensic Investigationsfor use in Forensic Investigations
Lin ZhangLin Zhang and and Nigel AllinsonNigel Allinson
Electronic Systems Design Research GroupDepartment of Electronic & Electrical EngineeringUniversity of Sheffield
Crime Scene Investigation
DNA
Shoeprint
Bullet
Cartridge case of firearm
FingerprintMake unique Make unique identificationidentification
Helpful in Helpful in recognitionrecognition
Face
Shoeprint Recognition
Shoeprints are often found at crime scenes and contribute considerably to forensic intelligence.
Identify linked crime scenes
Link suspects in custody to other crime scenes
Permit the targeting of prolific offenders
Provide strong courtroom evidence when detailed matching of mark and shoe exist
Database of impressions made by shoes available on the market
Database of footwear impressions found at other crime scenes
Database of impressions made by shoes from suspects
Forensic analysis requires comparison of shoeprint images against specific databases.
An image of the shoeprint can be obtained using photography, gel or electrostatic lifting or by making a cast when the impression is in soil.
Current solutions
There’s a danger of inconsistency between the codes and users.
Modern shoes have increasingly more intricate outsole patterns that are difficult and tedious to describe with only a few basic coding shapes.
Manually: search through paper catalogues
Slow, tedious, need considerable training!!!
Semi-automatically: Computer databasesHuman coding of shoeprint outsole patterns based on shape primitives (e.g. lines, circles, logos, zigzag, etc)
Aim Aim
The aim of this study is to develop a fully automatic shoeprint recognition system.
Sort the database in response to a query imageSort the database in response to a query image
Functions with minimum user interventionFunctions with minimum user intervention
Insensitive to scale, rotational and translational Insensitive to scale, rotational and translational variance in query imagevariance in query image
Database Database
A subset of 512 images from Foster & Freeman Ltd’s shoeprint database SoleMate (includes over 8000 different sole patterns)
System Overview System Overview
Feature Extraction
Feature ExtractionShoeprint
Image Databas
e
Pre Processing
Query
Correct Match
Pattern Matching
Display Ranked
list of Images
User SelectionUser Selection
image pre-processing feature extraction pattern matching
Image pre-processing Image pre-processing
PDE (partial differential equation)-based de-noising approach to implement edge preserving smoothing under controlled curvature motion
Evolving the image I as a surface is equivalent to repeatedly iterating the edge-preserving anisotropic filter:
2 2
1 2 2 3/ 2
(1 ) 2 (1 )
2(1 )xx y x y xy yy x
t tx y
I I I I I I II I
I I
Results of applying the filter to typical noisy images for 40 iterations. Noise effects are attenuated and useful edges are preserved.
Feature Extraction Feature Extraction
Canny edge detector edge image
An edge direction histogram of 72 bins is used to record edge directions quantized at 5o intervals.
Matching such histograms is sensible to rotational and scale variances
Normalize the histogram
( ) ( ) / , [0,1,...,71]eH i H i n i
H(i): count in bin I
ne: total number of edge points
The DFT coefficient vector is used as the feature extracted from image.
Calculate 1-D DFT coefficients on the normalized histogram
Pattern Matching Pattern Matching
0 10 20 30 40 50 60 70 800
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
0 10 20 30 40 50 60 70 800
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
DFT vector
of input image
DFT vectorof database
images
Euclideandistance
Sorted list of database images
Images with similar patterns as query image will stay on TOP of the ranking list.
Edgeimage
0 10 20 30 40 50 60 70 800
0.01
0.02
0.03
0.04
0.05
0.06
0.07
0.08
0.09
0.1
0 10 20 30 40 50 60 70 800
0.01
0.02
0.03
0.04
0.05
0.06
0.07
0.08
0.09
0 10 20 30 40 50 60 70 800
0.005
0.01
0.015
0.02
0.025
0.03
0.035
0.04
0.045
NormalizedEdge direction
histogram
0 10 20 30 40 50 60 70 800
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
0 10 20 30 40 50 60 70 800
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
0 10 20 30 40 50 60 70 800
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
DFT vector
De-noised shoeprint
(a)
(b)
(c)d(a,b)=0.260; d(a,c)=0.911; d(b,c)=0.802
AccuracyAccuracyRank the 512 database images from best to worst match and return top 20 for further investigation.
Stability Stability Original: All images in the database. Rotated: Every image in the database rotated randomly and
used as query. Scaled: Every image in the database scaled randomly and used
as query. Noisy: Random noise added to every image in the database
and used as query.
SpeedSpeed matching process needs about 1s for one image (excluding the pre-processing time)
Experimental Results Experimental Results
2 4 6 8 10 12 14 16 18 2030
40
50
60
70
80
90
100
110
n
%
OriginalRotated
ScaledNoised(10%)Noised(20%)
Probability of correct retrieval in the first n positions
Query result n =1(%)
n ≤ 2(%)
n ≤ 3(%)
n ≤ 4(%)
n ≤ 5(%)
n ≤ 20(%)
Not Retrieved(%)
Original 99.41 100 100 100 100 100 0
Rotated 48.83 58.97 63.87 67.77 71.09 87.50 12.50
Scaled 96.09 97.27 97.66 98.05 98.05 99.61 0.39
Noisy (10%) 85.35 90.04 92.77 93.95 94.53 97.66 2.34
Noisy (20%) 57.42 62.89 67.19 69.73 71.29 84.96 15.04
Pre-align images: rotated about the centroid, major axis parallel to y-axis
Highly deteriorated
Future workFuture work
Identify partial shoeprintsIdentify partial shoeprints
Incorporate some neural network Incorporate some neural network methodsmethods
Investigate and test alternative de-Investigate and test alternative de-noising methodsnoising methods