Automatic Shoeprint Retrieval System for use in Forensic Investigations Lin Zhang and Nigel Allinson Electronic Systems Design Research Group Department of Electronic & Electrical Engineering University of Sheffield

Crime Scene Investigation DNA Shoeprint Bullet Cartridge case of firearm Fingerprint Make unique identification Helpful in recognition 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 Theres 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 databases Human coding of shoeprint outsole patterns based on shape primitives (e.g. lines, circles, logos, zigzag, etc)

Aim The aim of this study is to develop a fully automatic shoeprint recognition system. Sort the database in response to a query image Functions with minimum user intervention Insensitive to scale, rotational and translational variance in query image

Database A subset of 512 images from Foster & Freeman Ltds shoeprint database SoleMate (includes over 8000 different sole patterns)

System Overview Feature Extraction Feature Extraction Shoeprint Image Databas e Pre Processin g Query Correct Match Pattern Matching Display Ranked list of Images User Selection image pre-processing feature extraction pattern matching

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: Results of applying the filter to typical noisy images for 40 iterations. Noise effects are attenuated and useful edges are preserved.

Feature Extraction Canny edge detector edge image An edge direction histogram of 72 bins is used to record edge directions quantized at 5 o intervals. Matching such histograms is sensible to rotational and scale variances Normalize the histogram H(i): count in bin I n e : 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 DFT vector of input image DFT vector of database images Euclidean distance Sorted list of database images Images with similar patterns as query image will stay on TOP of the ranking list.

Edge image Normalized Edge direction histogram DFT vector De-noised shoeprint (a) (b) (c) d(a,b)=0.260; d(a,c)=0.911; d(b,c)=0.802

Accuracy Accuracy Rank 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. Speed Speed matching process needs about 1s for one image (excluding the pre-processing time) Experimental Results

Probability of correct retrieval in the first n positions

Query resultn =1 (%) n 2 (%) n 3 (%) n 4 (%) n 5 (%) n 20 (%) Not Retrieved (%) Original Rotated Scaled Noisy (10%) Noisy (20%) Pre-align images: rotated about the centroid, major axis parallel to y-axis Highly deteriorated

Future work Identify partial shoeprints Incorporate some neural network methods Investigate and test alternative de- noising methods

Acknowledgments Foster + Freeman