PRELIMINARY RESULTS TO DEFINE AN ACTIVE ...

2012 4th Workshop on Hyperspectral Image and Signal Processing (WHISPERS)

PRELIMINARY RESULTS TO DEFINE AN ACTIVE MULTISPECTRAL IMAGER FOR GUNSHOT RESIDUES PATTERNS VISUALIZATION

Ph. Delio/, B. CorcelleJ, V AchardJ, Th. DartigalongueJ, A. Desmaraii, C. Giacometti2 I ONERA, the French Aerospace Lab, DOTA, F-31055 Toulouse (France) 2 INPS, Institut National de Police Scientifique - Laboratoire de Police Scientifique de Marseille 97 Boulevard Camille Flammarion 13245 MARSEILLE (France)

As the particles are slowed down by air resistance, the

ABSTRACT

larger particles are traveling the greater distances [I]. In incidents involving gunshot wounds, for short range

However the pattern of GSR is significantly influenced by

target shooting only, gunshot residues (GSR) are projected

various other factors including the different muzzle-to­

on the target and a distinct pattern around the bullet

target angles, type of ammunition and weapon parameters,

entrance hole may be observed by the forensic experts. A

caliber and barrel length [2].

careful analysis of this pattern is often carried out in order to

Determination of the muzzle-to-target distance is often a

determine the muzzle-to-target distance and angle, and draw

critical factor in criminal and civil investigations involving

some conclusions about the nature of the firearm. However,

firearms. However, seeing and recording gunshot residue

seeing and recording gunshot residue patterns can be

patterns can be difficult if the victim's clothing is dark.

difficult especially if the victim's clothing is dark. The main

Direct observation is rarely possible and does not provide

goal of our study is to develop a multispectral laser imaging

quick and reliable viewing and saving of GSR patterns.

device able to reveal gunshot residue patterns on many

Methods are usually based on chemical reactions like the

kinds of clothes, which provide a fast result. The first stage

modified Griess test (MGT) for nitrites. To improve it, some

before developing this device is to acquire and analyze

authors propose the previous use of sodium hypochlorite to

hyperspectral images taken on samples in order to select the

bleach the dyes in the fabric to enhance visualization of the

best spectral bands to use and to develop the appropriate

GSR patterns [4]. There are very few non intrusive methods, like the Video

processing to enhance the pattern.

Spectral Comparator VSC 2000 [3] or with a Scanning Index

Terms-

Hyperspectral,

Gunshot

residues,

Electron Microscope / Energy Dispersive Spectroscope by (SEM / EDS). This last technique is the most widely used

classification, patterns visualization

because it is automated by computer and can provide both 1. INTRODUCTION

morphological information and elemental composition of

For solving many crimes the police appeal to science.

primer residues on the hands analysis. Anyway they could

particles. In France, this latest method is commonly used in However, if scientific expertise has benefited over the last

hardly be used on the crime scene.

twenty years of progress

As we can see, there is a need for a device which could

(detection, identification and

analysis of DNA ...), ballistic branch is facing serious

reliably and quickly visualize and save GSR patterns. This

difficulties

in

mean should be reproducible, therefore independent of

projectiles

on

analyzing

the

signature

produced

by may

illumination conditions. The chosen method for the study

penalize further investigation (determining the position of

and design of the demonstrator, meeting these expectations,

clothes

bearing

bullet

holes.

This

the shooter, confirmation of the weapon used ...) or at least

is

the validity of the expert's opinion. The use of active

lighting of the scene and high resolution of the sensor could

active

imaging.

This

technique,

combining

spectral

hyperspectral imaging hyperspectral for visualization of

be very powerful to reveal the image features that are

gunpowder residues aims to address a specific problem

invisible by other conventional techniques of imaging. The

raised

great wealth of spectral information will allow us to address

by

our

Institut

National de

Police

Scientifique

the wide variety of samples.

(National Institute of Forensic Science). Gunshot residue consists of a variety of materials: particles

As an approach, we propose to acquire spectra on different

of

samples by hyperspectral imaging means in the VNIR and

the

projectile,

combustion

partially

products

and

burnt smoke.

particles These

of

powder,

materials

are

projected from the muzzle of the weapon in a conical cloud.

SWIR spectral domain. As our goal is to propose an easy­ to-use demonstrator, we will focus on the VNIR spectral domain and try to reduce the number of necessary bands.

978-1-4799-3406-S/12/$31.00©2012IEEE

2012 4th Workshop on Hyperspectral Image and Signal Processing (WHISPERS)

Indeed, for a lot of applications it is usually possible to reduce the number of bands from hyperspectral image without loosing relevant information. Another reason is that technological design of a multispectral imager (about a dozen bands) should reasonably be lighter and cheaper than a hyperspectral imager, especially if the detector is a silicon one. Furthermore it will lead to an effective gain in time for image processing. But in order to address the complexity due

to

the

various

fabrics,

we

need

to

start

with

hyperspectral data, and then to select the best bands. These bands will be defmed by their central wavelength and by their width. This will result in a preliminary definition of the demonstrator developed within the SYLLABES project. The subject of this paper is to describe the first results obtained and the progress of the project:

selection of

representative samples, setting up of a bench dedicated to

fig. I : samples of fabric (black fleece, printed synthetic, denim, printed cotton, cordura®, coloured cotton)

hyperspectral acquisition, achieving the first images, and preparing the band selection processing, with supervised and unsupervised detection and classification. 2. MATERIALS AND EXPERIMENTATION 2.1. Samples

The design of such imaging system requires at first the realization of a large number of samples from different fabrics and different geometries shooting. This is indeed to address the wide variety of cases encountered by INPS, especially where visualization is problematic. Samples are made of square pieces of cloth of about 30 cm x 30 cm size. Each one is placed over a 35 cm x 35 cm cardboard pad and stapled to it. The cloth is tight enough to suppress most of the wrinkles in the fabric. The gunshots are done at two different distances (15 cm and 50 cm) from the samples and perpendicular to it. As in the first stage of preparation we need very obvious GSR patterns to test our experimental device, shots were made on white cotton. We then selected usual fabrics encountered during inquiries:

cordura®, denim, printed

cotton, synthetic (fig. 1). For the first tests, several firearms and ammunitions were used to show their different effects. For the first detection tests, only one type of pistol is used. It is a semi-automatic CZ75, and ammunition is Partizan 9 mm parabellum. 2.2 Hyperspectral means

We use two hyperspectral cameras HySpexTM VNIR1600 and SWIR320-me from NEO (Norsk Elektrooptikk) [5]. These very versatile cameras are often used in airborne applications but can be equipped by short range lens objectives to become laboratory devices working at one

978-1-4799-3406-S/12/$31.00©2012IEEE

fig. 2 : Acquisition in lab with HySpex camera: sample is placed on a moving table. meter with a field of view of about 20 cm wide at this distance. We will focus on the VNIR1600 which has a better

resolution

than

the

SWIR320-me

(pixel

size

;:::; 0.15xO.30 mm compared to ;:::; 0.75xO.75 mm) and give significantly better spatial observations. Their spectral domains are respectively [0,4 /-lm - 1 /-lm] and [1 /-lm - 2,5 /-lm]. Samples are placed on a plate that moves synchronously with frame period thanks to the translation stage (fig. 2). A light source of about 100 W illuminates the sample to get a smaller integration time and a better signal on noise ratio (SNR).

2012 4th Workshop on Hyperspectral Image and Signal Processing (WHISPERS)

[7] on the fIrst better components of Minimwn Noise Fraction (MNF). We can thus check the result or frud new ROIs to make supervised classifIcation. After this stage we use all the relevant spectra from GSR and pure fabric to make a spectral library. This spectral library will be used to make a band selection (BS). For this second step of the analysis, we plan to use a genetic algorithm (GA) as search algorithm associated with a criterion function based on spectral angle mapper (SAM) [8] as we already obtained good results with such metric for band selection [9][10]. Using a GA method provides a band selection where bandwidth could be adapted. The last step would be to compare the result of classifIcation using the BS and considering as "Ground Truth" the classifIcation achieved with the whole spectra. If this final step confIrms the efficiency of the BS we could start the design of our prototype else we would refIne the BS. 4. PRELIMINARY RESULTS

fig. 3: Photo of white cotton sample shot at 15 cm. 2.3 Acquisition procedure

4.1 Visual tests

The VNIR spectral domain seems sufficient to extract interesting items. Some tests have shown that SWIR bands

In order to compensate for spatial and temporal variations of

(beyond I micron) do not actually provide any signifIcant

the iIIwnination we acquire images of a spectralon™ plate

improvement. This result has reinforced our priority to

about every 10 minutes from the beginning to the end of the

make a prototype in the VNIR. Most relevant information

experiment. Each image of samples is then divided by the

appears to be between 660 nm and 1000

closest image of spectralon to get a pseudo-reflectance

cm, we can observe visually the impacts of smoke powder,

nm.

For shots at 15

but not always the bullet wipe. Hyperspectral imaging can

image.

reveal systematically this wiping area, the smoked pattern 3. ANALYSIS METHODS

and partially burnt particles, in all cases. For shots at 50 cm, patterns are not obviously observable, and hyperspectral

The study of GSR may be used to estimate fIring distance,

imaging can reveal the wiping area and impacts of powder

to identify bullet holes and to determine whether or not a

in almost all of the cases, and the smoky pattern for half of

person has discharged a fIrearm. To do so, forensic experts

the samples. The sample made of cordura is the most

have identifIed three important elements to visualize on the

difficult case. On the contrary, we have identifIed a set of

samples. Figure 3 is a photograph of a GSR pattern

tissues for which the method works very well: the printed

produced on a white fabric. We can distinguish the three

fabrics, jeans, multicolored fabrics. All these colors seem to

main effects of the gunshot: The zone called "bullet wipe"

have a reflectance that is collapsing around 700 nm. Above

around the hole created by the bullet, the particles of

this wavelength we thus can more easily observe the GSR.

gunpowder in addition to smoke that are propelled out of

The above observations were made by viewing the images

the barrel, and the tattooing that is made of partially or

band

totally burnt particles.

thorough process allows us to further increase the contrast.

by

band,

without

any

advanced

processing.

A

As we do not have a "Ground Truth" linked with the samples we choose to detect the gunshot residues (GSR) by

4.2 After processing

different mean and combine the result to improve the detection. When GSR are visible we extract regions of

As explained in part 3 we use unsupervised and supervised

interest (ROIs) related to the different items we want to

classification. The unsupervised classification step helps us

observe. Then we make a SVM supervised classifIcation [6]

to choose learning pixels for supervised classifIcation. On

(support vector machine) thanks to these learning pixels. In

the fIgure 4

parallel, we make a ISODA TA unsupervised classifIcation

component of MNF with ISODATA algorithm (ENVI).

978-1-4799-3406-S/12/$31.00©2012IEEE

we

present

a result achieved

on

seven

2012 4th Workshop on Hyperspectral Image and Signal Processing (WHISPERS)

Acknowledgement: Authors thanks ANR for the foundation of the SYLLABES project (Systeme hyperspectral Laser pour L'Analyse Balistique et l'Expertise criminelle de Scene). 6. REFERENCES [1] W.F. Rowe, "Fireanns/Residues" Encyclopedia of forensic sciences, Academic Press,pp. 953,2000. [2]

T. Plattner, B. Kneubuehl, M. Thali, and U.

Zollinger,

"Gunshot Residue Patterns on Skin Angled Contact and Near Contact Gunshot Wounds", Forensic Sci. Int., vol. 138, pp. 68-74, 2003. [3] Ch. S. Atwater,M. E. Durina,J. P. Durina,R.D. Blackledge, "Visualization of Gunshot Residue Patterns on Dark Clothing", Journal of Forensic Sciences,vol. 51,Issue 5,pp. 1091-1095, September 2006.

(c) (d) fig. 4: (a) picture of cordura sample. (b) RGB hyperspectral image, (c) unsupervised classification (isodata), (d) supervised classification (SYM) using previous image to select learning pixels. On fig. 4 we show an intermediate case of sample: as the shot was made at 15 cm all the GSR features are present (wipe, smoke and powder residues). As this fabric is black, some

grey

powder

residues

are

visible

(a)

but

it

[4] J.A. Bailey, R. S. Casanova, K. Bufkin, "A Method for Enhancing Gunshot Residue Patterns on Dark and Multicolored Fabrics Compared with the Modified Griess Test", Journal of Forensic Sciences vol. 51,Issue 4,pp. 812-814,July 2006. [5] I. Baarstad, T. L0ke and P. Kaspersen, "ASI, A new airborne hyperspectral imager." Proc. 4th EARSeL Workshop on Imaging Spectroscopy-New

Quality

in

Environmental

Studies

(B.

Zagajewski and M. Sobczak,Eds.),Warsaw,Poland,2005.

is

impossible to visualize the smoke and wipe even by

[6] B.E. Boser, I. M. Guyon, V.N. Vapnik" "A training algorithm

adjusting the LUT of the image (b).

for optimal margin classifiers." In Haussler, David (editor); 5th

Using seven MNF components, ISODATA algorithm gives

Annual ACM Workshop on COLT,pp. 144-152,1992.

a very good classification of the studied patterns (c). Picking up learning pixels for the 4 classes from the previous

classification

image

we

achieve

a

SVM

classification (d) with good agreement.

[7] J.T. Tou" R.C. Gonzalez, Pattern Recognition Principles, Addison-Wesley Publishing Company, Reading, Massachusetts, 1974.

Printed samples cannot be classified with MNF as every

[8] F.A. Kruse, A.B Lekoff, J.B. Boardman, K.B. Heidebrecht,

color becomes a class. But if we use only the [0,7/lm- 1.0

A.T. Shapiro, P.J. Barloon, A.G.H. Goetz, "The spectral image

/lm] spectral domain, colors do not appear and unsupervised

processing

classification gives similar results.

imaging spectrometer data," Remote Sens. of Environment, vol.

(SIPS) - interactive visualization and analysis of

44,pp. 145-163 ,1993.

5. CONCLUSION

In this paper, we present preliminary results which are quite encouraging for the next step of our project of prototype development. We can now build a spectral library consisting

[9]

N.

Keshava, "Distance

hyperspectral

processing

metrics with

and

band

applications

selection to

identification and spectral libraries", IEEE Trans. Geosc. Remote Sens.,vol. 42,pp. 1552-1565,2004.

of four spectral classes, "fabrics", "bullet wipe", "smoke

[10] P. D6liot,M. Kervalla, "Stochastic band selection method

deposit" and " powder residues". Band selection based on

based on a spectral angle class separability criterion",SPIE

this spectral library will be processed in order to determine

Remote Sensing of Environnement,Toulouse,2010

the best bands for classification purpose. This will lead to the defmition of an active multi-spectral prototype, easy to carry upon crime scene and producing irrefutable clues for forensic activities.

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