forensic discrimination of latent fingerprints using laser...

Article

Forensic Discrimination of LatentFingerprints Using Laser-InducedBreakdown Spectroscopy (LIBS) andChemometric Approaches

Jun-Ho Yang and Jack J Yoh

Abstract

A novel technique is reported for separating overlapping latent fingerprints using chemometric approaches that combine

laser-induced breakdown spectroscopy (LIBS) and multivariate analysis. The LIBS technique provides the capability of real

time analysis and high frequency scanning as well as the data regarding the chemical composition of overlapping latent

fingerprints. These spectra offer valuable information for the classification and reconstruction of overlapping latent fin-

gerprints by implementing appropriate statistical multivariate analysis. The current study employs principal component

analysis and partial least square methods for the classification of latent fingerprints from the LIBS spectra. This technique

was successfully demonstrated through a classification study of four distinct latent fingerprints using classification methods

such as soft independent modeling of class analogy (SIMCA) and partial least squares discriminant analysis (PLS-DA). The

novel method yielded an accuracy of more than 85% and was proven to be sufficiently robust. Furthermore, through

laser scanning analysis at a spatial interval of 125 mm, the overlapping fingerprints were reconstructed as separate two-

dimensional forms.

Keywords

Laser-induced breakdown spectroscopy (LIBS), latent fingerprint, overlapping fingerprint, multivariate analysis

Date received: 3 November 2017; accepted: 20 February 2018

Introduction

Each human fingerprint consists of unique and distinctive

patterns of ridges and valleys. These patterns do not change

over the lifetime of an individual and the probability of fin-

gerprints from two different individuals being identical is

known to be only about one in a million.1 Additional infor-

mation such as eating habits, behavioral habits, and physical

growth can be obtained from the chemical composition of

the oil present on the latent fingerprints.2–6 As a result, an

individual’s fingerprints are widely recognized as a means of

personal authentication, and detection of latent fingerprints

in an actual crime scene can play a very important role.

However, in a real crime scene, obtaining a complete

fingerprint is often a challenging task. The fingerprints

may be present in a damaged or overlapped form and add-

itional efforts are necessary, as two or more overlapping

fingerprints are difficult to distinguish clearly. Various

studies on separating overlapping fingerprints have been

conducted. The majority of mainstream research focuses

on identifying the direction of the fingerprint as a vector

component after extracting images of overlapping finger-

prints, and to proposing an algorithm that separates lines

that do not have vector continuity.7–12 One such method

proposed by Nakamura et al. has been developed to ana-

lyze the laser spectrum of overlapping fingerprints using a

continuous wave green laser.10 Meanwhile, the method

developed by Bradshaw et al. utilizes matrix-assisted laser

desorption ionization mass spectrometry imaging with

multivariate statistical analysis for detecting the endogen-

ous and exogenous species on fingerprints.11 In the study

reported by Tang et al.,12 the molecular signal of the latent

fingerprint was enhanced using gold nanoparticles, and the

two latent fingerprints were classified using mass spectrom-

etry imaging.

Department of Aerospace Engineering, Seoul National University, Republic

of Korea

Corresponding author:

Jack J Yoh, Department of Aerospace Engineering, Seoul National

University, 1 Gwanakro, Gwanakgu, Seoul 151-742, Republic of Korea.

Email: [email protected]

Applied Spectroscopy

0(0) 1–10

! The Author(s) 2018

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Laser-induced breakdown spectroscopy (LIBS) is a

method of analyzing atomic components of the sample by

creating plasma using a high-power laser beam. When a

high-power laser is irradiated on to a targeted region,

laser ablation occurs, plasma is generated, and a high tem-

perature is generated at this time. In the plasma, the sample

is atomized and ionized after vaporization. The atoms and

ions being investigated attain an excited state due to the

energy absorbed from the plasma and return to the ground

state as they emit energy in electromagnetic form. This

energy is emitted with an intrinsic wavelength depending

on the kind of the element and the excited state. As a

result, LIBS can detect an atomic element whenever

plasma generation is allowed. Additionally, all elements in

the sample can be measured at the same time as the specific

signal of each element is analyzed independently in any state

of solid, liquid, or gas. The LIBS method offers several

advantages over other laser spectroscopic techniques

such as simplicity of setup in combination with other

optics, ability to perform real time, remote analysis, and

it does not need chemical pretreatment of the sample.

Consequently, LIBS has proven quite useful in various

fields that include flame diagnostics, space exploration,

and forensic science, in particular.13–16

The study by Taschuk et al. detected the sodium content

of the ridge portion of the latent fingerprint using 120 fs

laser pulse after engraving the latent fingerprint on a Si

wafer.17 This study was the first to detect the shape of

the latent fingerprint using LIBS and confirmed that the

shape of the fingerprint can be derived for an area of

5 mm width and 1 mm height as a two-dimensional (2D)

image. In the study by Godwal et al.,18 a 5 lJ ultraviolet

laser pulse was used and a portable LIBS device was fabri-

cated to produce realistic latent fingerprint detection sys-

tems. This study demonstrated that with a dual-pulse

technique the sensitivity of micro-LIBS can be increased

by magnitude of ablation pulse power. Other fingerprint

research using LIBS did not detect latent fingerprint but

detected explosives on the fingerprint and restored them

on the fingerprint as a 2D image. In the studies by

Abdelhamid et al.,19,20 optical catapulting was utilized in

combination with LIBS to analyze solid aerosol, explosive,

and confusant materials such as trinitrotoluene (TNT), dini-

trotoluene (DNT), and sodium maleonitriledithiolate

(MNT) were classified. Despite being widely utilized for

fingerprint detection, LIBS has so far been used only for

detection of a single fingerprint.17–20

Recently, the multivariate chemometrics method has

been combined with LIBS data and used in many diverse

fields. The multivariate chemometrics has been used to

identify soil constituents and classify the soil according to

composition. Various analytical and multivariate methods

such as partial least squares regression (PLSR), soft inde-

pendent modeling of class analogy (SIMCA), partial least

square discriminant analysis (PLS-DA), linear discrimination

analysis, and principal component regression been used to

analyze the soil elemental components.21–24 The multivari-

ate analysis combined with LIBS was also utilized in arche-

ology to classify coins with archeological significance

through principal component analysis (PCA) and conse-

quently to estimate the age and location of use for the

coin.25,26 Oztoprak et al. transformed medieval ceramics

into LIBS laser spectral data to classify archeological cer-

amic samples, glaze, paint, and clay via PCA.27 Dingari et al.

conducted a study on the classification and estimation of

chemical components of unknown substances through

multivariate analysis for previously specified chemical com-

ponents,28 even when LIBS spectroscopy was used for

unknown substances. In the study by Vance et al.,29 it was

also possible to classify protein components into 2D images

using the nonlinear classification method known as support

vector machine (SVM) together with LIBS data. Neiva et al.

used a linear classification method such as SIMCA and

PLS-DA analysis as an index to judge and classify the quality

of leather of sheep and cattle.30 In the study by Zhang

et al.,31 qualitative and quantitative analysis, and classifica-

tion of, the chemical composition of 20 slags was per-

formed by multivariate analysis such as SVM and PLS

combined with LIBS. In addition, there have been studies

to confirm the high degree of accuracy of classification of

explosives by analyzing the suspended matter containing

explosives and classifying them according to the situation.32

Highly reliable analysis as well as classification of pigments

and inks has been conducted using Raman spectroscopy,

which can detect molecular components differently from

LIBS, combined with the LIBS analysis method.33

The current study is focused on a methodology for sep-

aration of two overlapping latent fingerprints using a novel

technique which combines LIBS with multivariate analysis.

This research assumes the chemical composition of the

latent fingerprints to be different and extracts the latent

fingerprints from the four different individuals. The study

also includes a linear and nonlinear multivariate analysis

using PCA, SIMCA, and PLS-DA. In order to validate the

LIBS results, the classification was confirmed by conducting

a sensitivity test as well as a robustness test. The new tech-

nique was found to be suitable for identifying two different

overlapping fingerprints in a real crime scene using LIBS

combined with multivariate analysis and for classifying

them into two distinct fingerprint images.

Material and Methods

Preparation of Latent Fingerprint Sample

All of the latent fingerprints were extracted from four

Korean and Indian males in their 20 s and 30 s, and were

prepared spontaneously. This experiment is based on the

assumption that there are differences in the chemical com-

ponents existing in the latent fingerprint, and that the

2 Applied Spectroscopy 0(0)

classification and detection based on the difference in

chemical components can be performed through multivari-

ate analysis and utilized as a 2D chemical distribution.

Therefore, the same procedure was used to collect four

different latent fingerprints. Because the fingers and fore-

head oil can also be easily affected by external factors such

as humidity and contaminations, the task of cleaning using

the ethanol with the tissues was prioritized to index finger

and forehead. After 5 min, the index finger was rubbed for

10 s from the forehead of the fingerprint extractor, and

then pressed on the aluminum plate with a slight force

for 10 s to leave the fingerprint on the aluminum plate. A

fingerprint sample was prepared in the same order as in

Figure 1a. The strong fluorescent light is illuminated in the

case of Figure 1 to visualize the clear fingerprint. In the case

of overlapping fingerprint samples, the other two finger-

prints were individually extracted, and after washing with

ethanol like in the previous single fingerprint extraction,

two fingerprints were successively placed on the aluminum.

When fingerprinting two people, samples were prepared so

that overlapping parts existed. In preparation of the alumi-

num plate, vinyl was attached to prevent contact with the

outside before leaving the fingerprint, and the vinyl was

removed before the fingerprint was imprinted.

Experimental Setup

A Q switched neodymium doped yttrium aluminum garnet

(Nd:YAG) laser with a 1064 nm wavelength was used as a

LIBS experimental device (RT250-Ec, Applied Spectra Inc.)

for inducing laser ablation and generating plasma. The laser

has a pulse duration of 5 ns. In this study, we aim at mini-

mizing the level of destruction of the fingerprint in all

experiments through laser ablation, enhancing the reso-

lution at the time of image processing. Therefore, laser

pulse energy was maintained at approximately 10 mJ.

When the laser is irradiated, the gate delay is adjustable

from 0.1 to 0.5 ls, and the gate width is set to 1.05 ms. In

the case of the spectrometer, a six-channel charge-coupled

device spectrometer (Applied Spectra Inc.) is used to

obtain a high resolution (0.1 nm from ultraviolet to visible

range, 0.12 nm for visible to near-infrared range), and a

laser spectrum can be detected by dividing the wavelength

in the range 198–1050 nm into about 13 000 wavelength

channels. A 100 mm uncoated quartz lens was used as a

focal lens to allow laser light to generate plasma through

focusing laser light.

For minimizing laser crater, beam expander was used to

decrease beam diameter. First of all, entrance aperture of

12.7 mm focuses the wide beam from Nd:YAG laser. After

that, using 8.6 mm diameter lens, laser beam was expanded

to straight laser beam. To obtain regular and reasonable

LIBS data, the distance between the fingerprint sample

and the laser was kept constant at 15 cm through

the stage which can move in the xyz axis inside the

LIBS device. The experimental setup is the same as in

Figure 1b. Laser spot size and laser irradiance are 125 lm

and 6.366� 1013 W m�2 s�1.

Multivariate Chemometrics Methods

The principal component analysis (PCA) technique can

transfer high-dimensional data such as laser spectra to

low-dimensional data. When the data is arranged on PCA

plane, the axis having the greatest dispersion is set as the

first principal component (PC) to analyze and divide each

data, and the allocated PCs are sequentially set according to

the magnitude of dispersion about laser spectrum data.

Through PCA analysis, grouping based on the area of PCs

score plot becomes the basis of additional SIMCA and PLS-

DA methods.

Soft independent modeling of class analogy is used to

identify local models for possible groups through PCA ana-

lysis and to predict possible class memberships for new

Figure 1. (a) Sequence of preparation of latent fingerprint. (b) LIBS experimental setup.

Yang and Yoh 3

observations. Firstly, the global PCA is performed accord-

ing to the available data structure for the entire data set to

identify the observation group. Then, regional models are

evaluated for each model and classified as one of the exist-

ing class models. The number of major components

retained for each class is generally freely configurable, but

the signal or information contained in the model for the

class may be distorted if it has too few components, and if

there are too many PCs, since the signal is reduced, it is

important to determine the number of PCs that must be

retained for each class.

Partial least squares discriminant analysis can derive the

maximum separation between classes where each latent

fingerprint laser spectrum data is divided using PLS compo-

nents to sharpen the separation between observation

groups. Partial least squares discriminant analysis consists

of a PLSR analysis where the response variable is a categor-

ical variable representing the hierarchical membership of

the statistical unit. Therefore, PLS-DA does not allow any

other response variable that is a response variable to define

an individual group, and consequently all measurement vari-

ables play the same role in relation to class assignment. In

practice, the PLS component is constructed by attempting

to find the appropriate trade-off between the two object-

ives of explaining the set of explanatory variables and pre-

dicting the response variable. Classification based on PLS

has an advantage in constructing prediction capability, and

this method can be evaluated as a more advanced method

than the classical SIMCA classification analysis method,

which works more for unit reallocation of predefined

classes.

In the current study, sensitivity (cross-validation) and

robustness (validation) tests were performed for the

SIMCA and PLS-DA methods.28 The sensitivity test was

conducted by randomly splitting 100 sample laser spectra

into two groups, viz. training group of 30 spectra and test

data of 70 spectra. After that, the 12 test laser spectra are

comprised of three randomly chosen ones for each latent

fingerprint. Robustness test was conducted in a similar

sequence as the sensitivity test with a key difference of

removal of one latent fingerprint class from the calibration

set and repetition of the sequence for each latent

fingerprint class. In order to improve the accuracy of the

analysis, 100 independent iterations are performed by re-

splitting the whole data into training and test data for both

the sensitivity and robustness tests. Current study assigns

the term ‘‘correct classification’’ for the instances when the

data of each latent fingerprint is correctly assigned, ‘‘mis-

classification’’ for the cases with incorrect assignment from

the original latent fingerprint, and ‘‘unclassification’’ for the

cases where no class is assigned.

The Unscrambler X 10.1 and Matlab R2016a software

were used for data analysis and statistical multivariate ana-

lysis. In addition, Origin Pro 8.5.1 (Origin Lab Corporation)

was used as the graphing software.

Results and Discussion

Laser Spectrum Analysis and PCA of LatentFingerprints

The LIBS spectrum was extracted from four different per-

sons. To improve reliability of the laser spectrum, in each

human’s laser spectrum, the mean value of 100 laser spec-

tral data was used. In Figure 2, the y-axis value is offset by

35 000. It is confirmed that the signal of the latent finger-

print has a relative high and low signal intensity at several

specific wavelengths.

The wavelengths with relatively high signal intensities in

the oil fingerprint laser spectrum are approximately

238 nm, 239 nm, 274 nm, 308 nm, 309 nm, 393 nm,

394 nm, 396 nm, 588 nm, 589 nm, 747 nm, 766 nm, and

777 nm. As a result, the signals for Fe, Al, O, K, Na, and

Ca were confirmed, as shown in Figure 2. The composition

of this signal was analyzed and checked using the NIST

atomic spectra database.34

There are differences in body chemical composition due

to various factors such as eating habits, environment, and

inherited family history, and this is also checked from the oil

presented on human skin surface. Therefore, when the

chemical component of latent fingerprint is measured

using LIBS, there is a difference in the laser spectrum ten-

dency depending on the difference of the chemical compo-

nents. However, these results are only a result of visual

confirmation through naked eyes, and PCA is required to

separate or classify the latent fingerprints as reliable data.

The PCA analyzes the overall LIBS spectral region, extracts

a specific portion having a main emission line having a rela-

tively high SNR (signal to noise ratio) value capable of ana-

lyzing the PCs, and extracts a specific value based on the

extracted PC spectrum LIBS spectral results are inter-

preted. In this research, PCA was performed on 100

laser spectra data for each latent fingerprint. Figure 3

shows the results obtained from the PCA and PLS about

four different latent fingerprints. It can be seen that the

location of each latent fingerprints shows the independent

position on the 2D plane. Also, as shown in Figure 3, it can

be seen that about 90% of the total spectral range is also

explained when using only PC 1 and PC 2 (PC 1: 53.80%,

PC 2: 35.90%). In case of PLS analysis, 95% of the total

spectral range is explained when using only factor 1 and 2

(factor 1: 86%, factor: 7%).

As shown in Figure 3(b), the emission line wavenumber

classifications in PCA are Mg, Fe, Na, Ca, and K. PCs 1–7

show almost the same wavelength; however, the intensities

at some wavelengths are reversed by each PC, respectively.

It can be seen that this makes a difference.

Multivariate Analysis

For the first SIMCA analysis, 100 laser spectra were used

for each latent fingerprint. In the case of the sensitivity test,


Figure 2. LIBS spectra lines of four types of latent fingerprints.

Figure 3. (a) Separation via the principal component analysis, (b) the results of extracted PCs, (c) separation via the partial least square

(PLS) method, and (d) the PLS loading plot.

Yang and Yoh 5

randomly selected 30 fingerprints from the 100 fingerprint

laser spectra obtained at one time were set as a test

sample, and the remaining 70 fingerprint data were classi-

fied through PCA. The results are shown in Table 1, when

SIMCA analysis is performed on 30 test samples per

fingerprint. In this case, it was confirmed that the class

was correctly assigned except for the four data. For the

robustness test, one latent fingerprint class is deleted from

calibration set and repeated for each latent fingerprint class.

For the data analyzed by PCA, data such as PCA analysis

data were used for the sensitivity test. Figure 4 shows the

model distance between PCA models. This value means

how different two models are from each other. A larger

model to model distance express that two model can be

discriminated more clearly.

In order to obtain statistical reliability, not only SIMCA

analysis results were obtained for the whole data, but also

laser spectra were extracted randomly for every data of

each of the latent fingerprints and repeated 100 iterations.

This research conducted sensitivity and robustness test

about SIMCA. The difference between sensitivity and

robustness is whether the test sample comes from the

first 100 fingerprint data or the additional extracted spec-

tral data. The results are shown in Table 1. In the case of

the sensitivity test, it was confirmed that the average

classification accuracy was 90.36%, 6.18% were inadequate,

Table 1. The classification probability of SIMCA.

Type

Correct

classification Misclassification Unclassification

(a) Sensitivity test

Latent fingerprint 1 0.9010 0.0320 0.0670




(b) Robustness test





Figure 4. Model distance using SIMCA.


and 3.46% were not classified. In the robustness test, it

was found that the average of the test was 84.10%,

13.01%, and 2.89%, respectively. However, since robustness

experiments have a probability of 82% or more, it is pos-

sible to classify them using SIMCA in the case of latent

fingerprints of different origins, and it can be separated

based on the chemical composition of overlapping latent

fingerprints.

For the PLS-DA analysis, the same laser spectra were

used for the four latent fingerprints subjected to the

SIMCA analysis, after that, PLS regression was performed

for each type of fingerprint to classify each latent finger-

print. Although the PLS-DA analysis is basically stable and

robust compared with the SIMCA analysis, the actual

Table 2. The classification probability of PLS-DA.

Type

Correct

classification Misclassification Unclassification

Sensitivity test





Robustness test





Figure 5. (a) A process for the separation of overlapping fingerprints. (b, c) Schematic figures showing separation of overlapping

fingerprints.

Yang and Yoh 7

results (Table 2) are not significantly different from the

SIMCA analysis. For the sensitivity test, the correct classi-

fication was performed with an average probability of

93.33%, misclassification was 5.9%, and unclassification

was 3.2%. This leads to correct classification with relatively

high probability compared to SIMCA analysis, while mis-

and unclassification also have a small probability of drop.

In the robustness experiment, correct, mis-, and unclassifi-

cation were obtained with 85.78%, 9.13%, and 5.07% prob-

ability, respectively. The probability of correctly assigned

PLS-DA method was higher than that of SIMCA method,

and misclassification was also less probable. However,

unclassification has been confirmed to have a higher prob-

ability of PLS-DA scheme.

Separation of Overlapping Latent Fingerprints

It is confirmed that the classification is conducted through

multivariate analysis methods such as PCA, SIMCA, and

PLS-DA when the sources of the latent fingerprints are

different. In this section, we present result of separating

overlapping fingerprints in terms of 2D chemical distribu-

tions by comparing the laser spectrum tendencies, and then

the multivariate analysis method is applied to reconstruct

each fingerprint’s line. The fingerprint classification process

is shown in Figure 5. First of all, we extract 30 laser spectra

of each latent fingerprint. After that, the predicting class is

created through PCA analysis. Thereafter, the laser is irra-

diated at regular intervals, and the laser spectra of each

Figure 6. Separation of overlapping latent fingerprints using classification methods.


point is classified and analyzed to finally separate the over-

lapping fingerprints like Figure 6. As a result, it was con-

firmed that even in the case of actual overlapping

fingerprints, multivariate analysis can be used to classify

and separate each latent fingerprints.

Conclusion

The chemometric approach of combining LIBS with multi-

variate analysis for separating overlapping latent fingerprints

has been validated. The LIBS data was obtained using a

Q-switched Nd:YAG 1064 nm wavelength laser with the

laser energy, pulse duration, and the beam width optimized

for highly reliable spectra which were collected in the ultra-

violet to visible range. The PCA and PLS methods were

utilized to extract the independent regions corresponding

to each of the two overlapping fingerprints. Furthermore,

sensitivity testing of the new methodology established

probability of accurate detection of the fingerprint to be

higher than 90%. Similarly, the robustness of the method

against perturbations was recognized as the method yielded

an accuracy of approximately 85%. Finally, the separations

of overlapping fingerprints were performed using LIBS scan-

ning technique and multivariate analysis. Based on the ease

of application and accuracy of the results, the novel tech-

nique was confirmed to be useful in separating overlapping

fingerprints in real life scenarios.

Conflict of Interest

None declared.

Funding

This work was supported by the Korea National Research

Foundation (grant numbers NRF-2014M1A3A3A02034903 and

NRF-2016R1D1A1A02937421) through IAAT at Seoul National

University.

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