Li S.Z., Jain A.K. (eds.) Encyclopedia of Biometrics

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Although several speaker recognition systems for

commercial applications (mostly speaker veriﬁcation)

have been developed over the past 30 years, until

recently the development of a reliable technique for

FASR has been unsuccessful because methodological

aspects concerning automatic recognition of speakers

in criminalistics and the role of the forensic expert have

not been investigated sufﬁciently [8]. The role of a

forensic expert is to testify in court using, if possible,

quantitative measures that estimate the value and

strength of the evidence. The judge and/or the jury

use the testimony as an aid to the deliberations and

decisions [9].

A forensic expert testifying in court is not an advo-

cate, but a witness who presents factual information

and offers a professional opinion based upon that

factual information. In order for it to be effective, it

must be carefully documented, and expressed w ith

precision in neutral and objective way with the adver-

sary system in mind. Technical concepts based on

digital signal processing and pattern recognition must

be articulated in layman terms such that the judge and

the attorneys may understand them. They should also

be developed according to speciﬁc recommendations

that take into account also the forensic, legal, judicial,

and criminal policy perspectives. Therefore, forensic

speaker recognition methods should be developed

based on current state-of-the-art interpretation of

forensic evidence, the concept of identity used in crim-

inalistics, a clear understanding of the inferential pro-

cess of identity, and the respective duties of the actors

involved in the judicial process, jurists, and forensic

experts.

Voice as Evidence

When using FASR, the goal is to identify whether an

unknown voice of a questioned recording (trace)

belongs to a suspected speaker (source). The

▶ voice

evidence consists of the quantiﬁed degree of similarity

between speaker dependent features extracted from the

trace, and speaker dependent features extracte d from

recorded speech of a suspect, represented by his or her

model [1], so the evidence does not consist of

the speech itself. To compute the evidence, the proces-

sing chain illustrated in Fig. 1 may be employed [10].

As a result, the suspect’s voice can be recognized as the

recorded voice of the trace, to the extent that the

evidence supports the hypothesis that the questioned

and the suspect’s recorded voices were generated by

the same person (source) rather than the hypothesis

that they were not. However, the calculated value of

evidence does not allow the forensic expert alone to

make an inference on the identity of the speaker.

As no ultimate set of speaker speciﬁc features is

present or detected in speech, the recognition process

remains in essence a statistical-probabilistic process

based on models of speakers and collected data,

which depend on a large number of design decisions.

Information available from the auditory features and

their evidentiary value depend on the speech organs

and language used [3]. The various speech organs have

to be ﬂexible to carry out their primary functions

such as eating and breathing a s well as their secondary

function of speech, and the number and ﬂexibility of

the speech organs results in a high number of ‘‘degrees

of freedom’’ when producing speech. These ‘‘degrees of

freedom’’ may be manipulated at will or may be subject

to variation due to external factors such as stress,

fatigue, health, and so on. The result of this plasticity

of the vocal organs is that no two utterances from the

same individual are ever identical in a physical sense.

In addition to this, the linguistic mechanism (lan-

guage) driving the vocal mechanism is itself far from

invariant. We are all aware of changing the way we

speak, including the loudness, pitch, emphasis, and

rate of our utterances; aware, probably, too, that style,

pronunciation, and to some extent dialect, vary as w e

speak in different circumstances. Speaker recognition

thus involves a situation where neither the physical

basis of a person’s speech (the vocal organs) nor the

language driving it, are constant.

The speech signal can be represented by a sequence

of short-term feature vectors. This is known as feature

extraction (Fig. 1). It is typical to use features based on

the various speech production and perception models.

Although there are no exclusive features conveying

speaker identity in the speech signal, from the source-

ﬁlter theory of speech production it is known that the

speech spectrum envelope encodes information about

the speaker’s vocal tract shape [11 ]. Thus some form

of spectral envelope based features is used in most

speaker recognition systems even if they are dependent

on external recording conditions. Recently, the major-

ity of speaker recognition systems have converged to

the use of cepstral features derived from the envelope

spectra models [1].

1390

Voice, Forensic Evidence of

Thus, the most persistent real-world challenge in

this ﬁeld is the variability of speech. There is within-

speaker (within-source) variability as well as between-

speakers (between-sources) variabilit y. Consequently,

forensic speaker recognition methods should provide

a statistical-probabilistic evaluation, which attempts

to give the court an indication of the strength of the

evidence, given the esti mated within-source variability

and the between-sources variability [4, 10].

Bayesian Interpretation of Evidence

To address these variabilities, a probabilistic model [9],

Bayesian inference [8] and data-driven approaches [6]

appear to be adequate: in FASR statistical techniques

the distribution of various features extracted from a

suspect’s speech is compared with the distribution of

the same features in a reference population with re-

spect to the questioned recording. The goal is to infer

the identity of a source [9], since it cannot be known

with certainty.

The inference of identity can be seen as a reduction

process, from an initial population to a restricted class,

or, ultimately, to unity [8]. Recently, an investigation

concerning the inference of identity in forensic speaker

recognition has shown the inadequacy of the speaker

veriﬁcation and speaker identiﬁcation (in closed set

and in open set) techniques [8]. Speaker veriﬁcation

and identiﬁcation are the two main automatic techni-

ques of speech recognition used in commercial appli-

cations. When they are used for forensic speaker

recognition they imply a ﬁnal discrimination decision

based on a threshold. Speaker veriﬁcation is the task of

Voice, Forensic Evidence of. Figure 1 Block diagram of the evidence processing and interpretation system. ß IEEE.

Voice, Forensic Evidence of

1391

deciding, given a sample of speech, whether a speciﬁed

speaker is the source of it. Speaker identiﬁcation is the

task of deciding, given a sample of speech, which

among many speakers is the source of it. Therefore,

these techniques are clearly inadequate for forensic

purposes, because they force the forensic expert to

make decisions which are devolved upon the court.

Consequently, the state-of-the-art speaker recognition

algorithms using dynamic time warping (DTW) and

hidden Markov models (HMMs) for text-dependent

tasks, and vector quantization ( VQ), Gaussian mixture

models (GMMs), ergodic HMMs and others for text-

independent tasks have to be adapted to the Bayesian

interpretation framework which represents an ade-

quate solution for the interpretation of the evidence

in the judicial process [9].

The court is faced with decision-making under un-

certainty. In a case involving FASR it wants to know

how likely it is that the speech samples of questioned

recording have come from the suspected speaker.

The answer to this question can be given using

the Bayes’ theorem and a data-driven approach to

interpret the evidence [1, 7, 10].

The odds form of Bayes’ theorem shows how new

data (questioned recording) can be combined with

prior background knowledge (prior odds (province

of the court)) to give posterior odds (province of the

court ) for judici al outcomes or issues (Eq. 1). It allow s

for revision based on new information of a measure of

uncertainty (likelihood ratio of the evidence (province

of the forensic expert)) which is applied to the pair

of competing hypotheses: H

– the suspected speaker

is the source of the questioned recording, H

– the

speaker at the origin of the questioned recording is

not the suspected speaker.

posterior

knowledge

pðH

jEÞ

pðH

jEÞ

posterior

odds

ðprovince of

the courtÞ

new data

pðEjH

likelihood

ratio

ðprovince of

the expertÞ



prior

knowledge

pðH

prior odds

ðprovince of

the court Þ

ð1Þ

This hypothetical-deductive reasoning method, based

on the odds form of the Bayes’ theorem, allows evalu-

ating the likelihood ratio of the evidence that leads

to the statement of the degree of support for one

hypothesis against the other. The ultimate question

relies on the evaluation of the probative strength of

this evidence provided by an automatic speaker recog-

nition method [12]. Recently, it was demonstrated that

outcome of the aural (subjective) and instrumental

(objective) approaches can also be expressed as a

Bayesian likelihood ratio [4, 13].

Strength of Evidence

The ▶ strength of voice evidence is the result of the

interpretation of the evidence, expressed in terms of

the likelihood ratio of two alternative hypotheses. The

principal structure for the calculation and the inter-

pretation of the evidence is presented in Fig. 1.It

includes the collection (or selection) of the databases,

the automatic speaker recognition and the Bayesian

interpretation [10].

The methodological approach based on a Bayesian

interpretation (BI) framework is independent of the

automatic speaker recognition method chosen, but the

practical solution presented in this essay as an example

uses text-independent speaker recognition system based

on Gaussian mixture model (GMM) [14].

The Bayesian interpretation (BI) methodology

needs a two-stage statistical approach [10]. The ﬁrst

stage consists in modeling multivariate feature data

using GMMs. The second stage transforms the data

to a univariate projection based on modeling the simi-

larity scores. The exclusively multivariate approach is

also possible but it is more difﬁcult to articulate

in layman terms [15]. The GMM method is not only

used to calculate the evidence by comparing the

questioned recording (trace) to the GMM of the sus-

pected speaker (source), but it is also used to produce

data necessary to model the within-source variability

of the suspected speaker and the between-sources

variability of the potential population of relevant

speakers, given the questioned recording. The interpre-

tation of the evidence consists of calculating the likeli-

hood ratio using the probability density functions

(pdfs) of the variabilities and the numerical value of

evidence.

The information provided by the analysis of the

questioned recording (trace) leads to specify the initial

reference population of relevant speakers (potential pop-

ulation) having voices similar to the trace, and,

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Voice, Forensic Evidence of

combined with the police investigation, to focus on and

select a suspected speaker. The methodology presented

needs three databases for the calculation and the inter-

pretation of the evidence: the potential population data-

base (P), the suspected speaker reference database (R),

and the suspected speaker control database (C) [14].

The potential population database (P) is a database

for modeling the variability of the speech of all the

potential relevant sources, using the automatic speaker

recognition method. It allows evaluating the between-

sources variability given the questioned recording,

which means the distribution of the simila rity scores

that can be obtained, when the questioned recording is

compared to the speaker models (GMMs) of the po-

tential population database. The calculated between-

sources variability pdf is then used to estimate the

denominator of the likelihood ratio p(E|H

). Ideally,

the technical characteristics of the recordings (e.g.,

signal acquisition and transmission) should be chosen

according to the characteristics analyzed in the trace.

The suspected speaker reference database (R) is

recorded with the suspected speaker to model hi s/her

speech with the automatic speaker recognition method.

In this case, speech utterances should be produced in

the same way as those of the P database. The sus-

pected speaker model obtained is used to calculate the

value of the evidence, by comparing the questioned

recording to the model.

The suspected speaker control database (C) is

recorded with the suspected speaker to evaluate her/his

within-source variability, when the utterances of this

database are compared to the suspected speaker model

(GMM). This calculated within-source variability pdf

is then used to estimate the numerator of the likeli-

hood ratio p(E|H

). The recording of the C database

should be constituted of utterances as far as possible

equivalent to the trace, according to the technical

characteristics, as well as to the quantity and style of

speech.

The basic method proposed has been exhaustively

tested in mock forensic cases corresponding to real

caseworks [11, 14]. In an example presented in Fig. 2,

the strength of evidence, expressed in terms of likeli-

hood ratio gives LR = 9.165 for the evidence value

E = 9.94, in this case. This means that it is 9.165

times more likely to observe the score E given the

hypothesis H

than H

. The important point to be

made here is that the estimate of the LR is only as

good as the modeling techniques and databases used

to derive it. In the example, the GMM technique was

used to estimate pdfs from the data representing simi-

larity scores [11].

Voice, Forensic Evidence of. Figure 2 The LR estimation given the value of the evidence E. ß IEEE.

Voice, Forensic Evidence of

1393

Evaluation of the Strength of

Evidence

The likelihood ratio (LR) summarizes the statement of

the forensic expert in the casework. However, the great-

est interest to the jurists is the extent to which the LRs

correctly discriminate ‘‘the same speaker and different-

speaker’’ pairs under operating conditions similar to

those of the case in hand. As was made clear in the US

Supreme Court decision in Daubert case (Daubert v.

Merrell Dow Pharmaceuticals, 1993) it should be cri-

terial for the admissibility of scientiﬁc evidence to know

to what extent the method can be, and has been, tested.

The principle for evaluation of the strength of

evidence consists in the estimation and the comparison

of the likelihood ratios that can be obtained from the

evidence E, on one hand when the hypothesis H

true (the suspected speaker truly is the source of the

questioned recording) and, on the other hand, when

the hypothesis H

is true (the suspected speaker is truly

not the source of the question ed recording) [14]. The

performance of an automatic speaker recognition

method is evaluated by repeating the experiment de-

scribed in the previous sections, with several speakers

being at the origin of the questioned recording, and by

representing the results using experimental (histogram

based) probability distribution plots such as probabili-

ty density functions and cumulative distribution func-

tions in the form of Tippett plots (Fig. 3a)[10, 14].

The way of representation of the results in the form

of Tippett plots is the one proposed by Evett and

Buckleton in the ﬁeld of interpretation of the forensic

DNA analysis [6]. The authors have named this repre-

sentation ‘‘Tippett plot,’’ referring to the concepts of

‘‘within-source comparison’’ and ‘‘between-sources

comparison’’ deﬁned by Tippett et al.

Forensic Speaker Recognition in

Mismatched Conditions

Nowadays, state-of-the -art automatic speaker recogni-

tion systems show very good performance in discrimi-

nating between voices of speakers under controlled

recording conditions. However, the conditions in

which recordings are made in investigative activities

(e.g., anonymous calls and wire-tapping) cannot be

controlled and pose a challenge to automatic speaker

recognition. Differences in the background noise, in

the phone handset, in the transmission channel, and in

the recording devices can introduce variability over

and above that of the voices in the recordings. The

main unresolved problem in FASR today is that of

handling mismatch in recording conditions, also in-

cluding mismatch in languages, linguistic content, and

non-contemporary speech samples. Mismatch in re-

cording conditions has to be considered in the estima-

tion of the likelihood ratio [11–13]. Next step can

be combination of the strength of evidence using

aural-perceptive and acoustic-phonetic approaches

(aural-instrumental) of trained phoneticians with

that of the likelihood ratio returned by the automatic

Voice, Forensic Evidence of. Figure 3 (a) Estimated probability density functions of likelihood ratios; (b) Tippett plots

corresponding to (a). ß IEEE.

1394

Voice, Forensic Evidence of

system [4]. In order for FASR to be acceptable for

presentation in the courts, the methods and techniques

have to be researched, tested and evaluated for error, as

well as be generally accepted in the scientiﬁc commu-

nity. The methods proposed should be analyzed in the

light of the admissibility of scientiﬁc evidence (e.g.,

Daubert ruling, USA, 1993) [11].

Summary

The essay discussed some important aspects of fore-

nsic speaker recognition, focusing on the necessary sta-

tistical-probabilistic framework for both quantifying

and interpreting recorded voice as scientiﬁc evidence.

Methodological guidelines for the calculation of the

evidence, its strength and the evaluation of this strength

under operating conditions of the casework were pre-

sented. As an example, an automatic method using the

Gaussian mixture models (GMMs) and the Bayesian

interpretation (BI) framework were implemented for

the forensic speaker recognition task. The BI method

represents neither speaker veriﬁcation nor speaker iden-

tiﬁcation. These two recognition techniques cannot be

used for the task, since categorical, absolute and deter-

ministic conclusions about the identity of source of

evidential traces are logically untenable because of the

inductive nature of the process of the inference of iden-

tity. This method, using a likelihood ratio to indicate the

strength of the evidence of the questioned recording,

measures how this recording of voice scores for the

suspected speaker model, compared to relevant non-

suspect speaker models. It became obvious that partic-

ular effort is needed in the trans-disciplinary domain of

adaptation of the state-of-the-art speech recognition

techniques to real-world environmental conditions for

forensic speaker recognition. The future methods to be

developed should combine the advantages of automatic

signal processing and pattern recognition objectivity

with the methodological transparency solicited in

forensic investigations.

Related Entries

▶ Forensic Biometrics

▶ Forensic Evidence

▶ Speaker Recognition, An Overview

References

1. Rose, P.: Forensic Speaker Identiﬁcation. Taylor & Francis,

London (2002)

2. Dessimoz, D., Champod, C.: Linkages between biometrics

and forensic science. In: Jain, A., Flynn, P., Ross, A. (eds.)

Handbook of Biometrics, pp. 425–459. Springer, New York

(2008)

3. Nolan, F.: Speaker identiﬁcation evidence: its forms, limitations,

and roles. In: Proceedings of the Conference ‘‘Law and Language:

Prospect and Retrospect’’, Levi (Finnish Lapland), pp. 1–19

(2001)

4. Rose, P.: Technical forensic speaker recognition: Evaluation,

types and testing of evidence. Comput. Speech Lang. 20(2–3),

159–191 (2006)

5. Meuwly, D.: Voice analysis. In: Siegel, J., Knupfer, G., Saukko,

P. (eds.) Encyclopedia of Forensic Sciences, pp. 1413–1421.

Academic Press, London (2000)

6. Drygajlo, A.: Forensic automatic speaker recognition. IEEE

Signal Process. Mag. 24(2), 132–135 (2007)

7. Robertson, B., Vignaux, G.: Interpreting Evidence. Evaluating

Forensic Science in the Courtroom. John Wiley & Sons, Chiche-

ster (1995)

8. Champod, C., Meuwly, D.: The inference of identity in forensic

speaker identiﬁcation.’’ Speech Commun. 31(2–3), 193–203

(2000)

9. Aitken, C., Taroni, F.: Statistics and the Evaluation of

Evidence for Forensic Scientists. John Wiley & Sons, Chichester

(2004)

10. Drygajlo, A., Meuwly, D., Alexander, A.: Statistical

methods and Bayesian interpretation of evidence in forensic

automatic speaker recognition. In: Proceedings of Eighth

European Conference on Speech Communication and

Technology (Eurospeech’03), pp. 689–692 Geneva, Switzerland,

(2003)

11. Alexander, A.: Forensic automatic speaker recognition using

Bayesian interpretation and statistical compensation for mis-

matched conditions. Ph.D. thesis, EPFL (2005)

12. Gonzalez-Rodriguez, J., Drygajlo, A., Ramos-Castro, D., Garcia-

Gomar, M., Ortega-Garcia, J.: Robust estimation, interpretation

and assessment of likelihood ratios in forensic speaker recogni-

tion. Comput. Speech Lang. 20(2–3), 331–355 (2006)

13. Alexander, A., Dessimoz, D., Botti, F., Drygajlo, A.: Aural and

automatic forensic speaker recognition in mismatched condi-

tions. Int. J. Speech Lang. Law, 12(2), 214–234 (2005)

14. Meuwly, D., Drygajlo, A.: Forensic speaker recognition based

on a Bayesian framework and Gaussian mixture model-

ling (GMM). In: Proceedings 2001: A Speaker Odyssey, The

Speaker Recognition Workshop, pp. 145–150 Crete, Greece,

(2001)

15. Alexander, A., Drygajlo, A.: Scoring and direct methods for the

interpretation of evidence in forensic speaker recognition.

In: Proceedings of Eighth International Conference on Spoken

Language Processing (ICSLP’04), pp. 2397–2400 Jeju, Korea,

(2004)

Voice, Forensic Evidence of

1395

Voiced Sounds

The voiced speech is generated by the modulation of

the airstream of the lungs by periodic opening and

closing of the vocal folds in the glottis or larynx. This

is used, e.g., for vowels and nasal consonants.

▶ Speech Production

Voiceprint

Voiceprint is another name for spectrogram. This

name is usually avoided because of its association

with voiceprint recognition, which is a highly contro-

versial method of forensic speaker recognition, which

exclusively uses visual examination of spectrograms.

▶ Voice, Forensic Evidence of

Volunteer Crew

The volunteer crew for a biometric test is the indivi-

duals that participa te in the evaluation of the biometric

and from whom biometric samples are taken.

▶ Test Sample and Size

1396

Voiced Sounds

Walk-through

▶ Iris on the Move™

Walk-up

▶ Iris on the Move™

Watermarking, Biometric

A speciﬁc type of digital watermarking that includes

biometric information either in the watermark, the

host data, or both. The Biometric watermarking sys-

tems have the additional requirement of not degrading

the performance of the biometric system(s) which they

protect. This characteristic, sometimes referred to as

performability, can entail any effect on matc hing per-

formance, image quality, or computational efﬁciency.

Systems watermarking biometric host data with bio-

metric feature vectors add the potential for multimodal

authentication.

▶ Iris Digital Watermarking

Watermarking, Digital

Digital watermarking is a method of embedding infor-

mation within digital media for the purpose of proving

ﬁle authenticity, tracking chain of custody and data

reproduction, or describing host content. In digital

watermarking, the watermark and the host data are

ty pically related, and both are utilized by an intended

recipient. Although not necessary, digital watermarks

are t ypically imperceptible to humans either visually or

audibly unli ke their predecessors, paper watermarks.

As a result, digital watermarking systems rely on

machines to carry out the processes of watermark

detection and extraction.

▶ Iris Digital Watermarking

Wavefront Coded

Iris Biometric

Systems

V. PAU

L PAUCA

,KELLY SMITH FADDIS

,ARU N ROSS

OSEPH VAN DER GRACHT

,TODD C. TORGERSEN

Wake Forest University, Winston-Salem, NC, USA

West Virginia University, Morgantown, WV, USA

Holospex Inc., Columbia, MD, USA

Synonyms

Computational iris recognition systems; Pupil phas e

engineered iris biometrics

Definition

Wavefront coded iris biometrics is an imaging method

whereby a suitably designed phase mask, placed in

the pupil of an imaging system, is used to encode the

depth dimension of an extended three-dimensional

scene by means of an approximately shift-invariant

point spread function. Iris images so acquired are

thus deliberately distorted by a known amount in a

way that is insens itive to misfocus blur, w ithin a range

2009 Springer Science+Business Media, LLC

greater than the system’s depth of ﬁeld. For sufﬁcient

SNR, these images can be digitally deblurred by stan-

dard image deconvolution algorithms to recover depth

dependent detail, enabling accurate iris recognition

and identiﬁcation. It is shown that even the intermedi-

ate, distorted iris images maintain sufﬁcient low- and

mid-frequency informati on to enable increased iris

recognition performance, without resorting to digital

restoration.

Introduction

The iris is a popular biometric that is gaining increased

attention as a means of identiﬁcation and veriﬁcation

of individuals for controlling access to secured areas,

materials, or systems. The popularity of the iris as a

biometric stems from its availability for remote and

noninvasive assessment and the uniqueness of its tex-

ture from one subject to another, making possible a

fully automated recognition and veriﬁcation system

based upon machine vision [1]. This texture is well

known to provide a signature that is unique to each

subject. In fact, the operating probability of false iden-

tiﬁcation by the Daugman algorithm [2] can be of the

order of 1 in 10

. Compared with other biometric

signatures, the iris is generally considered to be more

stable and reliable for identiﬁcation.

However, a major limitation of conventional iris

recognition systems [3] is the inability to obtain in-

focus iris images over an extended distance range. In

order to achieve reasonable lighting levels and expo-

sure times, the optical system must have a high numer-

ical aperture and a corresponding low F-number, to

provide sufﬁciently high signal-to-noise ratio (SNR) at

the detector, with minimum motion blur. Unfortunate-

ly, a high numerical aperture results in a corresponding

small depth-of-ﬁeld and small

▶ iris recognition operat-

ing range. Often times, end-users are forced to ‘‘play

the trombone’’ in order to present their iris within the

system’s imaging volume. Figure 1 illustrates a typical

iris recognition scenario, w here a user must submit a

sample of his iris to the acquisition system, to gain

access to a secured environment. The shaded box indi-

cates the imaging volume that produces an in-focus

acceptable image for processing.

Wavefront coding is a novel technolog y that

was recently proposed for facilitating interaction of

end-users with an iris biometric system [4, 5 ]. In

particular, wavefront coding enables the acquisition

of iris data through a large ﬁeld of view and large

depth of ﬁeld using relatively fast optical systems.

They do so without compromising optical resolution

and light gathering capacity; a performance that can-

not be achieved using traditional optical designs [4].

Wavefront coding was originally proposed by Dowski

and Cathey [6], ﬁtting within the concept of modern

computational or task-based imaging. Since its origi-

nal proposal, wavefront coding has been extended to

include more general separable and non-separable type

surfaces or phase masks [7, 8]. A more recent develop-

ment includes a nov el general framework, known as

▶ pupil phase engineering (PPE), to address high qual-

ity image acquisition from a numerical optimization

perspective [9 , 10]. In this framework, image quality

requirements may include extending the depth of ﬁeld,

controlling or minimizing the impact of aberrations,

motion blur, scattering from the imaging medium,

among others (see, e.g., [10] and references therein).

Wavefront Coding for Extended Focus

and Aberration Correction

Wavefront coding is a novel imaging modality where

a unique aperture conﬁguration is used to increase

the depth of ﬁeld, without signiﬁcantly decreasing

the SNR and lig ht gathering capacity. In this modality,

a phase mask is placed in the pupil of an imaging

Wavefront Coded

Iris Biometric Systems. Figure 1 Iris

recognition imaging volume.

1398

Wavefront Coded

Iris Biometric Systems

system to deliberately distort the image, but in a way

that is relatively insensitive to misfocus. Figure 2 illus-

trates the difference between conventional limited-

focus and wavefront encoded systems. The distorted

image carries depth-dependent intensity information

which can be digitally recovered using standard decon-

volution methods. The most general polynomial

form for the added pupil phase mask f(x,y) is the

following:

fðx; yÞ¼

m¼1

n¼1

; ð1Þ

The original proposal [5] argued for a cubic phase

mask f(x,y) ¼ a(x

þy

), based on the requirement

that the encoding pupil phase f(x,y) be monomia l in

the pupil coordinates (x,y), be separable in those coor-

dinates, and lead to an MTF that is asy mptotically

focus-independent. Several other separable and non-

separable, symmetric, and odd phase masks of the

form (1) have been proposed and numerically opti-

mized [11, 12] to achieve desirable characteristics in

the point spread function (PSF). These may include

insensitivity to defocus as well as the control of optical

system aberrations such as spherical aberration, astig-

matism, and curvature (see [10] for details regarding

the numerical optimization of such phase masks using

information theoretic based metrics).

The use of phase masks in iris r ec ognition imaging

systems is a promising approach that could greatly

extend the operational range of iris recognition [4, 5],

thereby facilitating ﬂexibility when an end-user interacts

with the system. The performance of wavefront coded

iris biometric systems has been recen tly evaluated using

small iris datasets (less than 10 user irises). A more

comprehensiv e study using simulated unrestored wave-

front coded images evaluated 150 iris images pertaining

to 50 subjects has been recently conducted [13].

Simulation of Iris Biometric Systems

Fourier optics provides a convenient ﬁrst order ap-

proximation of image formation and computer si mu-

lation systems often take advantage of this theory for

efﬁcient design and study of optical systems. The Sim-

ulator of Iris Recognition Imaging Systems (SIRIS) is

one such a tool that was designed to generate conven-

tional blurred and wavefront coded imagery, for a wide

variety of polynomial form phase masks. SIRIS

employs Fourier optics based image formation, PSF,

and noise models to leverage the exploration of sepa-

rable and non-separable phase masks on iris recogni-

tion performance [14].

SIRIS uses an implementation of Daugman’s algo-

rithm and, among other functionalities, allows the user

to specify optical characteristics and parameters of an

imaging device. These parameters include focal length,

object distance, pupil diameter, pixel pitch, and noise.

Using the speciﬁed optical and detector parameters

and input imagery, SIRIS generates corresponding out-

put imagery that includes the effects of a pre-speciﬁed

phase mask and noise characteristics. It also provides

the waves of defocus blur corresponding to any speciﬁc

distance from the plane of best focus. The result is a

model that summarizes the effects of defocus blur and

the phase mask on the imaging system.

Simulation and Study of Unrestored

Wavefront Coded Iris Imagery

SIRIS was recently employed to simulate, study, and

compare the performance of conventional and wave-

front coded iris biometric systems. A set of 150

in-focus iris images from the Iris Challenge Evaluation

(ICE) datase t [15] were used in this study, pertaining

to 50 different hand-selected users. The selected 150

Wavefront Coded

Iris Biometric Systems. Figure 2 Standard (left) and cubic phase (right) imaging systems.

Wavefront Coded

Iris Biometric Systems

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