Iris Pattern Matching using Score Normalisation Techniques 

ICE: Iris Evaluation Challenge, 23 March 2006 [24 Mar 06, revised slides: 6, 16, 17, 24, 26; 27 Mar 06, revised slide 24] 

Iris Pattern Matching u

Score Normalisation Tech

Nigel Sedgwick 

Cambridge Algorithmica Limited Tel: +44 (0)149 Oakdene URL: http://w

Beaconsfield Fax: +44 (0)14Buckinghamshire Email: ncs@cUnited Kingdom HP9 2BZ 
Overview of Presenta

1. Score Normalisation and Multi-Biometric 2. Biometric Gain against Impostors (BGI) [3. Likelihood Ratios and Simple Bayesian F4. HD Normalisation for IrisCode Matching 5. Result A: Can HD Normalisation be Impro6. Fusion for Multi-Algorithmic IrisCodes 7. Result B: Does BGI Normalisation/Fusion 8. Iris Recognition and ICE Looking Forward 

References Summary 

Iris Pattern Matching using Score Normalisation Techniques ICE: Iris Evaluation Challenge, 23 March 2006 
Multi-Biometric Fusio

Multi-biometric is a term covering: • multi-modal: using 2 or more different biome

modalities; eg: iris combined with fingerprintcombined with hand geometry 

• multi-instance: combining more than one se

instance of the same biometric modality; eg from 2 or more different fingers; irises of bot

• multi-algorithmic: processing the same biom

with 2 or more feature analysis and/or patteralgorithms, and combining the results 

• multi-presentational: (somewhat different in 

capturing the same biometric instance (eg a fingerprint) more than once, to reduce image errors, and then combining, or selecting the 

Iris Pattern Matching using Score Normalisation Techniques ICE: Iris Evaluation Challenge, 23 March 2006 
Score Normalisatio

• Raw scores can be on arbitrary, device-depe

algorithm-dependent scales. 

• It is meaningless to combine scores from diff

arbitrary scales. 

• Score normalisation applies an appropriate t

to scores from each modality, instance or algthat all normalised scores are on the same s

• Probability ordered scales: high scores matc• Distance ordered scales: low scores match • Scores closely related to linear likelihood rati

best combined by multiplication. 

• Scores closely related to log likelihood ratios 

best combined by addition. 

Iris Pattern Matching using Score Normalisation Techniques ICE: Iris Evaluation Challenge, 23 March 2006 
Score Normalisation a

Multi-Biometric Fusio

• This talk is principally about multi-algorithmic fus

improved matching of a single biometric sample. 

• Aspects apply to multi-instance fusion, eg fusing 

left and right irises. 

• Improved technical performance arises from both

better trade-off between False Match Rate (FMR) Non-Match Rate (FNMR). 

• Multi-modal, multi-instance, and perhaps multi-al

fusion provide greater resistance to biometric avotechniques (eg gummy fingerprint overlays). 

• Multi-modal fusion, eg iris and fingerprint, also p

greater universality (works for more people), as dinstance fusion to some degree. 

• Score normalisation is essential to multi-algorith

multi-modal fusion; often it benefits multi-instanc

Iris Pattern Matching using Score Normalisation Techniques ICE: Iris Evaluation Challenge, 23 March 2006 
BGI: Biometric Gain against Im

BGI is rather like hi-fi amplifier gain: just consider output to the input, for each biometric modality, instan

Probability of being an impostor, given the biometr

BGI = -------------------------------------------------------------------------

Probability of being an impostor, given only prio

Most of the time, a very good approximation to the BGreciprocal of the Likelihood Ratio Genuine to Impostor used in many good pattern-matching algorithms in exidevices. 

1.0 Probability of seeing the evidence from 

BGI ~ -------- = ------------------------------------------------------------

LRGI Probability of seeing it from the expected 

Iris Pattern Matching using Score Normalisation Techniques ICE: Iris Evaluation Challenge, 23 March 2006 (rev 24 Mar 06) 
Relationship between BGI an

Bayes Rule: P(I|E).P(E) = P(E|I).P(I) I = Subject is Probs sum to 1.0: P(I) + P(G) = 1.0 G = Subject is 

P(I|E) + P(G|E) = 1.0 E = Evidence 

Weighted sum: P(E) = P(E|G).P(G) + P(E|I).P(I) We desire the a posteriori probability: P(I|E) 

P(E|I).P(I) 

P(I|E) = ------------------------

-

P(E|G).P(G) + P(E|I).P(I) 

P(I|E) 1.0 

BGI(E) = ------ = ----------------------------- = -----

P(I) P(I) + P(G).[P(E|G)/P(E|I)] P(I) 

But often, we do not know the a priori probabilities: P(I), P(G

So, assuming a priori probability of Impostor is very small: BGI

Iris Pattern Matching using Score Normalisation Techniques ICE: Iris Evaluation Challenge, 23 March 2006 
Bayesian Fusion is Exact using 

The definition of the Likelihood Ratio Genuine to Impostor is: 

P(E|G) 

LRGI(E) = -----

-

P(E|I) 

If the total biometric evidence comes from a multi-biometric findividual scores e1, e2, e3, etc), this can be represented (ex

P(E|G) P(e1,e2,e3,...|G) 

LRGI(E) = ------ = ----------------

-

P(E|I) P(e1,e2,e3,...|I) 

Note that, very importantly, the LRGI is independent of the a p

probabilities: P(I) and P(G). 

Thus multi-biometric fusion can be done, using LRGIs, withoknowledge of the a priori probabilities. In so far as a priori probabilities are important (eg if P(I) is nthese can be applied (as on the previous slide) after multi-bio

Iris Pattern Matching using Score Normalisation Techniques ICE: Iris Evaluation Challenge, 23 March 2006 
Simple Bayesian Fusion is usually 

Approximation (often a Very Goo

• The basic assumption of simple Bayesian fusion is th

biometric measurements (e1, e2, e3, ...) are statistical

P(e1,e2,e3,...|G) P(e1|G) P(e2|G) 

LRGI(E) = ----------------- ~ ------- * ------- * 

P(e1,e2,e3,...|I) P(e1|I) P(e2|I) 

• This is often not the case; it most certainly is not the c

algorithmic fusion. 

• However, experiments show [3] that pattern recognitio

based on simple Bayesian fusion are very often highly with more complicated and sophisticated approaches

• Furthermore, theory shows [4] that simple Bayesian fu

over wide ranges of the LRGI values to be combined, 

• So, for these reasons, we view simple Bayesian fusion 

approach that should be evaluated for every applicatiomulti-biometric fusion. 

Iris Pattern Matching using Score Normalisation Techniques ICE: Iris Evaluation Challenge, 23 March 2006 
Notes on Modified IrisCod

and Multi-Algorithmic Defin

• Work reported here is only concerned with patte

of IrisCodes. No work is done on image process

• Here, IrisCodes differ from Daugman’s original 2

quantised phasors [5]. 

• Each IrisCode is divided into 2 subsets: T1 and T• Each subset is a different algorithmic analysis of 

iris image. 

• Initial pattern matching is done separately on ea

T2 subsets, using the usual Hamming Distance a

• Initial pattern matching is also done treating the 

as a single entity. This is usually referred to as “IrisCode” or “all”. 

• The underlying direction of this work has been to 

improved ways (multi-algorithmic fusion) of comanalysis of the T1 and T2 subsets. 

Iris Pattern Matching using Score Normalisation Techniques ICE: Iris Evaluation Challenge, 23 March 2006 
Initial ROC Curve Compariso

T1 and T2 Subsets, and Whole I

Note unexpected ranking 

of T2 Subset, 

as better than w

Iris Pattern Matching using Score Normalisation Techniques ICE: Iris Evaluation Challenge, 23 March 2006 
Hamming Distance Norma

for IrisCode Matching 

• Daugman’s normalisation [6] is well-established a

stages. 

• This is to deal with variation, between iris image p

number of bits actually compared to form the HamDistance. 

• Stage 1: the raw Hamming Distance (HDraw) is give

number of bits differing between the 2 IrisCodes dnumber of bits compared (n, as determined from tgallery mask bits). 

• Stage 2: this modifies HD non-linearly, leading 

raw 

• By mistake, but fortuitously, our initial implement

IrisCode pattern matching used only the Stage 1 N

Iris Pattern Matching using Score Normalisation Techniques ICE: Iris Evaluation Challenge, 23 March 2006 
ROC Curve Comparison for 1-Sta

and 2-Stage (N2) HD Normalis

Result A: Unexpectedly, over most of each pair of curveor All), 1-stage normalisation is better than 2-stage norma

Iris Pattern Matching using Score Normalisation Techniques ICE: Iris Evaluation Challenge, 23 March 2006 
Investigation into Why 1-S

Normalisation is Bette

Note: 2-stage normalisation is better for FMR; 1-stage normalisatioRate. Effect on Miss Rate mostly dominates, so 1-stage normalisat

Iris Pattern Matching using Score Normalisation Techniques ICE: Iris Evaluation Challenge, 23 March 2006 
Note on Datasets Us

• In this work, the BGI multi-algorithmic fusion use

pattern matching results to characterise the PDFs scores (HDraw) for genuine matches and for impos

• It is appropriate to separate the evaluation datase

characterisation dataset. 

• Accordingly, the IrisCodes from all right-iris imag

divided into 2 Sets. 

Subjects IrisCodes Genuine 

Matches 

Set A (B, C combined) 132 1,426 12,221 Set B (characterisation) 66 796 7,024 

Set C (evaluation) 66 630 5,197 

Sum: Set B + Set C 132 1,426 12,221 

Iris Pattern Matching using Score Normalisation Techniques ICE: Iris Evaluation Challenge, 23 March 2006 
BGI Fusion: The Starting 

ROC curves for T1 and T2 Subsets, and for All, each wnormalisation (and correction of the number of microNote re-labelling the “all” case as “Standard HD Fusio

Iris Pattern Matching using Score Normalisation Techniques ICE: Iris Evaluation Challenge, 23 March 2006 (rev 24 Mar 06) 
BGI Fusion: PDF Modelling usin

Pre-Existing Approach (from BSS

Selected pre-existing PDF models (pdfp02, [2]) fail t

T2 Subset only. Performance very close to Standar

Iris Pattern Matching using Score Normalisation Techniques ICE: Iris Evaluation Challenge, 23 March 2006 (rev 24 Mar 06) 
BGI Fusion: New IrisCode-Specific PDF 

These PDF models (pdfp05) are shown here normalising Subsets. These normalisations are not monotonic. Thercurves before/after normalisation do not exactly overlay e

Iris Pattern Matching using Score Normalisation Techniques ICE: Iris Evaluation Challenge, 23 March 2006 
BGI Fusion: ROC Curve for Fu

after IrisCode-Specific Normalis

These PDF models (pdfp05) also do not give better perforT2 Subset, just as with pdfp02 models and with Standard [Note: not shown, pdfp05 models may do slightly better th

Iris Pattern Matching using Score Normalisation Techniques ICE: Iris Evaluation Challenge, 23 March 2006 
ICE: Iris Evaluation Challenge, 23 March 2006 

Iris Pattern Matching using Score Normalisation Techniques 

BGI Fusion: Starting Point ROC 

for Evaluation Dataset (Set 

These curves show different performance from the Charac

Dataset (Set B). Overall performance is worse. The T2 Sub

better than Standard HD Fusion. The T1 subset is better, o

range. 
BGI Fusion: T1 and T2 Subset RO

on Evaluation Dataset (Set 

On the Evaluation Dataset, unlike for Set B, the pdfp05 normSubsets show consistently better performance over some o

Iris Pattern Matching using Score Normalisation Techniques ICE: Iris Evaluation Challenge, 23 March 2006 
BGI Fusion: ROC Curves on Eva

Dataset (Set C) 

The Iris-Specific normalisation (pdfp05) clearly shows bettover part of the range. Performance equals or exceeds, ovrange, the better of the contributing multi-algorithmic featu

Iris Pattern Matching using Score Normalisation Techniques ICE: Iris Evaluation Challenge, 23 March 2006 
BGI Fusion: Final ROC Curv

Result B: BGI Iris-Specific Normalisation clearly shows be

performance over the whole ROC curve, on the Evaluation 

Caveats: the datasets are not large; Set B gives conflicting region of improvement is of most interest for multi-instance fusion. 

Iris Pattern Matching using Score Normalisation Techniques ICE: Iris Evaluation Challenge, 23 March 2006 
References 

[1] Nigel Sedgwick, The Need for Standardisation of Multi-Model B

Combination, Cambridge Algorithmica Ltd, 6 November 2003, http://www.camalg.co.uk/s03017_pr0/s03017_pr0.pdf 

[2] Nigel Sedgwick, Preliminary Report on Development and Evalu

Biometric Fusion using the NIST BSSR1 517-Subject Dataset, 

Algorithmica Ltd, 28 May 2005, http://www.camalg.co.uk/s05011_

[3] Pedro Domingos and Michael Pazzini, Beyond Independence: C

Optimality of the Simple Bayesian Classifier, Proc 13th Int Con

Learning (ICML), 1996. 

[4] Pedro Domingos and Michael Pazzini, On the Optimality of the 

Classifier under Zero-One Loss, Machine Learning 1-30, 1997 

[5] John Daugman, How Iris Recognition Works, IEEE Trans on Cir

for Video Technology, CVST 14(1), January 2004, http://www.cl.cam.ac.uk/users/jgd1000/csvt.pdf 

[6] John Daugman, Probing the Uniqueness and Randomness of 

from 200 Billion Iris Pair Comparisons, Cambridge University CLaboratory. 

Iris Pattern Matching using Score Normalisation Techniques ICE: Iris Evaluation Challenge, 23 March 2006 (rev 24 & 27 Mar 06) 
ICE 2006: Looking For

Thoughts on things that impact, potentially, on perfo• Left/Right Iris Multi-Instance Fusion: provide iri

captured together or at the same time. 

• Possible Multi-Modal Use: more emphasis on w

performance, rather than at a single operating pFAR). 

• Metadata on Sample Capture Equipment: samp

details on manufacturer, model and version allonormalisations according to probe/gallery devic

• Demography/Ethnicity of Gallery Subjects: aga

potential benefit for normalisation, using such is available. 

Iris Pattern Matching using Score Normalisation Techniques ICE: Iris Evaluation Challenge, 23 March 2006 
Iris Pattern Matching using Score Normalisation Techniques ICE: Iris Evaluation Challenge, 23 March 2006 (rev 24 Mar 06) 

Summary 

Theory of Normalisation and Fusion. The BGI approach is normalisation of scores (from each contributing modality, ialgorithm) to be likelihood ratios. Such fusion is independprobabilities. Simple Bayesian Fusion is a good approach, and experimental support. Result A: Iris recognition ROC performance with the modifimproved by using the 1-stage normalisation, rather than thnormalisation. [This should be investigated for the standaroriginally defined by Daugman.] Result B: With the modified IrisCodes, using multi-algoritheach whole iris image, an improvement in ROC performancusing the BGI approach to normalisation and fusion. This wStandard Hamming Distance Fusion approach, of just treatbits together and deriving a single Hamming distance. Pursue BGI Approach. It has definite potential to be usefulwork to date are: small datasets used for characterisation aknown poor images in characterisation dataset; conflicting two datasets.