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Published in IET Biometrics

Received on 5th May 2012

Revised on 11th October 2012

doi: 10.1049/iet-bmt.2012.0019

ISSN 2047-4938

Critical analysis of adaptive biometric systemsN. Poh1 A. Rattani2 F. Roli2

1Department of Computing, FEPS, University of Surrey, Guildford, UK2Department of Electrical and Electronic Engineering, University of Cagliari Piazza dArmi, Cagliari, Italy

E-mail: [email protected]

Abstract: Biometric-based person recognition poses a challenging problem because of large variability in biometric samplequality encountered during testing and a restricted number of enrollment samples for training. Solutions in the form of

adaptive biometrics have been introduced to address this issue. These adaptive biometric systems aim to adapt enrolledtemplates to variations in samples observed during operations. However, despite numerous advantages, few commercialvendors have adopted auto-update procedures in their products. This is due in part to the limited understanding andlimitations associated with existing adaptation schemes. In view of that the topic of adaptive biometrics has not beensystematically investigated, this study works towards filling this gap by surveying the topic from a growing body of the recentliterature and by providing a coherent view (critical analysis) of the limitations of the existing systems. In addition, theauthors have also identified novel research directions and proposed a novel framework. The overall aim is to advance thestate-of-the-art and improve the quality of discourse in this field.

1 Introduction

Although the biometric technology continues to improve, anintrinsic characteristic of this technology is that a systemserror rate, for example, the false accept rate (FAR), falsereject rate (FRR) and equal error rate (EER) (the rate atwhich FAR is equal to FRR), cannot attain the absolutezero. A major cause of these errors is the compound effectof the scarcity of training samples during the enrollment

phase as well as the presence of substantial samplevariations because of human-sensor interaction and theacquisition environment during operations [1]. Apart fromthis, being biological tissues in nature, biometric traits can

be altered either temporarily or permanently, because ofageing [2], diseases or treatment to diseases. An importantconsequence of these factors is that a biometric reference[A template refers to the biometric sample used forenrollment. The term model refers to a statisticalrepresentation derived from one or more biometric samples.In order for our discussion to cover both types of method,we shall adapt the standard vocabulary that is biometricreference or simply reference. A reference is subsequentlyused for comparing a biometric test/query sample to obtaina similarity score.] (obtained during enrollment) cannot beexpected to fully represent a persons identity.

Solutions in the form of adaptive biometrics have beenintroduced to address this issue of referencerepresentativeness [3, 4]. These adaptive biometric systemsattempt to update reference galleries by integrating

information captured in input operational samples. The two-fold aim is to continuously adapt the biometric system tothe intra-class variation of the input data as a result of (1)changing acquisition conditions that may have adverse

impact on the system, for example, pose and illuminationchanges for face biometrics, and (2) age and life-style-

related changes that can cause permanent changes to thebiometric trait.

Most of the existing automated adaptive biometric systemshave adopted semi-supervised learning [4, 5] for the purposeof adaptation. Semi-supervised learning is a machine learningscheme based on the joint use of labelled and unlabelledsamples. In other words, input samples are assigned identitylabels using enrolled references and the positively classifiedsamples are used to adapt the references. A commonlyadopted adaptation procedure is to augment the referenceset with the newly classified input samples. The efficacy ofthe system can be gauged by comparing the obtained

performance gain with a traditional biometric system whichdoes not have any adaptation mechanism. The expected

performance gain is dependent on the effective labelling(classification) of the input samples. This is becausemisclassification errors will introduce impostor samples intothe updated reference set, the result of which can becounterproductive. An adaptive biometric system may alsooperate in supervised mode in which biometric samples aremanually labelled [3]. The supervised method represents the

best-case performance as all the available positive (genuine)samples are used for adaptation. However, manualintervention may be time consuming and costly. Thereforeit is generally infeasible to manually update referencesregularly.

In contrast, an adaptive biometric system has numerous

advantages. First, with this system, one no longer needs tocollect a large number of biometric samples duringenrollment. Second, it is no longer necessary to re-enrol orre-train the system (classifier) from scratch in order to cope

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up with the changing environment [3]. This convenience cansignificantly reduce the cost of maintaining a biometricsystem. Third, the actual observed variations can beincorporated into the references. Despite these advantages,to our knowledge, few biometric vendors such as BIOsingle(fingerprint) and Recogsys (hand geometry) haveincorporated automated adaptation mechanism into theirtechnologies at the time of this writing. This is due in part

to the limited understanding and limitations associated withexisting adaptive biometric systems.

The goal of this manuscript is to advance the state-of-the-art in adaptive biometrics by improving the understandingand drawing on the limitations of the existing adaptive

biometric systems. To this aim, critical analysis of theexisting literature is conducted. Based on the findings of thecritical analysis, we propose a novel framework that aims tomitigate some of the limitations and investigate possiblefuture research avenues.

Specific contributions of this manuscript are as follows:

1. a taxonomy of adaptive biometric systems through anumber of key attributes;2. use of a meta-analysis technique to objectively comparethe effectiveness of key attributes across various systemsreported in the literature; and3. identification of novel research directions based on thefindings of the above meta-analysis.

A preliminary version of this manuscript appeared in [3] inthe form of critical survey. The current manuscriptsubstantially differs from [3] in the following ways. First,novel attributes that distinguish an adaptive system fromone another are introduced. Second, meta-analysis isutilised to aid analysis of various state-of-the-art adaptivesystems. Last but not least, a novel framework, as well as

research directions, is proposed.The paper is organised as follows: Section 2 formulates the

key attributes and conducts the meta-analysis. Section 3provides the novel framework and research directions.Conclusions are drawn in Section 4.

2 Attributes and critical analysis

2.1 Attributes of the existing adaptive biometricsystems

In an attempt to categorise adaptive biometric systems, themost logical way to proceed is to define a number of keyattributes. On surveying the current state-of-the-art, we findthat the following attributes are relevant to distinguish oneadaptive biometric system from another:

1. Supervised against semi-supervised: The foremostattribute in classifying adaptive systems is indisputably onthe basis of whether the data labelling process is supervised[3, 4, 6, 7] or unsupervised [4, 8, 9]. Although insupervised adaptation, samples are manually labelled, in theunsupervised case, they are inferred by the system. Thelatter approach is generally referred to as semi-supervisedlearning because the enrollment biometric reference(template) is effectively labelled but the potentialoperational biometric samples that are used for adaptation

are unlabelled. As mentioned before, supervised adaptationrepresents the best-case scenario, that is, resulting in the

best possible performance because all available genuinesamples are used for the process of adaptation. Therefore it

is generally useful to report both strategies when comparingdifferent adaptive methods.2. Self- against co-train: For an automated adaptive systems

based on semi-supervised learning, self- [810] and co-training [4, 11] are the commonly adopted schemes foradaptation. In self-training, the system updates itself byadding only highly confidently classified input samples asadditional data for training. A sample is said to be highly

confidently classified if its matching score on comparisonwith the enrolled templates is above a stringent operatingthreshold. The reason to adopt highly confidently classifiedsamples for adaptation is to avoid impostor intrusion intothe updated template set.

On the other hand, a co-training-based scheme utilises themutual and complementary help of the two biometrics toupdate the references. Intuitively, one system is expected toassign correct labels to biometric query samples that aredifficult for another system. Consider an example of faceand fingerprint co-training system. Although on its own, theface sub-system may have difficulty in labelling a querysample in difficult conditions, the fingerprint sub-systemmay classify the associated fingerprint sample with veryhigh confidence. In this case, the face system can benefitfrom the high confidence of the fingerprint by incorporatingthe additional face samples for training. Therefore twosystems operating at high threshold can still help each otherto identify difficult samples exhibiting large intra-classvariations.3. Verification against identification: These adaptive

biometric system can also be differentiated on the basis ofthe systems basic mode of operation that is, verification(input sample is compared with the references of theclaimed identity) or identification (input samples arematched to the references of all the users in the databaseand then the correct identity is determined among the top

most retrievals) [1]. Accordingly, the performance gain willbe measured using EER or rank-one performance metrics,respectively.4. Level of adaptation: In addition to the adaptation at thereference level, the process of adaptation can also take

place at score or decision level where the matching score ordecision functions are adapted to the variations of the input

biometric samples. For instance, Poh et al. [6] usesbiometric sample quality to adapt the matching score so asto render the final accept/reject decision independent of theinput sample quality.5. Online against offline: Online adaptive systems [8, 9]adapt themselves as soon as the input data is available afterthe recognition process. On the other hand, offline methods[4, 12, 13] adapt themselves after a batch of input sampleshave been accumulated over a period of time. Another finedistinction between the two is that while an online methodfollows the chronological ordering of the availability of thesamples during adaptation, the offline one may not adhereto such an ordering.6. Quality against non-quality based: Recent advancement inthe biometric community shows that biometric sample quality[http://www.itl.nist.gov/iad/894.03/quality/workshop/] hasconsiderable impact on the system performance for varioustraits like fingerprint, iris, face etc, as well as for fusion[14]. Quality measures quantify the degree of excellence orconformance of biometric samples to some predefined

criteria known to influence the system performance.However, it is only recently that biometric sample quality

has been considered for adaptive biometric systems [6, 15].Quality based adaptation requires maintaining a different set

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of updated references for each type of condition. Since aquery sample is always acquired under a particularcondition, the inference (matching task) requiresidentification of its quality condition and matching with theset of references of the same quality type [15]. In this

paper, the resultant system is termed condition-adaptivesystem.7. Impostor against non-impostor attack: Adaptive biometric

systems deployed in a real operational environment arevulnerable to impostor attack where an unauthorised userattempts to gain access to the system. However, earlystudies did not assume impostor attack during the systemsoperation [7, 8, 10, 12]. This is evident by the fact that thedata used for adaptation (called the adaptation set)contained only genuine samples for adaptation. Later on,this limitation was identified and impostor samples wereintroduced in the adaptation set to simulate update processin a real operational environment [4, 13, 1618].

In the next section, we will quantify existing adaptivebiometric systems based on the mentioned attributes usingmeta-analysis.

2.2 Meta-analysis: a tool for critical analysis

Meta-analysis is a quantitative method for analysing resultsfrom multiple papers on the same subject [19]. It has the

property [19] of synthesising (summarising) results frommultiple independent experiments in a quantitative manner.Therefore we adopted meta-analysis as a tool to performcritical analysis and to compare existing adaptive biometricsystems based on the identified key attributes (in Section 2.1).

To this end, we divided the existing adaptive systems basedon the key attributes that is, supervised against semi-supervised, self- against co-train, online against offline and

quality against non-quality based. Furthermore, wedistinguished these systems on the basis of inclusion orexclusion of impostor attacks during the adaptation processthat is, impostor against non-impostor attack. The effect ofa systems different mode of operation (i.e. verification oridentification) is mitigated by standardising the performancemetrics (explained in detail in the following section).

Then we use meta-analysis to validate the followinghypotheses:

1. Is supervised adaptation better than semi-supervised?2. Can co-training outperform self-training?3. Is offline adaptation better than its online counter-part?4. Can quality-based adaptation outperform its non-quality-

based counterpart?5. Is there any performance bias if one excludes non-match(impostor) samples in the adaptation set (i.e. no impostorattack)?

As it turns out, there are already 23% of existing papersaddressing the second hypothesis above. Nevertheless, itwould still be interesting to infer the expected difference inthe performance gain of co-training over self-training basedon the existing studies.

However, if we would like to infer whether or not the use ofbiometric sample quality can outperform a non-quality-basedadaptive system (hypothesis 4), none of the selected papers

directly tested this hypothesis. Only meta-analysis can offerthe possibility of utilising the diverse experiments

performed by independent researchers to test hypothesis 4without recourse to direct experimentation.

In summary, irrespective of the different protocols and datasets, meta-analysis offers a means to objectively quantify andcompare different adaptive biometrics in our survey, as thefirst recourse. The resultant set of hypotheses may then besubject to direct testing, that is, explicit comparison of twoadaptive systems on a common data set, if clear inferencescannot be drawn.

For the purpose of meta-analysis, we selected a total of 22

research papers based on the criteria that these papersprovided sufficient details regarding the obtainedperformance and clearly stated different attributes of theexperiments (as listed in Section 2.1); they are: [613, 1618, 2030]. Since each paper reported several experiments(with a median of 4), a total of 103 experiments areavailable for meta-analysis.

We characterise and summarise the outcome of these 103experiments [The data used for meta-analysis are availablein the following link: https://sites.google.com/site/ajitarattaniitaly/resources], based on pre-identified attributes,using meta-analysis. A generalised linear model (GLM)with linear output is trained with a data table containingone experiment per line. This model takes a set of attributes(as binary variables) in order to predict the performancegain as its output. In the following sections, we first explainhow a GLM can be used to characterise the outcome of oneof the 103 experiments and how the attributes are encoded.We then present the experimental protocols. The finalsection presents the findings of the meta-analysis.

2.2.1 Standardising the performance statistics:Proceeding to meta-analysis is not straightforward since the

performance quoted by each paper is not consistent. Inparticular, there are two types of performance metric thatare systematically quoted: EER and rank-one recognition

performance. EER quantifies the probability of error at an

operating threshold where the rate of false acceptance isequal to that of false rejection. It is often quoted in a

biometric verification scenario. The rank-one recognitionperformance, on the other hand, is quoted in a biometricidentification scenario. It is defined as the probability of atarget user is indeed ranked the top from a gallery ofregistered users.

In order to handle the different metrics used, we opted toderive a secondary metric called performance gain. It isdefined as the amount of improvement with respect to the

baseline system as well as the primary target metric onewould like to achieve.

For EER, it is defined as

Perf. gain =EERb EERa

EERb 0(1)

where EERa is the EER of the adaptive system, EERb is theEER of the baseline system and the zero value in thedenominator is the target EER value which one would liketo achieve. For the rank-one recognition performance, weused the following performance gain definition, instead

Perf. gain =Perfa Perfb

1 Perfb(2)

where Perfa denotes the performance of the adaptive systemwhereas Perfb is the performance of the baseline system,and the unit value in the denominator is the target rank-onerecognition performance.



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Despite the differences in definition of the EER and therank-one recognition metrics, the performance gain metrichas the following properties in both cases. First, a positive

performance gain implies improvement over the baselinesystem. Second, the maximum performance gain can bealmost equal to one. This can be simply verified by the factthat Perfa 1 and EERa . 0. Therefore given that anadaptive biometric system is always reported to be better

than its baseline counterpart, the performance gain will bebounded between 0 and 1. If a value of 1 is registered, thenthe target metric is achieved (that is zero for EER and onefor rank-one recognition performance). Therefore the

performance gain we introduced is a viable means to handlethe differences in the two primary metrics used (because ofthe different mode of operation that is, verification oridentification) by the researchers, allowing the performancegain of different systems to be compared on equal ground.

2.2.2 Encoding the attributes of an experimentaloutcome: An experiment is assigned a code of 4 bits inorder to represent the following binary attributes, namely:

1. Presence of quality (quality): 1 means yes; and 0,otherwise2. Use of co-training (co-train): 1 means yes; and 0,otherwise, which implies either self-training or supervisedadaptation3. Use of supervised adaptation (supervised): 1 means yes;and 0, otherwise, which implies semi-supervised adaptation(co-training or self-training)4. Presence of non-match samples in the data set reserved foradaptation (impostor attack): 1 means presence; and 0,otherwise.

For instance, an experiment coded as 1101 implies that the

experiment involves an adaptive biometric system that usesbiometric sample quality, relies on co-training hence,cannot be supervised and contains non-match samples(impostor attacks) in the adaptation data set.

In order to compare different attributes, two types of meta-analysis experiments are performed, namely single-factor andmulti-factor analysis. In the former, only one of the fourattributes (as explained earlier) is considered, whereas in thelatter, all four attributes are considered at the same time. Inorder to carry out the two types of experiments, we used aGLM with linear output that estimates performance gainusing (1) or (2). Apart from these binary attributes, we alsocollected other contextual variables that may impact on thegeneralisation performance of our analysis. These variablesare the database size, number of samples used foradaptation, modality involved etc. Collected data for thecontextual variables [Collected data is available in thetabulated form (excel file) in the following link: https://sites.google.com/site/ajitarattaniitaly/resources ] demonstratethat face is the commonly adopted modality followed byfingerprint in the existing studies. The adopted adaptation

procedure is the same irrespective of the modality involved.Existing adaptive studies have handled short-to-mediumterm temporal variations without explicitly considering theageing effect over long time span. This is evident by thefact that the adopted databases are collected over 14 15weeks in most of the studies.

However, since these contextual variables are not used ininference, they are not considered in fitting the GLM. Byinference, we mean that the model will be used to predict anovel but valid combination of attributes not necessarily

represented by the data table. Therefore our primary goal isto study the attributes that are likely to dictate the

performance gain of an adaptive biometric system. Theinfluence of contextual variables are not of interest here butthis can be a subject of future investigation. Next, weexplain how the GLM is trained.

2.2.3 Training with GLM: The GLM [31] takes a set of

four attributes as independent variables in order to predictthe performance gain as its output. If a; [a1,. . .aN] i s avector of binary attributes encoded as a binary string, andw ; [w1, . . . , wN] is the weight vector of real numberswhose elements are associated with those in a, then, GLM

produces

y = waT

+ w0 (3)

as output. The training process involves estimating the vectorof coefficients w including a bias term, w0 [ R.

After training, the weights {w0, . . . , wN} are obtained. TheGLM is inferred by enumerating a subset of valid attributes

{a}. In the single-factor analysis, (3) is then invoked toconsider only an attribute (N 1) which can take either a 1or a 0. The performance gain inferred by both cases, alongwith their respective upper and lower confidence intervals,are then compared. In the multi-factor analysis scenario, themulti-dimensional attribute a is enumerated but invalidcombinations are excluded. The performance gain of eachvalid attributes in {a} is then compared.

Next, we shall report the findings of single-factor meta-analysis followed by that of multi-factor one.

2.2.4 Findings of single-factor meta-analysis: Theresult for single-factor analysis is shown in Fig. 1.

Supervised adaptation is likely to outperform (about 22.2%more performance gain) semi-supervised method toadaptation such as self-training and co-training. Asmentioned before, performance of the supervised adaptationcan be considered as best-case as the references are adaptedto all the available genuine samples [4]. This is in contraryto methods based on semi-supervised learning in which

Fig. 1 Performance gain for a given attribute obtained by the

trained GLM

Blue (red) bar denotes a 95% confidence interval around the expectedperformance gain, denoted as circle (square), when a given attribute ispresent (absent)



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only selective (mostly highly confidently classified) samplesare used for adaptation. Our meta-analysis findings showlarge variance in the performance gain of the supervisedmethod such that it overlaps significantly with that of semi-supervised one, indicating also the effectiveness of thelatter. However, for the real time deployment of automatedmethods (based on semi-supervised learning), their

performance should be equal to their supervised (manual)

counterparts. Thus, further indicating the need of effectiveadaptation schemes for automated systems. Co-training is likely to boost the performance gain byabout 25.3 % in comparison to its self-training counterpart. The use of biometric sample quality appears to be much

better than not using this information. According to ourfindings, adaptive biometric systems considering qualitymeasurements resulted in about 47% more performance gain. Including impostor samples in the adaptation set can resultin lesser performance gain (16% lesser in our experiments)than if the samples were not present. Since an automatedadaptive system deployed in operational environment isvulnerable to impostor attack, it is unrealistic not to includeimpostor samples in the adaptation set. As a consequence,our exercise here shows that not including impostor samplesin adaptation set can over-estimate the performance gain.

2.2.5 Findings of joint-factor meta-analysis: Fig. 2summarises performance gain for the joint-factor meta-analysis scenario spanned by four binary attributes: quality,co-train, supervised and impostor attack. For instance, 0001implies that an adaptive system that does not use biometricsample quality, that is based on self-training (hence, notsupervised), and the system has been tested with non-matchsamples (impostor attack) in the adaptation data set. Theattribute impostor attack is always true as this strategyreports a less biased performance gain, as explained before.

The first three attributes are then enumerated, excludinginvalid combinations. For instance, it is not possible thatco-training and supervised adaptation to be present at thesame time, as co-training is a semi-supervised learningstrategy; hence, cannot be supervised. Note that theadaptive systems considering supervised adaptation andquality at the same time (quoted as 1011) are managing theupdated references on the basis of quality type. The query

biometric samples are matched to the references of the samequality type [6]. Adaptive systems based on co-trainingexploit mutual and complementary information of the

bi-modal system for template adaptation as well as testing.On the other hand, existing studies on supervised adaptationhave been reported only for single biometric modality. Thisexplains the superiority of co-training over supervisedadaptation.

These findings suggest that there is a natural increase inperformance as one exploits co-training, supervisedadaptation and biometric sample quality systematically. To

sum-up, our meta-analysis findings (both single and jointfactor analysis), support the conjecture that quality andco-train are important attributes for the design of automatedadaptive biometric systems.

3 Novel framework and research directions

In this section, we propose a novel framework and set somefuture research directions that are motivated by the findingsof the meta-analysis.

The results of both the single and joint factor analysisindicate that quality and co-training are importantingredients when designing an adaptive biometric system.Furthermore, when analysing the contextual variables of thereported systems, such as the adopted database size and thenumber of samples, we found that these systems did notconsider the notion of time in order to account for theageing effects which may induce temporal performancevariation over a long time span. Motivated by this, we shall

propose a novel system that can make use of quality andfurther include the notion of time in a single framework.This proposed novel framework is termed as condition-andage-adaptive system.

3.1 Framework for condition and age adaptivesystem

Existing adaptive biometric systems have not considered theageing effect explicitly. A possible reason for this is that,the effect of ageing is often considered to be very differentfrom that caused by biometric sample quality. As aconsequence, methods that aim to address ageing oftenassume that the biometric sample is free from noise, that is,images are often well aligned and acquired in controlledconditions.

In practice, however, an adaptive biometric system has todeal with both the aspects (i.e. adaptation to ageing andquality conditions) for the life-long learning and copingunder non-stationary conditions caused by changes in

biometric sample quality. Two separate strategies areneeded in order to handle variations caused by biometricsample quality and those caused by ageing because whilethe former can cause dramatic changes to the captured

biometric features almost instantaneously, age-relatedchanges are, in comparison, a much more slower andirreversible process. However, beyond a certain limit oftime, the variation because of age-related factors willdominate over that because of the quality-related ones. Thisis illustrated in Fig. 3, where one image is taken under asomewhat controlled condition, another with a significantlydifferent quality (head pose) but taken at the same time, andthen another taken after two years.

Thus in order to cope with the changes in the quality aswell as temporal variations in the input sample, we propose

a possible framework called a condition and age adaptivesystem.

We have adopted a Bayesian approach for the formulationof the system. This choice is appropriate because biometric

Fig. 2 Performance gain along with the confidence intervals of

various configurations spanned by four binary attributes: quality,

co-train, supervised and impostor (see text)



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features are generated by a stochastic process. As a result, notwo consecutive samples obtained from a biometric trait areexactly the same. Thus, the uncertainty at the feature spacecan be characterised using a distribution defined over thefeature space. Indeed, a number of state-of-the-art face andspeaker recognition classifiers are based on Bayesianformulation, for example, [32, 33]. Furthermore, the state-of-the-art online template update method used in thefingerprint literature [810] can be interpreted using aBayesian framework [15].

Next, we introduce the Bayesian framework and explainthe proposed system.

3.1.1 Bayesian framework and notations: Therecursive formulation of Bayesian estimation allows one toupdate the parameters of an old or initial model with anew ones given only the latest sample. Thus, given asequence of observations collected over time, (x1, . . . , xT)or (x1:xT), one can estimate a statistical model parameterised

by u, p(x|u), in the following way (ignoring the normalisingfactor in each step since we are only interested inmaximising the function with respect to u)

p(u|x1:xT)/Ti=1

p(xi|u)p(u)

/

Ti=2

p(xi|u)p(u|x1)

/

Ti=3

p(xi|u)p(u|x1, x2)

/...

/p(xT|u)p(u|x1:xT1) (4)

This recursive formulation implies that in order to calculatethe optimal value of u given all previously observed Tsamples, one only needs to use the parameter calculated upto T 1 to do so. The above recursive formulation shows the

benefit of learning for density-based classifiers as anexample, leading to finding the optimal value of u. Thisrecursive formulation is known as true recursive Bayesianlearning. The right-hand term, p(u|x1:xT), is a reproducingdensity and the term p(u) is a conjugate prior [34].Although the above adaptation is well established andappears to be sound, it does not consider biometric samplequality nor the ageing effect.

A theoretical framework for model (reference) adaptationusing biometric sample quality has been proposed in [15]

but did not consider the time effect. The model ishenceforth referred to a condition adaptive system. In thesubsequent section, we shall propose a theoreticalformulation of condition and age adaptive system that

considers both aspects, hence will be capable of life-longlearning (age adaptive) and learning under non-stationaryenvironment causing concept drift (condition adaptive).

Let x be a biometric feature vector; j[ N, the usersidentity; Qu [ {Q0, Q1, . . . , QQ}. The condition in which a

biometric sample is captured, with Q0 being the enrollment(controlled) condition and Qu|u= 0 being other uncontrolled conditions. Each condition Qu is because of a

number of factors. Let these factors be enumerated by ( f(1)

,. . . , f(F)). For instance, for the face biometrics, f(1) islighting; f(2) corresponds to facial expression types; f(3)

indicates the presence of glasses, f(4) estimates the headpose etc. Then, each Qu is a compound effect of thesefactors, that is, Qu ( f

(1), . . . , f(F)). It is arguable that, inpractice, the condition Q is countable but the total numberof conditions, that is, Q + 1 (including the enrollmentcondition Q0), cannot be determined exactly. This number,however, is not impossible to estimate. For instance, it can

be estimated by clustering quality measures or by manualannotation [15].

Let t[ N, the time at which a sample is captured. This

notion of time is discrete; it is loosely defined such that twosamples that are close in time (say a few seconds apart) willhave the same t value. The rationale for using thisdefinition of t is that the appearance of each biometric traitdoes not change, as a result of ageing, at the same rate.

Using the above notation, the feature distribution of personj can be completely specified by p(x|j, Q, t).

Letx(j, Q, t) be a sample drawn from p(x|j, Q, t). We shallrefer to x(j, Q0, t0) as a reference or model where t0 is the timeat which this sample is obtained; and p(x|u(j, Q0, t0)), a modelwith parameteru(j, Q0, t0) that approximates the true density

p(x|j, Q0, t0). Q0 implies that the sample is taken undercontrolled conditions, that is one in which all the quality-related factors have been carefully controlled, that is,

F(1) = F(1)0 , . . . , F(F) = F(F)0 . The notation also allows us todescribe non-ideal samples, for instance, non-frontal head

poses, presence of glasses, as may be captured duringenrollment, that is, {x(j, Qu, t0)} for u= 0. The distributiondefined over these samples is written as p(x|j, Qu, t0) foreach u; and their corresponding approximated model, asp(x|u(j, Qu, t0)).

Let y [ R be a matching score. Furthermore, let j be theclaimed identity and x ; x(j, Q, t) be a query sampletaken at time t from an unknown person j under anunknown condition state Q. For simplicity and without lossof generality, we also write x ;x(j, Q, t) but write in fullin order to emphasise a particular state, for example, x(j, Q,

t) to emphasise a given time

t and

x(j

,Q

,t) to emphasise

a given state Q .We can then define the following modes of operation

enrol:biometric trait x(j, Q0, t0)

matchtq:x, x(j, Q0, t0) y

matchbayes:x, p(x| , Q, t), p(x|j, Q, t), y

adaptcond:p(x|u(j, Q, t)), x(j, Q, t) p(x|u(j, Q, t))

adapttime: p(x|u(j, Q, t)), x(j, Q, t) p(x|u(j, Q, t))

where Q is a short hand for Q Q to emphasise that Qassumes a particular quality state. The same convention isadopted for t .

Fig. 3 Illustrating an example of face images taken at different

quality conditions (left against middle) and over time (left against

right)



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The operation matchtq produces a similarity or adissimilarity measure between a reference, denoted as x(j ,Q0, t0) and a query sample, x(j, Q, t), with the unknownidentity j. On the other hand, the operation matchbayestypically takes a query sample and a pair of densities (onerepresenting the universal background or world model andanother representing the client-specific model) as input and

produces a likelihood ratio or a posterior probability as

output. For instance, the state-of-the-art speaker verificationsystem computes

matchbayes(x) = logp(x|u(j, Q, t))

p(x|u( , Q, t))

(5)

as output, where p(x|u( , Q, t)) is the density of the generalpopulation, also known as the universal background orworld model

p(x|u( , Q, t) =

j=j

p(x|u(j, Q, t)P(j)

where P(j) weighs the contribution of client-specific densityp(x|u(j, Q, t) to the final general-population density. Thisapproach can be traced back to NeymanPearson theorem.An equivalent, alternative formulation is to invoke theBayes rule, which computes matchbayes(x) P(j|u

(j ,

Q,t)) as output, noting that the parameter u(j , Q,t) is not

the same as the us used before if the posterior probabilityis approximated using a discriminative classifier such as amulti-layer perceptrons. Accordingly, the conditionadaptation (adaptcond) operation adapts an existing model toa new quality condition, Q , represented by a given inputsample taken in a different condition. On the other hand,temporal adaptation (adapttime) updates an existing model to

a new one given the most current sample taken at time t

.A non-adaptive biometric system is neither adaptive to theage-related factors nor the quality related ones. These systemscan be defined as

match(x) = logp(x|u(j, Q0, t0))

p(x|u(, Q0, t0))

Based on the mentioned notation, next we will describe theproposed method.

3.1.2 Towards a condition and age adaptive system:In order to describe the proposed condition and age-adaptive

system, first, we will present a condition-adaptive system andan age-adaptive one separately. The condition adaptivesystem has been proposed in [15]. However, different from[15] which considers only condition-adaptive systems, wefurther introduce the age-adaptive system and the proposedmixture of both condition- and age-adaptive systems here.

Accordingly, a condition-adaptive system operates in twomodes: adaptation mode and matching (comparison) mode.In the adaptation mode, the system adapts its model usingone or more samples taken from an unseen condition Q.

Adaptation

p(x|j, Q, ) = adaptcond( p(x|j, Q0, t0), x) for all Q

p(x| , Q, ) = adaptcond( p(x| , Q0, t0), x) for all Q

The result is a new model that will operate optimally on thenovel condition. We assume for now that the query sample,

x, has already been identified to belong to the claimedidentity j for now.

The matching mode of a condition-adaptive system will beslightly more complicated, since there are several models eachof which can only operate optimally under a given conditionQ. Intuitively, the model with the same condition as thequery sample will be chosen or weighed more heavily thanthe rest. Formally, this weight is called the posterior

probability of a condition Q given the observation a setof quality measures, q-assess(x). The posterior probabilityis written as P(Q|q-assess(x)). The inference using thelog-likelihood ratio is computed as follows:

Matching

match(x) = log

Q P(Q|q-assess(x)) p(x|j, Q, t0)Q P(Q|q-assess(x)) p(x| , Q, t0)

(6)

= logp(x|j, t0, q-assess)

p(x| , t0, q-assess)(7)

The sum over Q implies that this variable is marginalised

because it is not observable. This summation in (6) showsthat if one were to estimate the density p(x|j, t0, q-assess)correctly, one will need sufficient number of quality statesQ + 1. It is recommended that Q be determined byclustering the quality measures, q-assess [15].

The above formulation of condition-adaptive systems canbe found in [15]. An experiment is realised in [6] usingsamples that are manually labelled, providing the mostfavorable scenario for adaptation. Nevertheless, the

performance gain of 30% is significant.An age-adaptive system does not have any mechanism to

handle variation in biometric sample quality. Almost allreported literature assumes that a query sample is always

captured in the controlled condition, Q0. The goal of anage-adaptive system is to retain a number of time-dependentmodels, that is, p(x, Q0, tu) at various point in time{tu|u [ U}. The set U reflects the time window in such away that models that are too old will be eventuallyabandoned [8, 10]. During inference, a more recent modelis given more important consideration than the rest of themodels. The two modes of operation are computed as follows:

Adaptation

p(x|j, Q0, tu) = adapttime(p(x|j, Q0, t0), x(j, Q, tu)

for u [ U

Matching

match(x) = log

u[U f(t tu) p(x|j, Q0, t)

u[U f(t tu) p(x| , Q0, t)

where the function f(.) is a decreasing non-negative function.The argument for function fcannot be negative since t . tu.

Although the age-adaptive model can be easily replacedwith a synthesised model using image-based regression thesynthesised model does not take into account of the person-specific variation. These include facial surgery, the use ofmake-up products such as Botulinum toxin type Ainjections that can make a person look a lot younger than

they actual are, and other life-style-related changes such asdiet regime and weight-lifting exercise. In contrast, byadapting the model to the actually observed samples, anage-adaptive system could possibly be a better solution.



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A major weakness of the age-adaptive system is that it doesnot consider variation in biometric sample quality because ofchanging acquisition conditions. This can be remedied byeither restoring a query sample to the same enrollmentcondition or by allowing the system to adapt to differentquality as well as age-related conditions. These systems will

be described next.A condition- and age-adaptive system contains a set of

references that vary in time as well as in conditions. Duringinference, the more recently adapted models (references)with the matching conditions are given higher weights thanthe rest of the models during inference. The system operatesin two modes, as follows:

Adaptation

p(x|j, Q, tu) = adapttime( p(x|j, Q, t), x(j, Q, tu) for all Q

and u [ U

p(x| , Q, tu) = adapttime( p(x| , Q, t), x(j, Q, tu) for all Q

and u [ U

Matching

match(x)

= log

u

Q f(t tu)P(Q|q-assess(x)) p(x|j, Q, t)

u

Q f(t tu)P(Q|q-assess(x)) p(x| , Q, t)

where the function fis a non-negative decreasing function, sothat more important weights are given to more recent samples.

In the proposed condition and age adaptive system, theinput samples may be labelled and added to the referenceset using either co-training or self-training. However, the

updated reference set will be managed and inferences willbe drawn (for input samples) considering the quality andthe notion of time using the proposed framework.

However, one of the challenges at the moment is the lack oflarge-scale, longitudinal and multi-modal biometric database,capturing quality as well as long-term temporal variations.The available databases capturing long-term ageing effect(for instance MORPH face database [http://www.faceaginggroup.com/projects-morph.html]) do not provideadequate number of samples with changes in qualityconditions (like pose or illumination changes for face) andvice versa. Thus the evaluation of the proposed frameworkremains a part of future work. Nevertheless, the proposedframework is a step ahead to the field of adaptive biometric

systems and will provide important incentives, ideas andfuture directions to the research community.

3.2 New research directions

Our findings related to impostor against non-impostor attacksuggest that classification errors in the labelling process canresult in sub-optimal performance of an adaptive biometricsystem. This is because classification error (falseacceptance) cause adapting user references with theimpostor samples. Thus, increasing the vulnerability totemplate security and undermining the integrity of adaptive

biometric systems. To this front, modelling and early

stoppage of impostor attack is an important researchdirection to be pursued. Developed solutions will allowvendors to adopt auto-update procedures in theircommercial biometric products.

Furthermore, there is a need for incorporating robustlabeling scheme in the adaptive biometric systems. This issupported by our findings related to supervised againstsemi-supervised methods to adaptation where supervisedscheme generalises better than semi-supervised one. Theseresults indicate that the use of confidently classified(labelled) input samples (as used by most of the existingautomated systems) may not be an efficient strategy for

adaptation. Thus emphasising the need for more robustlabelling schemes that are capable of correctly classifyinggenuine (with substantial variations) as well as impostorsamples.

Furthermore, our findings related to supervised againstsemi-supervised and online against offline mode ofadaptation need direct testing via single experimentalframework. This is because of large variance and overlap inthe obtained performance gain on comparing adaptivesystems based on these mentioned attributes. As aconsequence, our results do not allow us to state theconjecture firmly.

4 Conclusion

This paper has worked towards advancing the state-of-the-artrelated to adaptive biometric systems. This has been achieved

by identifying key attributes related to adaptive biometricsystems followed by the comparison and critical analysis.Meta-analysis has been used as a tool to performcomparison, critical analysis and to draw on the limitationsof the existing systems. Specifically, meta-analysis has beenused to gauge the relative impact of single and jointattributes on the generalisation behaviour (performancegain) of adaptive biometric systems. Our meta-analysisfindings has generally supported our conjecture that

biometric sample quality and co-training are important

ingredients for an adaptive biometric system. Furthermore, anovel framework termed condition-and-age adaptive systemhas been proposed and future avenues have been set.Collection of a large scale, longitudinal multibiometricdatabase capturing both quality and age-related variationswill be useful for future work in this field.

5 Acknowledgments

This work was partially supported by Regione Autonomadella Sardegna ref. no. CRP2-442 through the RegionalLaw n.7 for Fundamental and Applied Research, in thecontext of the funded project Adaptive biometric systems:

models, methods and algorithms. Rattani was partlysupported by a grant awarded to Regione Autonoma dellaSardegna, PO Sardegna FSE 2007-2013, L.R. 7/2007Promotion of the scientific research and technologicalinnovation in Sardinia. Poh was partially supported byBiometrics Evaluation and Testing (BEAT), an EU FP7

project with grant no. 284989.

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