Online and Offline Fingerprint Template Update Using Minutiae: An Experimental Comparison Biagio Freni, Gian Luca Marcialis, and Fabio Roli University of Cagliari Department of Electrical and Electronic Engineering Piazza d Armi - I-09123 Cagliari (Italy) {biagio.freni,marcialis,roli}@diee.unica.it Abstract. Although the template fingerprint collected during the registration phase of personal verification systems can be considered in principle as representative of the subject identity, some recent works pointed out that it is not completely able to follow the intra-class variations of the fingerprint shape. Accordingly, making these systems adaptive to these variations is one of the most interesting problems, and is often called as the template updating problem. In this paper, two different approaches for fingerprint template updating are compared by experiments. The first one, already proposed in other works, relies on the concept of online template updating, that is, the fingerprint template is updated when the system is operating. As alternative, we propose the offline template update, which requires the collection of a representative batch of samples when the system is operating. They concur to the template updating when the system is offline, that is, it is not operating. Preliminary experiments carried out on the FVC data sets allow to point out some differences among the investigated approaches. 1 Introduction Fingerprints [1] are the most used and popular among the biometric traits, for personal identification and verification. Several approaches have been proposed to fingerprint matching. The best ones are based on the so-called minutiae, that is, the terminations and bifurcations of the ridge lines [1]. The core of these verification systems, which allow to obtain good verification results as shown in several works [1], is the collection of one or more samples of each user s fingerprint, in order to extract a representative template, that is, a representative set of minutiae of the given subject. This is often performed during the so-called registration phase, in which the user, under the supervision of a human expert, put his finger on the electronic scanner surface for his fingerprint acquisition. It is easy to notice that this phase is expensive, especially when more samples must be collected. Further, the will of cooperating of the subject is crucial. On the other hand, when the system is operating, a lot of fingerprint samples is captured, but, being out of a human expert supervision, they exhibit several differences. Moreover, intra-class variations of the fingerprint shape arise (due to F.J. Perales and R.B. Fisher (Eds.): AMDO 2008, LNCS 5098, pp. 441 448, 2008. c Springer-Verlag Berlin Heidelberg 2008
442 B. Freni, G.L. Marcialis, and F. Roli scratches, increase of moisture or dryness, aging etc.), and the captured fingerprints can exhibit, over the time, minutiae sets different from that captured in the registration phase. Accordingly, recent works pointed out the need of making the current fingerprint verification systems adaptive to the above variations. This requirement can be realized by updating the template over the time [2,3,4]. We can currently subdivide the template update approaches in supervised and semi-supervised. The former [2] require, after a fixed period of time, a novel (supervised) registration phase in which fingerprint samples are collected. If necessary, the most representative samples among the collected ones can be selected. In [2], some clustering algorithms are proposed to this goal, as alternative to the manual selection. The latter introduce the concept of semi-supervised template updating. In other words, the additional fingerprint samples are collected and pseudo-labelled during the verification phase and are exploited to update the template. The concept of sample pseudo-labeling relies on the decision of the verification system, and not on the explicit intervention of the human expert, thus justifying the term semi-supervised template updating. Hence, the labeling, that is, the association of the identity to the sample, is performed automatically, without the human contribute, making the template update process cheaper and feasible over frequent periods of time. With regard to fingerprint template updating, methods in Refs. [3,4] try to update templates during the system operations. The claim is that these system can increase the reliability of templates without loss of time in additional registration phases. This can be realized by adopting algorithms aimed to the fusion of the input minutiae-set and the template minutiae-set. The output is a novel template, called supertemplate, which should be more representative than the original template. On the other hand, it is reasonable to hypothesise that, if a batch of unlabelled samples can be collected before updating, so increasing the centralization degree of the system, these samples could be exploited when the system is not operating. This approach can be called offline template update. In this paper, we compare online and offline approaches to fingerprint template updating by experiments on FVC2000 benchmark data sets [5]. Update is performed by adopting the fusion of minutiae-set proposed in [3] in order to generate a supertemplate, but also the use of multiple templates per client is investigated. Reported results allow to point out some interesting differences among the proposed methods. The paper is organized as follows. Section 2 describes the online and offline systems to fingerprint template updating. Section 3 reports some experimental results. Section 4 draw some preliminary conclusions. 2 Offline and Online Fingerprint Template Updating Let us consider a fingerprint verification system, with C enrolled users, also called clients. For each client c, only one template is initially stored. Thus, the related
Online and Offline Fingerprint Template Update Using Minutiae 443 gallery, T c, is made up of only one template. No limit to the size of T c is given in this paper, but it is worth noting that in real verification systems this size depends on the memory available and constraints in terms of verification time. Proposed method is shown in pseudo-code form by Algorithm 1 for online template update and Algorithm 2 for offline template update. In both algorithms, the boolean variable buildsupert emplate is true if the system is constrained to work with only one template per class. Thus, a supertemplate, i.e. a sort of average minutiae set from those of samples inserted into the client s gallery, must be builded. Our supertemplate generation algorithm is the same proposed in Ref. [3]. Algorithm 1 works as follows. As a novel fingerprint is submitted to the system, features are extracted in order to generate an input feature set x. Thissetis compared with the template set of the claimed identity c. As well-known, the result of this comparison is a matching score, usually a real value into [0, 1], which represents the degree of similarity among compared fingerprints. Function which outputs such a value is called meanscore(x, T c ) in Algorithm 1. If this matching score exceeds a threshold value such that the probability of being impostor is low enough, the related input can be fused with existing template or added to the client s gallery. In the second case, as the size of T c increases, multiple templates are available. Algorithm 1 stops according to a fixed criterion. This criterion depends on the particular context. For example, update can stop after a certain period of time is passed, or a certain number of verification attempts has been submitted, in order to test the system performance and monitor updated templates into each gallery. Algorithm 2 works similarly to the previous one, with the difference that it is offline, i.e. works on a batch of unlabelled samples collected during system operations. This leads to the fact that it is possible to select, for each client, the sample exhibiting the highest matching score for update, among the ones with probability of being impostors low enough (line 6). In our implementation of these algorithms: 1. Feature set x, and all templates in the union of clients galleries, namely, T, are represented by minutiae [1], which are terminations and bifurcations of fingerprint ridge lines. String algorithm has been implemented to match minutiae sets each others [1]. Further details can be found in [6]; 2. threshold has been set to 1%FAR operational point, in order to have a low probability (1%) of introducing impostors into the gallery. A more conservative threshold was at zerofar, but in practical cases it allows to introduce a significantly smaller number of genuine users, thus slowing the performance increase of template update; 3. threshold can be evaluated only on T, which is the only labelled set available for this aim. In principle, threshold should be updated at each insertion of novel samples, but this increases computational time of updating, and it is not statistically significant. Therefore, we have chosen to update threshold after that at least one sample has been submitted for all clients (see Algorithm 1-2, lines 2-3).
444 B. Freni, G.L. Marcialis, and F. Roli Algorithm 1. OnlineTemplateUpdate Require: { Let C be the number of clients Let M be the maximum number of template per client Let T c the template gallery of class c Let s = meanscore (x, T c ) be a function such that s is the average score of the input sample x on the template set T c Let buildsupert emplate be a boolean variable which is true if a supertemplate must be builded Let supert c = fuse(t c ) be a supertemplate building function} 1: repeat 2: T = ct c 3: Estimate threshold on T 4: for each client c =1..C do 5: x i {where i is an input sample claiming c-th identity} 6: s = meanscore(x, T c ) 7: if s>thresholdthen 8: if buildsupert emplate then 9: supert c = fuse(t c ) 10: T c = {supert c } 11: else 12: T c = T c {x} 13: end if 14: end if 15: end for 16: until (stop criterion is not met) 3 Experimental Results 3.1 Data Sets The FVC2000 data sets consist of four fingerprint databases (Db1-Db4) adopted for the Fingerprint Verification Competition held in 2000 [5]. Each one is made up of eight samples per 100 different fingers. The images of fingerprint impressions were collected with different sensors. In particular, the Db1 was acquired with a low-cost optical sensor (300x300 pixels), the Db2 with a low-cost capacitive sensor (256x364 pixels), the Db3 with another optical sensor (448x478 pixels) and for the Db4 a synthetic generator (240x320 pixels). It is worth noting that, due to the small number of samples per client, FVC data sets are not fully appropriate for this task. In fact, template update algorithms should be tested on data sets with large number of samples per client, possibly captured at different periods of time [4]. On the other hand, FVC data sets exhibit large intra-class variations, thus can be useful for assessing a preliminary evaluation of template update algorithms, as done in Ref. [3].
Online and Offline Fingerprint Template Update Using Minutiae 445 Algorithm 2. OfflineTemplateUpdate Require: { Let C be the number of clients Let M be the maximum number of template per client Let X c be the batch of unlabelled samples which claim to be the c-th client Let T c the template gallery of client c Let s = meanscore (x, T c ) be a function such that s is the average score of the input sample x on the template set T c Let buildsupert emplate be a boolean variable which is true if a supertemplate must be builded Let supert c = fuse(t c ) be a supertemplate building function} 1: repeat 2: T = ct c 3: Estimate threshold on T 4: for each client c =1..C do 5: X ct = {x X c meanscore(x, T c ) > threshold} 6: y = argmax x Xct meanscore(x, T c ) 7: T c = T c {y} 8: X c = X c {y} 9: end for 10: X t = cx ct 11: until (X t φ) 12: if buildsupert emplate then 13: for each client c =1..C do 14: supert c = fuse(t c ) 15: T c = {supert c } 16: end for 17: end if 3.2 Experimental Protocol We subdivided each data set in three partitions. The original labeled set is made up of one finger sample per subject. The set of unlabeled data is made up of six samples per subject and the test set, which we used for comparing the template updating approaches, is made up of one sample. We carried out seven experiments for each data set and averaged the results. In each experiment, the labeled set was always made up of the first sample, the test set was made up from the second to the eighth sample, and the unlabeled set was made up of the remaining six samples. In online experiments, for each client a sample has been randomly chosen from the unlabelled set, with equal prior for genuine and impostors classes (on average, six genuine and seven impostors have been submitted for each client). In offline experiments, for each client, the same seven impostors used in the related online experiment have been considered with the six genuine samples, in order to make comparable reported results.
446 B. Freni, G.L. Marcialis, and F. Roli 3.3 Results First of all, we performed a study on the quality of images in FVC data sets. To this aim, we applied the NFIQ quality evaluation algorithm proposed by NIST [7]. NFIQ classifies fingerprint images in five cateogories: Excellent, Very good, Good, Fair, Poor. In all cases, the majority of images fell in the first and second classes. This allowed to avoid the problem of image quality which could affect template update effectiveness [4], and to better focus on the pros and cons of investigated algorithms. Figures 1(a-d) show the ROC curves obtained on the FVC2000 test sets. For each plot, five curves are shown: 1) unimproved, which refers to the ROC curve obtained without template updating, 2) online multiple templates, related to Algorithm 1 in which the variable buildsupert emplate FALSE, 3) online supertemplate, where buildsupert emplate TRUE in Algorithm 1, 4) offline multiple templates related to Algorithm 2 in which the variable buildsupert emplate FALSE, 5) offline supertemplate, where buildsupertemplate TRUE in Algorithm 2. It is easy to see that all template update approaches have been effective with respect to the unimproved system. By comparing offline curves each others, as well as online ones, it can be noticed that using multiple templates approach led to a better result than adopting a supertemplate, with the exception of FVC2000-Db4 data set. This can be explained as follows: (1) the minutiae sets fusion was dependent on the matching algorithm - String in this case. Finding a good alignment is an open issue, but it can be hypothesised that template update results can be improved; (2) performing fusion of templates smoothed the intra-class variations among them. In general, it is expected that using multiple templates is better than using only one (super)template. The exception of FVC2000-Db4 can be explained by hypothesising that intra-class variations were less evident in this data set, thus supertemplate avoided the redundancy of multiple templates. Some interesting findings can be noticed by comparing online and offline algorithms each others. In the case of offline vs. online using supertemplate, the performance was quite similar, with a small superiority of the online approach. This result is remarkable, and points out a clear advantage of these approaches with respect to offline ones. Things appear to change by considering online vs. offline using multiple templates. However, a clear superiority of the offline method appeared only for FVC2000-Db1 and Db2, whilst in the other ones online approach exhibited a better performance. This result and the previous one motivate further investigations on online and offline template update approaches. 3.4 Discussion on Results Comparison of online and offline approaches must be done by considering: Performance achieved (ROC curves) Centralization degree of the system
Online and Offline Fingerprint Template Update Using Minutiae 447 Memory available in smart cards or other supports embedding the personal information of each client Managing classification errors during update In our opinion, the first requirement helps in evaluating the second one, whilst the third one can be referred to the particular technology adopted. In fact, offline methods require that the centralized part of the system must capture, and store, over the time, a large number of queries. The template update process can be clearly separated from the verification phase. Using offline approaches derives from the hypothesis that the more samples are available, the more selective the template updating is (Algorithm 2). However, reported results showed that centralization is not always necessary. Online approaches, which decrease the centralization degree of the system (since the update is performed at once), have shown to be effective on the adopted data sets, and exhibit a verification accuracy in some cases superior than that of offline approaches. FVC2000Db1a UPDATE FVC2000Db2a UPDATE unimproved online super template online multiple templates offline super template offline multiple templates unimproved online super template online multiple templates offline super template offline multiple templates FRR FRR FAR (a) FVC2000Db1 10 0 10 0 FAR (b) FVC2000Db2 FVC2000Db3a UPDATE FVC2000Db4a UPDATE unimproved online super template online multiple templates offline super template offline multiple templates unimproved online super template online multiple templates offline super template offline multiple templates FRR FRR FAR (c) FVC2000Db3 FAR (d) FVC2000Db4 Fig. 1. ROC Curve of the offline and online algorithms on the four dataset FVC2000
448 B. Freni, G.L. Marcialis, and F. Roli A different problem is given by the choice of using multiple templates or a supertemplate. This is strongly dependent on the available technology (third item), and cannot be treated in this paper. Adopted algorithms do not take into account memory or verification time limitations, but this is a topic to be investigated. The fourth item brings to a still open issue. In particular, how do classification errors (i.e. impostors with score higher than threshold) impact on the obtained galleries of templates when performing update? How much large are the security breatches caused by classification errors? This is a clear problem for current template update algorithms, especially for online ones. No solutions have been proposed so far. The common guideline is to set threshold high enough to reduce this problem. At current state of our knowledge, the human intervention should be taken into account in order to monitor the obtained galleries over certain periods of time. 4 Conclusions In this paper, we started to investigate the differences of online and offline approaches to fingerprint template update in personal verification systems. This preliminary experimentation has been performed on FVC2000 data sets, which are not fully suitable for the task, but have been already adopted for template update algorithms evaluation. Their images exhibit a high quality on average, thus help in studying the template update effectiveness under the hypothesis of cooperative user population. It has been found that performance of offline and online approaches are comparable in some cases. In other cases, online approaches performed better than offline ones. This is a quite surprising result which is worthy to be better investigated in future works. References 1. Maltoni, D., Maio, D., Jain, A.K., Prabhakar, S.: Handbook of fingerprint recognition. Springer, Heidelberg (2003) 2. Uludag, U., Ross, A., Jain, A.K.: Biometric template selection and update: a case study in fingerprints. Pattern Recognition 37(7), 1533 1542 (2004) 3. Jiang, X., Ser, W.: Online Fingerprint Template Improvement. IEEE Transactions on Pattern Analysis and Machine Intelligence 24(8), 1121 1126 (2002) 4. Ryu, C., Kim, H., Jain, A.K.: Template Adaptation based Fingerprint Verification. In: 18th ICPR 2006, vol. 4, pp. 582 585. IEEE Computer Society, Los Alamitos (2006) 5. http://bias.csr.unibo.it/fvc2000/ 6. Jain, A.K., Hong, L., Bolle, R.: On-line Fingerprint Verification. IEEE Transactions on Pattern Analysis and Machine Intelligence 19(4), 302 314 (1997) 7. Tabassi, E., Wilson, C.L., Watson, C.I.: Fingerprint image quality, NIST Technical Report NISTIR 7151 (August 2004)