Score Fusion by Maximizing the Area under the ROC Curve

The final publication is available at Springer via http://dx.doi.org/10.1007/978-3-642-02172-5_61Information fusion is currently a very active research topic aimed at improving the performance of biometric systems. This paper proposes a novel method for optimizing the parameters of a score fusion model based on maximizing an index related to the Area Under the ROC Curve. This approach has the convenience that the fusion parameters are learned without having to specify the client and impostor priors or the costs for the different errors. Empirical results on several datasets show the effectiveness of the proposed approach.Work supported by the Spanish projects DPI2006-15542-C04 and TIN2008-04571 and the Generalitat Valenciana - Consellería d’Educació under an FPI scholarship.Villegas Santamaría, M.; Paredes Palacios, R. (2009). Score Fusion by Maximizing the Area under the ROC Curve. En Pattern Recognition and Image Analysis: 4th Iberian Conference, IbPRIA 2009 Póvoa de Varzim, Portugal, June 10-12, 2009 Proceedings. Springer Verlag (Germany). 473-480. https://doi.org/10.1007/978-3-642-02172-5_61S473480Toh, K.A., Kim, J., Lee, S.: Biometric scores fusion based on total error rate minimization. Pattern Recognition 41(3), 1066–1082 (2008)Jain, A., Nandakumar, K., Ross, A.: Score normalization in multimodal biometric systems. Pattern Recognition 38(12), 2270–2285 (2005)Gutschoven, B., Verlinde, P.: Multi-modal identity verification using support vector machines (svm). In: Proceedings of the Third International Conference on Information Fusion. FUSION 2000, vol. 2, pp. THB3/3–THB3/8 (July 2000)Ma, Y., Cukic, B., Singh, H.: A classification approach to multi-biometric score fusion. In: Kanade, T., Jain, A., Ratha, N.K. (eds.) AVBPA 2005. LNCS, vol. 3546, pp. 484–493. Springer, Heidelberg (2005)Maurer, D.E., Baker, J.P.: Fusing multimodal biometrics with quality estimates via a bayesian belief network. Pattern Recogn. 41(3), 821–832 (2008)Ling, C.X., Huang, J., Zhang, H.: Auc: a statistically consistent and more discriminating measure than accuracy. In: Proc. of IJCAI 2003, pp. 519–524 (2003)Yan, L., Dodier, R.H., Mozer, M., Wolniewicz, R.H.: Optimizing classifier performance via an approximation to the wilcoxon-mann-whitney statistic. In: Machine Learning, Proceedings of the Twentieth International Conference (ICML 2003), Washington, DC, USA, pp. 848–855. AAAI Press, Menlo Park (2003)Marrocco, C., Molinara, M., Tortorella, F.: Exploiting auc for optimal linear combinations of dichotomizers. Pattern Recogn. Lett. 27(8), 900–907 (2006)Marrocco, C., Duin, R.P.W., Tortorella, F.: Maximizing the area under the roc curve by pairwise feature combination. Pattern Recogn. 41(6), 1961–1974 (2008)Paredes, R., Vidal, E.: Learning prototypes and distances: a prototype reduction technique based on nearest neighbor error minimization. Pattern Recognition 39(2), 180–188 (2006)Villegas, M., Paredes, R.: Simultaneous learning of a discriminative projection and prototypes for nearest-neighbor classification. In: IEEE Conference on Computer Vision and Pattern Recognition. CVPR 2008, pp. 1–8 (2008)Nandakumar, K., Chen, Y., Dass, S.C., Jain, A.: Likelihood ratio-based biometric score fusion. IEEE Transactions on Pattern Analysis and Machine Intelligence 30(2), 342–347 (2008)Poh, N., Bengio, S.: A score-level fusion benchmark database for biometric authentication. In: Kanade, T., Jain, A., Ratha, N.K. (eds.) AVBPA 2005. LNCS, vol. 3546, pp. 1059–1070. Springer, Heidelberg (2005)National Institute of Standards and Technology: NIST Biometric Scores Set - Release 1 (BSSR1) (2004), http://www.itl.nist.gov/iad/894.03/biometricscores/Bengio, S., Mariéthoz, J., Keller, M.: The expected performance curve. In: Proceedings of the Second Workshop on ROC Analysis in ML, pp. 9–16 (2005

Continuous Distribution Approximation and Thresholds Optimization in Serial Multi-Modal Biometric Systems

Multi-modal biometric verification systems use information from several biometric modalities to verify an identity of a person. The false acceptance rate (FAR)and false rejection rate (FRR) are metrics generally used to measure the performance of such systems.In this paper, we first approximate the score distributions of both genuine users and impostors by continuous distributions. Then we incorporate the exact expressions of the distributions in the formulas for the expected values of both FAR and FRR for each matcher. In order to determine the upper and lower acceptance thresholds in the sequential multi-modal biometric matching, we further minimize the expected values of FAR and FRR for the entire processing chain. We propose a non-linear bi-objective programming problem whose objective functions are the two error probabilities. We analyze the efficient set of the bi-objective problem, and derive an efficient solution as a best compromise between the error probabilities. Replacing the least squares approximation of the score distributions by a continuous distributionapproximation, this approach modifies the method presented in Stanojević et al. [15] (doi: 10.1109/ICCCC.2016.7496752) (a).The results of our experiments showed a good performance of the sequential multiple biometric matching system based on continuous distribution approximation and optimized thresholds.(a)Reprinted (partial) and extended, with permission based on License Number3938230385072 © [2016] IEEE, from "Computers Communications and Control (ICCCC), 2016 6th International Conference on"

Contributions to High-Dimensional Pattern Recognition

This thesis gathers some contributions to statistical pattern recognition particularly targeted at problems in which the feature vectors are high-dimensional. Three pattern recognition scenarios are addressed, namely pattern classification, regression analysis and score fusion. For each of these, an algorithm for learning a statistical model is presented. In order to address the difficulty that is encountered when the feature vectors are high-dimensional, adequate models and objective functions are defined. The strategy of learning simultaneously a dimensionality reduction function and the pattern recognition model parameters is shown to be quite effective, making it possible to learn the model without discarding any discriminative information. Another topic that is addressed in the thesis is the use of tangent vectors as a way to take better advantage of the available training data. Using this idea, two popular discriminative dimensionality reduction techniques are shown to be effectively improved. For each of the algorithms proposed throughout the thesis, several data sets are used to illustrate the properties and the performance of the approaches. The empirical results show that the proposed techniques perform considerably well, and furthermore the models learned tend to be very computationally efficient.Villegas Santamaría, M. (2011). Contributions to High-Dimensional Pattern Recognition [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/10939Palanci

Framework for evaluation of multimodal biometric systems

Primena biometrijskih tehnologija danas u ljudskom društvu postaje sve češća, gotovo da možemo konstatovati da je ona deo naše svakodnevnice. Prilikom implementacije biometrijske autentikacije, svaki sistem ima svoje zahteve i ograničenja, u zavisnosti od konkretnog scenarija u kojem se sistem koristi. Za odabir odgovarajućeg biometrijskog modaliteta, kao i algoritama za rad sa biometrijskim modalitetom, neophodno je sprovesti odgovarajuću evaluaciju performansi rada biometrijskog sistema. Ipak, ovu evaluaciju nije uvek lako sprovesti, kako za unimodalne, tako i za multimodalne biometrijske sisteme. Čak i kada su dostupne javne baze biometrijskih podataka za evaluaciju algoritama određenog biometrijskog modaliteta, potrebno je prilagoditi rad sistema protokolu testiranja koji konkretna baza definiše. U slučaju multimodalnog pristupa, evaluacija se dodatno komplikuje usled upotrebe različitih algoritama za fuziju informacija. Kako u dostupnoj relevantnoj literaturi nije pronađen detaljan prikaz modela evaluacije multimodalnih biometrijskih sistema, a radi prevazilaženja ovih teškoća, u okviru ovog doktorata definisan je objedinjeni model evaluacije multimodalnih biometrijskih sistema. Za definisanje ovog modela primenjena je MDA (Model Driven Architecture) paradigma. U okviru objedinjenog modela dat je metamodel evaluacije multimodalnih biometrijskih sistema, koji predstavlja svojevrsnu ontologiju pojmova značajnih za ovu oblast. Primenom ovog metamodela, moguće je kreirati modele evaluacije različitih biometrijskih sistema. Na osnovu modela evaluacije multimodalnih biometrijskih sistema kreiran je prototip okvira za evaluaciju multimodalnih biometrijskih sistema. Pomoću predloženog okvira moguća je evaluacija performansi multimodalnog biometrijskog sistema u različitim slučajevima korišćenja. Eksperimentalni rezultati evaluacije nad konkretnom bazom i algoritmima pokazuju da primena okvira skraćuje za četiri puta vreme potrebno za evaluaciju. Razvijena je i nova metoda za analitičko određivanje praga osetljivosti u skladu sa postavljenim parametrima željenog ponašanja sistema. Na kraju, na primeru alata koji je koristio neke od funkcionalnosti okvira, prikazano je kako primena okvira može učiniti efikasnijim proces obrazovanja inženjera u oblasti biometrije.Application of biometric technologies in our contemporary human society is getting more frequent, so we can almost state that biometric technologies are part of our everyday life. When implementing biometric authentication, each system has specific requirements and constraints, which depend on the actual scenario in which the system is being used. In order to choose the adequate biometric modality, and also a fitting algorithm for the chosen modality, it is necessary to perform an evaluation of the biometric system performance. However, this evaluation is not always easy to conduct. This fact is true for both the unimodal and multimodal biometric systems. Even when open biometrics databases are available for evaluation, it is necessary to adapt system to work with testing protocol of the chosen open database. Moreover, if the biometric system uses multiple biometric modalities, evaluation gets even more complicated because of different available fusion algorithms. In order to overcome these difficulties, as there is not a detailed model of multimodal biometric systems available in relevant literature, this thesis presents a unified multimodal biometric systems evaluation model. Presented model is based on MDA (Model Driven Architecture) paradigm. A part of the unified multimodal biometric systems evaluation model is the metamodel of multimodal biometric system evaluation, which represents an ontology of terms used in this domain. Based on unified multimodal biometric systems evaluation model, a prototype framework for multimodal biometrics systems evaluation has been created. By using proposed framework it is possible to evaluate performance of multimodal biometric system in different use cases. Experimental evaluation results based on used database and algorithms show that the use of framework shortens time necessary for evaluation to a quarter of previously required time. Also, a new analytical method for biometric system threshold optimization, based on the predefined desired system behavior was developed. As final, a learning tool based on some of the framework functionalities is used to show how the use of framework can make the process of educating engineers in the field of biometrics more efficient