Binary Biometric Representation through Pairwise Adaptive Phase Quantization

Extracting binary strings from real-valued biometric templates is a fundamental step in template compression and protection systems, such as fuzzy commitment, fuzzy extractor, secure sketch, and helper data systems. Quantization and coding is the straightforward way to extract binary representations from arbitrary real-valued biometric modalities. In this paper, we propose a pairwise adaptive phase quantization (APQ) method, together with a long-short (LS) pairing strategy, which aims to maximize the overall detection rate. Experimental results on the FVC2000 fingerprint and the FRGC face database show reasonably good verification performances.\ud \u

Fingerprint and On-Line Signature Verification Competitions at ICB 2009

This paper describes the objectives, the tasks proposed to the participants and the associated protocols in terms of database and assessment tools of two competitions on fingerprints and on-line signatures. The particularity of the fingerprint competition is to be an on-line competition, for evaluation of fingerprint verification tools such as minutiae extractors and matchers as well as complete systems. This competition will be officialy launched during the ICB conference. The on-line signature competition will test the influence of multi-sessions, environmental conditions (still and mobility) and signature complexity on the performance of complete systems using two datasets extracted from the BioSecure database. Its result will be presented during the ICB conference

Applying the Upper Integral to the Biometric Score Fusion Problem in the Identification Model

This paper presents a new biometric score fusion approach in an identification system using the upper integral with respect to Sugeno's fuzzy measure. First, the proposed method considers each individual matcher as a fuzzy set in order to handle uncertainty and imperfection in matching scores. Then, the corresponding fuzzy entropy estimates the reliability of the information provided by each biometric matcher. Next, the fuzzy densities are generated based on rank information and training accuracy. Finally, the results are aggregated using the upper fuzzy integral. Experimental results compared with other fusion methods demonstrate the good performance of the proposed approach

Detecting Morphing Attacks via Continual Incremental Training

Scenarios in which restrictions in data transfer and storage limit the possibility to compose a single dataset -- also exploiting different data sources -- to perform a batch-based training procedure, make the development of robust models particularly challenging. We hypothesize that the recent Continual Learning (CL) paradigm may represent an effective solution to enable incremental training, even through multiple sites. Indeed, a basic assumption of CL is that once a model has been trained, old data can no longer be used in successive training iterations and in principle can be deleted. Therefore, in this paper, we investigate the performance of different Continual Learning methods in this scenario, simulating a learning model that is updated every time a new chunk of data, even of variable size, is available. Experimental results reveal that a particular CL method, namely Learning without Forgetting (LwF), is one of the best-performing algorithms. Then, we investigate its usage and parametrization in Morphing Attack Detection and Object Classification tasks, specifically with respect to the amount of new training data that became available.Comment: Paper accepted in IJCB 2023 conferenc

A computer-aided diagnosis system for glioma grading using three dimensional texture analysis and machine learning in MRI brain tumour

Glioma grading is vital for therapeutic planning where the higher level of glioma is associated with high mortality. It is a challenging task as different glioma grades have mixed morphological characteristics of brain tumour. A computer-aided diagnosis (CAD) system based on three-dimensional textural grey level co-occurrence matrix (GLCM) and machine learning is proposed for glioma grading. The purpose of this paper is to assess the usefulness of the 3D textural analysis in establishing a malignancy prediction model for glioma grades. Furthermore, this paper aims to find the best classification model based on textural analysis for glioma grading. The classification system was evaluated using leave-one-out cross-validation technique. The experimental design includes feature extraction, feature selection, and finally the classification that includes single and ensemble classification models in a comparative study. Experimental results illustrate that single and ensemble classification models, can achieve efficient prediction performance based on 3D textural analysis and the classification accuracy result has significantly improved after using feature selection methods. In this paper, we compare the proficiency of applying different angles of 3D textural analysis and different classification models to determine the malignant level of glioma. The obtained sensitivity, accuracy and specificity are 100%, 96.6%, 90% respectively. The prediction system presents an effective approach to assess the malignancy level of glioma with a non-invasive, reproducible and accurate CAD system for glioma grading

Face Video Competition

The final publication is available at Springer via http://dx.doi.org/10.1007/978-3-642-01793-3_73Person recognition using facial features, e.g., mug-shot images, has long been used in identity documents. However, due to the widespread use of web-cams and mobile devices embedded with a camera, it is now possible to realise facial video recognition, rather than resorting to just still images. In fact, facial video recognition offers many advantages over still image recognition; these include the potential of boosting the system accuracy and deterring spoof attacks. This paper presents the first known benchmarking effort of person identity verification using facial video data. The evaluation involves 18 systems submitted by seven academic institutes.The work of NPoh is supported by the advanced researcher fellowship PA0022121477of the Swiss NSF; NPoh, CHC and JK by the EU-funded Mobio project grant IST-214324; NPC and HF by the EPSRC grants EP/D056942 and EP/D054818; VS andNP by the Slovenian national research program P2-0250(C) Metrology and Biomet-ric System, the COST Action 2101 and FP7-217762 HIDE; and, AAS by the Dutch BRICKS/BSIK project.Poh, N.; Chan, C.; Kittler, J.; Marcel, S.; Mc Cool, C.; Rua, E.; Alba Castro, J.... (2009). Face Video Competition. En Advances in Biometrics: Third International Conference, ICB 2009, Alghero, Italy, June 2-5, 2009. Proceedings. 715-724. https://doi.org/10.1007/978-3-642-01793-3_73S715724Messer, K., Kittler, J., Sadeghi, M., Hamouz, M., Kostyn, A., Marcel, S., Bengio, S., Cardinaux, F., Sanderson, C., Poh, N., Rodriguez, Y., Kryszczuk, K., Czyz, J., Vandendorpe, L., Ng, J., Cheung, H., Tang, B.: Face authentication competition on the BANCA database. In: Zhang, D., Jain, A.K. (eds.) ICBA 2004. LNCS, vol. 3072, pp. 8–15. Springer, Heidelberg (2004)Messer, K., Kittler, J., Sadeghi, M., Hamouz, M., Kostin, A., Cardinaux, F., Marcel, S., Bengio, S., Sanderson, C., Poh, N., Rodriguez, Y., Czyz, J., Vandendorpe, L., McCool, C., Lowther, S., Sridharan, S., Chandran, V., Palacios, R.P., Vidal, E., Bai, L., Shen, L.-L., Wang, Y., Yueh-Hsuan, C., Liu, H.-C., Hung, Y.-P., Heinrichs, A., Muller, M., Tewes, A., vd Malsburg, C., Wurtz, R., Wang, Z., Xue, F., Ma, Y., Yang, Q., Fang, C., Ding, X., Lucey, S., Goss, R., Schneiderman, H.: Face authentication test on the BANCA database. In: Int’l. Conf. Pattern Recognition (ICPR), vol. 4, pp. 523–532 (2004)Phillips, P.J., Flynn, P.J., Scruggs, T., Bowyer, K.W., Chang, J., Hoffman, K., Marques, J., Min, J., Worek, W.: Overview of the Face Recognition Grand Challenge. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 947–954 (2005)Bailly-Baillière, E., Bengio, S., Bimbot, F., Hamouz, M., Kittler, J., Marithoz, J., Matas, J., Messer, K., Popovici, V., Porée, F., Ruiz, B., Thiran, J.-P.: The BANCA Database and Evaluation Protocol. In: Kittler, J., Nixon, M.S. (eds.) AVBPA 2003. LNCS, vol. 2688. Springer, Heidelberg (2003)Turk, M., Pentland, A.: Eigenfaces for Recognition. Journal of Cognitive Neuroscience 3(1), 71–86 (1991)Martin, A., Doddington, G., Kamm, T., Ordowsk, M., Przybocki, M.: The DET Curve in Assessment of Detection Task Performance. In: Proc. Eurospeech 1997, Rhodes, pp. 1895–1898 (1997)Bengio, S., Marithoz, J.: The Expected Performance Curve: a New Assessment Measure for Person Authentication. In: The Speaker and Language Recognition Workshop (Odyssey), Toledo, pp. 279–284 (2004)Poh, N., Bengio, S.: Database, Protocol and Tools for Evaluating Score-Level Fusion Algorithms in Biometric Authentication. Pattern Recognition 39(2), 223–233 (2005)Martin, A., Przybocki, M., Campbell, J.P.: The NIST Speaker Recognition Evaluation Program, ch. 8. Springer, Heidelberg (2005

Support vector machine regression for robust speaker verification in mismatching and forensic conditions

The final publication is available at Springer via http://dx.doi.org/10.1007/978-3-642-01793-3_50Proceedings of Third International Conference, ICB 2009, Alghero, ItalyIn this paper we propose the use of Support Vector Machine Regression (SVR) for robust speaker verification in two scenarios: i) strong mismatch in speech conditions and ii) forensic environment. The proposed approach seeks robustness to situations where a proper background database is reduced or not present, a situation typical in forensic cases which has been called database mismatch. For the mismatching condition scenario, we use the NIST SRE 2008 core task as a highly variable environment, but with a mostly representative background set coming from past NIST evaluations. For the forensic scenario, we use the Ahumada III database, a public corpus in Spanish coming from real authored forensic cases collected by Spanish Guardia Civil. We show experiments illustrating the robustness of a SVR scheme using a GLDS kernel under strong session variability, even when no session variability is applied, and especially in the forensic scenario, under database mismatch.This work has been supported by the Spanish Ministry of Education under project TEC2006-13170-C02-0