Biometric presentation attack detection: beyond the visible spectrum

The increased need for unattended authentication in multiple scenarios has motivated a wide deployment of biometric systems in the last few years. This has in turn led to the disclosure of security concerns specifically related to biometric systems. Among them, presentation attacks (PAs, i.e., attempts to log into the system with a fake biometric characteristic or presentation attack instrument) pose a severe threat to the security of the system: any person could eventually fabricate or order a gummy finger or face mask to impersonate someone else. In this context, we present a novel fingerprint presentation attack detection (PAD) scheme based on i) a new capture device able to acquire images within the short wave infrared (SWIR) spectrum, and i i) an in-depth analysis of several state-of-theart techniques based on both handcrafted and deep learning features. The approach is evaluated on a database comprising over 4700 samples, stemming from 562 different subjects and 35 different presentation attack instrument (PAI) species. The results show the soundness of the proposed approach with a detection equal error rate (D-EER) as low as 1.35% even in a realistic scenario where five different PAI species are considered only for testing purposes (i.e., unknown attacks

How to improve students’ experience in blending learning? Evidence from the perceptions of students in a Postgraduate Master’s Degree

[EN] This paper examines the perceptions of a group of students of a Postgraduate Master’s Degree in Cosmetics Industry at the Universitat de València, delivered with a blended learning modality, in relation to their experience in face-to-face learning and differentiating between those with or without a previous background in a remote online learning environment, with the added purpose of identifying strategies to enhance that experience, while offering further evidence for scholars, educators and institutions in this field. To this end, a survey with open questions devised ad hoc leaning on our literature review was submitted to a group of 114 students of the Master’s Degree in the period 2017-2020. Students were enquired about the pros and cons of their blended learning experience in relation to the traditional face-to-face learning, and which modality they would choose next time if both were offered, only considering the achievement, experience and satisfaction, regardless of the price. 77 students of our initial sample participated in the questionnaire, 38 of them without previous experience in blended or distance learning. The results show a certain predilection for face-to-face learning, especially in the group of newbies in blended or distance learning. They highlight how they miss a closer interaction with their peers and professors and the difficulties to assimilate certain content, while appraising the flexibility, autonomy, and the self-pace of the blended learning modality. Correspondingly, students with experience in remote online education settings generally show a better predisposal and find fewer disadvantages in blended learning. This suggests that the factor of experience and adaptation to new tools and methods improves student perception and confidence and shapes their preferences, with a foreseeable growing acceptance of blended learning in the future. Finally, the outcome allows us to define a series of strategies to improve the achievement, experience, and satisfaction of students in this learning context.Garcia-Ortega, B.; Galan-Cubillo, J. (2021). How to improve students’ experience in blending learning? Evidence from the perceptions of students in a Postgraduate Master’s Degree. WPOM-Working Papers on Operations Management. 12(2):1-15. https://doi.org/10.4995/wpom.15677OJS115122Al-Khanjari, Z. A. S. (2018). Applying online learning in software engineering education. In Computer Systems and Software Engineering: Concepts, Methodologies, Tools, and Applications (pp. 217-231). IGI Global. https://doi.org/10.4018/978-1-5225-3923-0.ch010Angeli, C., Valanides, N., & Bonk, C. J. (2003). Communication in a web‐based conferencing system: the quality of computer‐mediated interactions. British Journal of Educational Technology, 34(1), 31-43. https://doi.org/10.1111/1467-8535.00302Arroyo-Barrigüete, J. L., López-Sánchez, J. I., Minguela-Rata, B., & Rodriguez-Duarte, A. (2019). Use patterns of educational videos: a quantitative study among university students. WPOM-Working Papers on Operations Management, 10(2), 1-19. https://doi.org/10.4995/wpom.v10i2.12625Bonk, C. J., & Graham, C. R. (2012). The handbook of blended learning: Global perspectives, local designs. John Wiley & Sons.Clark, T., & Barbour, M. K. (2015). Online, Blended, and Distance Education: Building Successful School Programs.Dhawan, S. (2020). Online learning: A panacea in the time of COVID-19 crisis. Journal of Educational Technology Systems, 49(1), 5-22. https://doi.org/10.1177/0047239520934018Garcia-Ortega, B., & Galan-Cubillo, J., (2021). Combining teamwork, coaching and mentoring as an innovative mix for self-aware and motivational learning. Imlementation case in teamwork sessions in the context of practices in a bachelor's degree. 15th Annual International Technology, Educationa and Development Conference. Valencia. Spain. https://doi.org/10.21125/inted.2021.2219Garrison, D. R., & Vaughan, N. D. (2008). Blended learning in higher education: Framework, principles, and guidelines. John Wiley & Sons. https://doi.org/10.1002/9781118269558Ginns, P., & Ellis, R. A. (2009). Evaluating the quality of e‐learning at the degree level in the student experience of blended learning. British Journal of Educational Technology, 40(4), 652-663. https://doi.org/10.1111/j.1467-8535.2008.00861.xGómez, W. A. R. (2014). Preguntas abiertas en encuestas ¿cómo realizar su análisis?. Comunicaciones en estadística, 7(2). https://doi.org/10.15332/s2027-3355.2014.0002.02Grasso, L. (2006). Encuestas. Elementos para su diseño y análisis. Editorial Brujas.Gros, B., & García-Peñalvo, F. J. (2016). Future trends in the design strategies and technological affordances of e-learning. Springer. https://doi.org/10.1007/978-3-319-17727-4_67-1Halverson, L. R., & Graham, C. R. (2019). Learner engagement in blended learning environments: A conceptual framework. Online Learning, 23(2), 145-178. https://doi.org/10.24059/olj.v23i2.1481Hong, J. C., Tai, K. H., Hwang, M. Y., Kuo, Y. C., & Chen, J. S. (2017). Internet cognitive failure relevant to users' satisfaction with content and interface design to reflect continuance intention to use a government e-learning system. Computers in Human Behavior, 66, 353-362. https://doi.org/10.1016/j.chb.2016.08.044López-Pérez, M. V., Pérez-López, M. C., & Rodríguez-Ariza, L. (2011). Blended learning in higher education: Students' perceptions and their relation to outcomes. Computers & education, 56(3), 818-826. https://doi.org/10.1016/j.compedu.2010.10.023Means, B., Toyama, Y., Murphy, R., & Baki, M. (2013). The effectiveness of online and blended learning: A meta-analysis of the empirical literature. Teachers College Record, 115(3), 1-47. https://doi.org/10.1177/016146811311500307McGEE, E., & Poojary, P. (2020). Exploring Blended Learning Relationships in Higher Education Using a Systems-based Framework. Turkish Online Journal of Distance Education, 21(4), 1-13. https://doi.org/10.17718/tojde.803343Kemp, N. (2020). University students' perceived effort and learning in face-to-face and online classes. Journal of Applied Learning and Teaching, 3(1), 69-77. https://doi.org/10.37074/jalt.2020.3.s1.14Krause, K. (2007) "Griffith University blended learning strategy," Document number2008/0016252, 2007.Norberg, A., Dziuban, C. D., & Moskal, P. D. (2011). A time‐based blended learning model. On the Horizon. https://doi.org/10.1108/10748121111163913Poon, J. (2013). Blended learning: An institutional approach for enhancing students' learning experiences. Journal of online learning and teaching, 9(2), 271-288.Rafiola, R., Setyosari, P., Radjah, C., & Ramli, M. (2020). The Effect of Learning Motivation, Self-Efficacy, and Blended Learning on Students' Achievement in The Industrial Revolution 4.0. International Journal of Emerging Technologies in Learning (iJET), 15(8), 71-82. https://doi.org/10.3991/ijet.v15i08.12525Rovai, A. P., & Downey, J. R. (2010). Why some distance education programs fail while others succeed in a global environment. The Internet and Higher Education, 13(3), 141-147. https://doi.org/10.1016/j.iheduc.2009.07.001Rovai, A. P., & Jordan, H. M. (2004). Blended learning and sense of community: A comparative analysis with traditional and fully online graduate courses. International Review of Research in Open and Distributed Learning, 5(2), 1-13. https://doi.org/10.19173/irrodl.v5i2.192Sayed, M. (2013). Blended learning environments: The effectiveness in developing concepts and thinking skills. Journal of Education and Practice, 4(25), 12-17.Stein, J., & Graham, C. R. (2020). Essentials for blended learning: A standards-based guide. Routledge. https://doi.org/10.4324/9781351043991Tang, C. M., & Chaw, L. Y. (2016). Digital Literacy: A Prerequisite for Effective Learning in a Blended Learning Environment?. Electronic Journal of E-learning, 14(1), 54-65.Tseng, H., & Walsh, E. J. (2016). Blended vs. traditional course delivery: Comparing students' motivation, learning outcomes, and preferences. Quarterly Review of Distance Education, 17(1), 1-21.Volery, Thierry, and Deborah Lord. "Critical success factors in online education." International journal of educational management (2000). https://doi.org/10.1108/09513540010344731Woo, Y., & Reeves, T. C. (2007). Meaningful interaction in web-based learning: A social constructivist interpretation. The Internet and higher education, 10(1), 15-25. https://doi.org/10.1016/j.iheduc.2006.10.005Zhu, Y., Au, W., & Yates, G. (2016). University students' self-control and self-regulated learning in a blended course. Internet and Higher Education, 30, 54-62. https://doi.org/10.1016/j.iheduc.2016.04.00

Enhanced on-line signature verification based on skilled forgery detection using Sigma-LogNormal Features

Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. M. Gomez-Barrero, J. Galbally, J. Fierrez, and J. Ortega-Garcia, "Enhanced on-line signature verification based on skilled forgery detection using Sigma-LogNormal Features", in International Conference on Biometrics, ICB 2015, 501-506One of the biggest challenges in on-line signature verification is the detection of skilled forgeries. In this paper, we propose a novel scheme, based on the Kinematic Theory of rapid human movements and its associated Sigma LogNormal model, to improve the performance of on-line signature verification systems. The approach combines the high performance of DTW-based systems in verification tasks, with the high potential for skilled forgery detection of the Kinematic Theory of rapid human movements. Experiments were carried out on the publicly available BiosecurID multimodal database, comprising 400 subjects. Results show that the performance of the DTW-based system improves for both skilled and random forgeries.This work has been partially supported by project Bio- Shield (TEC2012-34881) from Spanish MINECO, BEAT (FP7-SEC-284989) from EU, Cátedra UAM-Telefónica, CECABANK, and grant RGPIN-915 from NSERC Canada. M. G.-B. is supported by a FPU Fellowship from Spanish MECD

A review of schemes for fingerprint image quality computation

Fingerprint image quality affects heavily the performance of fingerprint recognition systems. This paper reviews existing approaches for fingerprint image quality computation. We also implement, test and compare a selection of them using the MCYT database including 9000 fingerprint images. Experimental results show that most of the algorithms behave similarly.Comment: Published at 3rd COST-275 Workshop on Biometrics on the Internet. arXiv admin note: text overlap with arXiv:2111.0743

Keystroke Biometrics in Response to Fake News Propagation in a Global Pandemic

This work proposes and analyzes the use of keystroke biometrics for content de-anonymization. Fake news have become a powerful tool to manipulate public opinion, especially during major events. In particular, the massive spread of fake news during the COVID-19 pandemic has forced governments and companies to fight against missinformation. In this context, the ability to link multiple accounts or profiles that spread such malicious content on the Internet while hiding in anonymity would enable proactive identification and blacklisting. Behavioral biometrics can be powerful tools in this fight. In this work, we have analyzed how the latest advances in keystroke biometric recognition can help to link behavioral typing patterns in experiments involving 100,000 users and more than 1 million typed sequences. Our proposed system is based on Recurrent Neural Networks adapted to the context of content de-anonymization. Assuming the challenge to link the typed content of a target user in a pool of candidate profiles, our results show that keystroke recognition can be used to reduce the list of candidate profiles by more than 90%. In addition, when keystroke is combined with auxiliary data (such as location), our system achieves a Rank-1 identification performance equal to 52.6% and 10.9% for a background candidate list composed of 1K and 100K profiles, respectively.Comment: arXiv admin note: text overlap with arXiv:2004.0362

Introduction to Presentation Attacks in Signature Biometrics and Recent Advances

Applications based on biometric authentication have received a lot of interest in the last years due to the breathtaking results obtained using personal traits such as face or fingerprint. However, it is important not to forget that these biometric systems have to withstand different types of possible attacks. This chapter carries out an analysis of different Presentation Attack (PA) scenarios for on-line handwritten signature verification. The main contributions of this chapter are: i) an updated overview of representative methods for Presentation Attack Detection (PAD) in signature biometrics; ii) a description of the different levels of PAs existing in on-line signature verification regarding the amount of information available to the impostor, as well as the training, effort, and ability to perform the forgeries; and iii) an evaluation of the system performance in signature biometrics under different scenarios considering recent publicly available signature databases, DeepSignDB and SVC2021_EvalDB. This work is in line with recent efforts in the Common Criteria standardization community towards security evaluation of biometric systems.Comment: Chapter of the Handbook of Biometric Anti-Spoofing (Third Edition

Biometric Signature Verification Using Recurrent Neural Networks

“© 2017 IEEE.  Personal use of this material is permitted.  Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.”Architectures based on Recurrent Neural Networks (RNNs) have been successfully applied to many different tasks such as speech or handwriting recognition with state-of-The art results. The main contribution of this work is to analyse the feasibility of RNNs for on-line signature verification in real practical scenarios. We have considered a system based on Long Short-Term Memory (LSTM) with a Siamese architecture whose goal is to learn a similarity metric from pairs of signatures. For the experimental work, the BiosecurID database comprised of 400 users and 4 separated acquisition sessions are considered. Our proposed LSTM RNN system has outperformed the results of recent published works on the BiosecurID benchmark in figures ranging from 17.76% to 28.00% relative verification performance improvement for skilled forgeriesThis work has been supported by project TEC2015-70627-R MINECO/FEDER and by UAM-CecaBank Project. Ruben Tolosana is supported by a FPU Fellowship from Spanish MEC

DeepSign: Deep On-Line Signature Verification

Deep learning has become a breathtaking technology in the last years, overcoming traditional handcrafted approaches and even humans for many different tasks. However, in some tasks, such as the verification of handwritten signatures, the amount of publicly available data is scarce, what makes difficult to test the real limits of deep learning. In addition to the lack of public data, it is not easy to evaluate the improvements of novel proposed approaches as different databases and experimental protocols are usually considered. The main contributions of this study are: i) we provide an in-depth analysis of state-of-the-art deep learning approaches for on-line signature verification, ii) we present and describe the new DeepSignDB on-line handwritten signature biometric public database, iii) we propose a standard experimental protocol and benchmark to be used for the research community in order to perform a fair comparison of novel approaches with the state of the art, and iv) we adapt and evaluate our recent deep learning approach named Time-Aligned Recurrent Neural Networks (TA-RNNs) for the task of on-line handwritten signature verification. This approach combines the potential of Dynamic Time Warping and Recurrent Neural Networks to train more robust systems against forgeries. Our proposed TA-RNN system outperforms the state of the art, achieving results even below 2.0% EER when considering skilled forgery impostors and just one training signature per user