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DeepSignCX: Signature Complexity Detection using Recurrent Neural Networks

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DeepSignCX: Signature Complexity Detection using Recurrent N...

International Conference on Document Analysis and Recognition

作者： Ruben Vera-Rodriguez Ruben Tolosana Miguel Caruana Gustavo Manzano Carlos Gonzalez-Garcia Julian Fierrez Javier Ortega-Garcia Biometrics and Data Pattern Analytics - BiDA Lab Universidad Autonoma de Madrid Madrid Spain

This paper proposes a novel approach for on-line signature complexity detection based on Recurrent Neural Networks (RNNs). Complexity of handwritten signatures can vary from very simple ones (just a simple flourish) to very complex signatures (including the handwritten full name and complex flourish). Three different complexity levels are proposed: low, medium, and high. Time functions are extracted from the on-line signatures and a system based on RNNs (BLSTM in particular) is trained to classify the three levels of complexity over a ground truth manually labelled database (BiosecurID with 400 subjects). This initial model is used to automatically label a very large database (DeepSignDB) containing over 1500 subjects, which is then used to train the proposed RNN for signature complexity detection. Promising results ca. 85% of accuracy are achieved. This complexity detector could be used as a first stage in a signature verification system in order to train a specific biometric system per signature complexity level and improve the overall system performance.

关键词： Complexity theory databases Biological system modeling Detectors Manuals Handwriting recognition Training

A Comprehensive Study on Face Recognition Biases beyond Demographics

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IEEE Transactions on Technology and Society

IEEE Transactions on Technology and Society 2022年第1期3卷 16-30页

作者： Terhorst, Philipp Kolf, Jan Niklas Huber, Marco Kirchbuchner, Florian Damer, Naser Moreno, Aythami Morales Fierrez, Julian Kuijper, Arjan Department of Smart Living & Biometric Technologies Fraunhofer Institute for Computer Graphics Research IGD Darmstadt64283 Germany Interactive Graphics Systems Group Technical University of Darmstadt Darmstadt64289 Germany Biometrics and Data Pattern Analytics Lab-BiDA Lab Universidad Autonoma de Madrid Madrid28049 Spain

Face recognition (FR) systems have a growing effect on critical decision-making processes. Recent works have shown that FR solutions show strong performance differences based on the user's demographics. However, to enable a trustworthy FR technology, it is essential to know the influence of an extended range of facial attributes on FR beyond demographics. Therefore, in this work, we analyze FR bias over a wide range of attributes. We investigate the influence of 47 attributes on the verification performance of two popular FR models. The experiments were performed on the publicly available MAAD-Face attribute database with over 120M high-quality attribute annotations. To prevent misleading statements about biased performances, we introduced control group-based validity values to decide if unbalanced test data causes the performance differences. The results demonstrate that also many nondemographic attributes strongly affect recognition performance, such as accessories, hairstyles and colors, face shapes, or facial anomalies. The observations of this work show the strong need for further advances in making the FR system more robust, explainable, and fair. Moreover, our findings might help to a better understanding of how FR networks work, enhance the robustness of these networks, and develop more generalized bias-mitigating FR solutions. © 2022 IEEE.

关键词： biometrics

OTB-morph: One-Time biometrics via Morphing

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arXiv 2023年

作者： Ghafourian, Mahdi Fierrez, Julian Vera-Rodriguez, Ruben Morales, Aythami Serna, Ignacio Biometrics and Data Pattern Analytics Lab BiDA Lab Universidad Autonoma de Madrid Madrid28049 Spain

Cancelable biometrics are a group of techniques to transform the input biometric to an irreversible feature intentionally using a transformation function and usually a key in order to provide security and privacy in biometric recognition systems. This transformation is repeatable enabling subsequent biometric comparisons. This paper is introducing a new idea to exploit as a transformation function for cancelable biometrics aimed at protecting the templates against iterative optimization attacks. Our proposed scheme is based on time-varying keys (random biometrics in our case) and morphing transformations. An experimental implementation of the proposed scheme is given for face biometrics. The results confirm that the proposed approach is able to withstand against leakage attacks while improving the recognition performance. © 2023, CC BY-NC-ND.

关键词： biometrics

BioTouchPass: Handwritten Passwords for Touchscreen biometrics

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arXiv 2022年

作者： Tolosana, Ruben Vera-Rodriguez, Ruben Fierrez, Julian Biometrics and Data Pattern Analytics - BiDA Lab Escuela Politecnica Superior Universidad Autonoma de Madrid Madrid28049 Spain

This work enhances traditional authentication systems based on Personal Identification Numbers (PIN) and OneTime Passwords (OTP) through the incorporation of biometric information as a second level of user authentication. In our proposed approach, users draw each digit of the password on the touchscreen of the device instead of typing them as usual. A complete analysis of our proposed biometric system is carried out regarding the discriminative power of each handwritten digit and the robustness when increasing the length of the password and the number of enrolment samples. The new e-BioDigit database, which comprises on-line handwritten digits from 0 to 9, has been acquired using the finger as input on a mobile device. This database is used in the experiments reported in this work and it is available together with benchmark results in GitHub1. Finally, we discuss specific details for the deployment of our proposed approach on current PIN and OTP systems, achieving results with Equal Error Rates (EERs) ca. 4.0% when the attacker knows the password. These results encourage the deployment of our proposed approach in comparison to traditional PIN and OTP systems where the attack would have 100% success rate under the same impostor scenario. © 2022, CC BY-NC-ND.

关键词： Authentication

BioTouchPass2: Touchscreen password biometrics using time-aligned recurrent neural networks

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arXiv 2020年

作者： Tolosana, Ruben Vera-Rodriguez, Ruben Fierrez, Julian Ortega-Garcia, Javier Biometrics and Data Pattern Analytics BiDA Lab Escuela Politecnica Superior Universidad Autonoma de Madrid Madrid28049 Spain

Passwords are still used on a daily basis for all kind of applications. However, they are not secure enough by themselves in many cases. This work enhances password scenarios through two-factor authentication asking the users to draw each character of the password instead of typing them as usual. The main contributions of this study are as follows: i) We present the novel MobileTouchDB public database, acquired in an unsupervised mobile scenario with no restrictions in terms of position, posture, and devices. This database contains more than 64K on-line character samples performed by 217 users, with 94 different smartphone models, and up to 6 acquisition sessions. ii) We perform a complete analysis of the proposed approach considering both traditional authentication systems such as Dynamic Time Warping (DTW) and novel approaches based on Recurrent Neural Networks (RNNs). In addition, we present a novel approach named Time-Aligned Recurrent Neural Networks (TA-RNNs)1. This approach combines the potential of DTW and RNNs to train more robust systems against attacks. A complete analysis of the proposed approach is carried out using both MobileTouchDB2 and e-BioDigitDB3 databases. Our proposed TA-RNN system outperforms the state of the art, achieving a final 2.38% Equal Error Rate, using just a 4-digit password and one training sample per character. These results encourage the deployment of our proposed approach in comparison with traditional typed-based password systems where the attack would have 100% success rate under the same impostor scenario. Copyright © 2020, The Authors. All rights reserved.

关键词： Recurrent neural networks

Optimizing Key-Selection for Face-Based One-Time biometrics via Morphing

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Optimizing Key-Selection for Face-Based One-Time Biometrics ...

IEEE International Workshop on Information Forensics and Security (WIFS)

作者： Dailé Osorio-Roig Mahdi Ghafourian Christian Rathgeb Ruben Vera-Rodriguez Christoph Busch Julian Fierrez Biometrics and Internet Security Research Group Hochschule Darmstadt Germany Biometrics and Data Pattern Analytics (BiDA) Lab Universidad Autonoma de Madrid (UAM) Spain

Nowadays, facial recognition systems are still vulnerable to adversarial attacks. These attacks vary from simple perturbations of the input image to modifying the parameters of the recognition model to impersonate an authorised subject. So-called privacy-enhancing facial recognition systems have been mostly developed to provide protection of stored biometric reference data, i.e. templates. In the literature, privacy-enhancing facial recognition approaches have focused solely on conventional security threats at the template level, ignoring the growing concern related to adversarial attacks. Up to now, few works have provided mechanisms to protect face recognition against adversarial attacks while maintaining high security at the template level. In this paper, we propose different key selection strategies to improve the security of a competitive cancelable scheme operating at the signal level. Experimental results show that certain strategies based on signal-level key selection can lead to complete blocking of the adversarial attack based on an iterative optimization for the most secure threshold, while for the most practical threshold, the attack success chance can be decreased to approximately 5.0%.

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A Comprehensive Analysis of Factors Impacting Membership Inference

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A Comprehensive Analysis of Factors Impacting Membership Inf...

IEEE Computer Society Conference on Computer Vision and pattern Recognition Workshops (CVPRW)

作者： Daniel DeAlcala Gonzalo Mancera Aythami Morales Julian Fierrez Ruben Tolosana Javier Ortega-Garcia Biometrics and Data Pattern Analytics Lab Universidad Autonoma de Madrid Spain

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

We analyze various factors affecting the proper functioning of MIA and MINT, two research lines aimed at detecting data used for training. The difference between these lines lies in the environmental conditions, while the fundamental bases are similar for both. As evident in the literature, this detection task is far from straightforward and poses an ongoing challenge for the scientific community. Specifically, in this work, we conclude that factors such as the number of times data passes through the original network, the loss function, or dropout significantly impact detection outcomes. Therefore, it is crucial to consider them when developing these methods and during the training of any neural network, both to avoid (MIA) and to enhance (MINT) this detection. We evaluate the AdaFace facial recognition model using five databases with over 22 million images, modifying the different factors under analysis and defining a suitable protocol for their examination. State-of-the-art accuracy reaching up to 87% is achieved, surpassing existing methods.

关键词： Training Analytical models Accuracy Protocols databases Face recognition Biological system modeling

BeCAPTCHA-Mouse: Synthetic mouse trajectories and improved bot detection

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arXiv 2020年

作者： Acien, Alejandro Morales, Aythami Fierrez, Julian Vera-Rodriguez, Ruben Biometrics and Data Pattern Analytics Lab Universidad Autonoma de Madrid Spain

We first study the suitability of behavioral biometrics to distinguish between computers and humans, commonly named as bot detection. We then present BeCAPTCHA-Mouse, a bot detector based on neuromotor modeling of mouse dynamics that enhances traditional CAPTCHA methods. Our proposed bot detector is trained using both human and bot data generated by two new methods developed for generating realistic synthetic mouse trajectories: i) a knowledge-based method based on heuristic functions, and ii) a data-driven method based on Generative Adversarial Networks (GANs) in which a Generator synthesizes human-like trajectories from a Gaussian noise input. Experiments are conducted on a new testbed also introduced here and available in GitHub: BeCAPTCHA-Mouse Benchmark;useful for research in bot detection and other mouse-based HCI applications. Our benchmark data consists of 10,000 mouse trajectories including real data from 58 users and bot data with various levels of realism. Our experiments show that BeCAPTCHA-Mouse is able to detect bot trajectories of high realism with 93% of accuracy in average using only one mouse trajectory. When our approach is fused with state-of-the-art mouse dynamic features, the bot detection accuracy increases relatively by more than 36%, proving that mouse-based bot detection is a fast, easy, and reliable tool to complement traditional CAPTCHA systems. Copyright © 2020, The Authors. All rights reserved.

关键词： biometrics

Measuring Bias in AI Models: An Statistical Approach Introducing N-Sigma

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Measuring Bias in AI Models: An Statistical Approach Introdu...

IEEE Annual International Computer Software and Applications Conference (COMPSAC)

作者： Daniel DeAlcala Ignacio Serna Aythami Morales Julian Fierrez Javier Ortega-Garcia Biometrics and Data Pattern Analytics Lab Universidad Autonoma de Madrid Spain

The new regulatory framework proposal on Artificial Intelligence (AI) published by the European Commission establishes a new risk-based legal approach. The proposal highlights the need to develop adequate risk assessments for the different uses of AI. This risk assessment should address, among others, the detection and mitigation of bias in AI. In this work we analyze statistical approaches to measure biases in automatic decision-making systems. We focus our experiments in face recognition technologies. We propose a novel way to measure the biases in machine learning models using a statistical approach based on the N-Sigma method. N-Sigma is a popular statistical approach used to validate hypotheses in general science such as physics and social areas and its application to machine learning is yet unexplored. In this work we study how to apply this methodology to develop new risk assessment frameworks based on bias analysis and we discuss the main advantages and drawbacks with respect to other popular statistical tests.

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