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DeepWriteSYN: On-line handwriting synthesis via deep short-term representations

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arXiv 2020年

作者： Tolosana, Ruben Delgado-Santos, Paula Perez-Uribe, Andres Vera-Rodriguez, Ruben Fierrez, Julian Morales, Aythami Biometrics and Data Pattern Analytics Lab Universidad Autonoma de Madrid Spain Switzerland

This study proposes DeepWriteSYN, a novel on-line handwriting synthesis approach via deep short-term representations. It comprises two modules: i) an optional and interchangeable temporal segmentation, which divides the handwriting into short-time segments consisting of individual or multiple concatenated strokes;and ii) the on-line synthesis of those short-time handwriting segments, which is based on a sequence-to-sequence Variational Autoencoder (VAE). The main advantages of the proposed approach are that the synthesis is carried out in short-time segments (that can run from a character fraction to full characters) and that the VAE can be trained on a configurable handwriting dataset. These two properties give a lot of flexibility to our synthesiser, e.g., as shown in our experiments, DeepWriteSYN can generate realistic handwriting variations of a given handwritten structure corresponding to the natural variation within a given population or a given subject. These two cases are developed experimentally for individual digits and handwriting signatures, respectively, achieving in both cases remarkable results. Also, we provide experimental results for the task of on-line signature verification showing the high potential of DeepWriteSYN to improve significantly one-shot learning scenarios. To the best of our knowledge, this is the first synthesis approach capable of generating realistic on-line handwriting in the short term (including handwritten signatures) via deep learning. This can be very useful as a module toward long-term realistic handwriting generation either completely synthetic or as natural variation of given handwriting samples. Copyright © 2020, The Authors. All rights reserved.

关键词： Deep learning

DeepFakes and beyond: A survey of face manipulation and fake detection

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arXiv 2020年

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

The free access to large-scale public databases, together with the fast progress of deep learning techniques, in particular Generative Adversarial Networks, have led to the generation of very realistic fake contents with its corresponding implications towards society in this era of fake news. This survey provides a thorough review of techniques for manipulating face images including DeepFake methods, and methods to detect such manipulations. In particular, four types of facial manipulation are reviewed: i) entire face synthesis, ii) face identity swap (DeepFakes), iii) facial attributes manipulation, and iv) facial expression manipulation. For each manipulation type, we provide details regarding manipulation techniques, existing public databases, and key benchmarks for technology evaluation of fake detection methods, including a summary of results from those evaluations. Among the different databases available and discussed in the survey, FaceForensics++ is for example one of the most widely used for detecting both face identity swap and facial expression manipulations, with results in the literature in the range of 90-100% of manipulation detection accuracy. In addition to the survey information, we also discuss trends and provide an outlook of the ongoing work in this field, e.g., the recently announced DeepFake Detection Challenge (DFDC). Copyright © 2020, The Authors. All rights reserved.

关键词： Face recognition

mEBAL: A Multimodal database for Eye Blink Detection and Attention Level Estimation

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arXiv 2020年

作者： Daza, Roberto Morales, Aythami Fierrez, Julian Tolosana, Ruben Biometrics and Data Pattern Analytics BiDA-Lab Universidad Autonoma de Madrid Spain

ECG Biometric Recognition: Review, System Proposal, and Benchmark Evaluation

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arXiv 2022年

作者： Melzi, Pietro Tolosana, Ruben Vera-Rodriguez, Ruben Biometrics and Data Pattern Analytics - BiDA Lab Universidad Autonoma de Madrid Spain

Electrocardiograms (ECGs) have shown unique patterns to distinguish between different subjects and present important advantages compared to other biometric traits, such as difficulty to counterfeit, liveness detection, and ubiquity. Also, with the success of Deep Learning technologies, ECG biometric recognition has received increasing interest in recent years. However, it is not easy to evaluate the improvements of novel ECG proposed methods, mainly due to the lack of public data and standard experimental protocols. In this study, we perform extensive analysis and comparison of different scenarios in ECG biometric recognition. Both verification and identification tasks are investigated, as well as single- and multi-session scenarios. Finally, we also perform single- and multi-lead ECG experiments, considering traditional scenarios using electrodes in the chest and limbs and current user-friendly wearable devices. In addition, we present ECGXtractor, a robust Deep Learning technology trained with an in-house large-scale database and able to operate successfully across various scenarios and multiple databases. We introduce our proposed feature extractor, trained with multiple sinus-rhythm heartbeats belonging to 55,967 subjects, and provide a general public benchmark evaluation with detailed experimental protocol. We evaluate the system performance over four different databases: i) our in-house database, ii) PTB, iii) ECG-ID, and iv) CYBHi. With the widely used PTB database, we achieve Equal Error Rates of 0.14% and 2.06% in verification, and accuracies of 100% and 96.46% in identification, respectively in single- and multi-session analysis. We release the source code, experimental protocol details, and pre-trained models in GitHub to advance in the field. © 2022, CC BY-NC-ND.

关键词： biometrics

SaFL: Sybil-aware Federated Learning with Application to Face Recognition

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arXiv 2023年

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

关键词： Federated learning

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

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

Is my data in your AI Model? Membership Inference Test with Application to Face Images

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arXiv 2024年

作者： DeAlcala, Daniel Morales, Aythami Fierrez, Julian Mancera, Gonzalo Tolosana, Ruben Ortega-Garcia, Javier The Biometrics and Data Pattern Analytics Lab Universidad Autonoma de Madrid Madrid28049 Spain

This article introduces the Membership Inference Test (MINT), a novel approach that aims to empirically assess if given data was used during the training of AI/ML models. Specifically, we propose two MINT architectures designed to learn the distinct activation patterns that emerge when an Audited Model is exposed to data used during its training process. These architectures are based on Multilayer Perceptrons (MLPs) and Convolutional Neural Networks (CNNs). The experimental framework focuses on the challenging task of Face Recognition, considering three state-of-the-art Face Recognition systems. Experiments are carried out using six publicly available databases, comprising over 22 million face images in total. Different experimental scenarios are considered depending on the context of the AI model to test. Our proposed MINT approach achieves promising results, with up to 90% accuracy, indicating the potential to recognize if an AI model has been trained with specific data. The proposed MINT approach can serve to enforce privacy and fairness in several AI applications, e.g., revealing if sensitive or private data was used for training or tuning Large Language Models (LLMs). © 2024, CC BY-NC-ND.

关键词： Convolutional neural networks

Combining Blockchain and biometrics: A Survey on Technical Aspects and a First Legal Analysis

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arXiv 2023年

作者： Ghafourian, Mahdi Sumer, Bilgesu Vera-Rodriguez, Ruben Fierrez, Julian Tolosana, Ruben Moralez, Aythami Kindt, Els Biometrics and Data Pattern Analytics Lab Universidad Autonoma de Madrid Spain KU Leuven Belgium

Biometric recognition as a unique, hard-to-forge, and efficient way of identification and verification has become an indispensable part of the current digital world. The fast evolution of this technology has been a strong incentive for integrating it into many applications. Meanwhile, blockchain, the very attractive decentralized ledger technology, has been widely received both by the research and industry in the past years and it is being increasingly deployed nowadays in many different applications, such as money transfer, IoT, healthcare, or logistics. Recently, researchers have started to speculate what would be the pros and cons and what would be the best applications when these two technologies cross paths. This paper provides a survey of technical literature research on the combination of blockchain and biometrics and includes a first legal analysis of this integration to shed light on challenges and potentials. While this combination is still in its infancy and a growing body of literature discusses specific blockchain applications and solutions in an advanced technological set-up, this paper presents a holistic understanding of blockchain applicability in the biometric sector. This study demonstrates that combining blockchain and biometrics would be beneficial for novel applications in biometrics such as the PKI mechanism, distributed trusted service, and identity management. However, blockchain networks at their current stage are not efficient and economical for real-time applications. From a legal point of view, the allocation of accountability remains a main issue, while other difficulties remain, such as conducting a proper data Protection Impact Assessment. Finally, it supplies technical and legal recommendations to reap the benefits and mitigate the risks of the combination. © 2023, CC BY-NC-ND.

关键词： Blockchain