This work introduces a new multispectral database and novel approaches for eyeblink detection in RGB and Near-Infrared (NIR) individual images.
1 code implementation • 12 Sep 2023 • Sergio Romero-Tapiador, Ruben Tolosana, Aythami Morales, Isabel Espinosa-Salinas, Gala Freixer, Julian Fierrez, Ruben Vera-Rodriguez, Enrique Carrillo de Santa Pau, Ana Ramírez de Molina, Javier Ortega-Garcia
In addition to the framework, we also provide and describe a unique food image dataset that includes 4, 800 different weekly eating behaviours from 15 different profiles and 1, 200 subjects.
The application of mobile biometrics as a user-friendly authentication method has increased in the last years.
Every day, thousands of digital documents are generated with useful information for companies, public organizations, and citizens.
The analysis of public affairs documents is crucial for citizens as it promotes transparency, accountability, and informed decision-making.
In this article, we present a Web-based System called M2LADS, which supports the integration and visualization of multimodal data recorded in learning sessions in a MOOC in the form of Web-based Dashboards.
Over the past years, deep learning capabilities and the availability of large-scale training datasets advanced rapidly, leading to breakthroughs in face recognition accuracy.
This risk assessment should address, among others, the detection and mitigation of bias in AI.
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.
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.
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.
With the aim of studying how current multimodal algorithms based on heterogeneous sources of information are affected by sensitive elements and inner biases in the data, we propose a fictitious case study focused on automated recruitment: FairCVtest.
The remarkable success of face recognition (FR) has endangered the privacy of internet users particularly in social media.
This work presents a new multimodal system for remote attention level estimation based on multimodal face analysis.
Periocular refers to the externally visible region of the face that surrounds the eye socket.
We compare our fusion approach to a set of rule-based fusion schemes over normalized scores.
This is also supported by numerous experiments involving a simpler quality estimator, a trained method (NFIQ), as well as the human perception of fingerprint quality on several public databases.
We present edBB-Demo, a demonstrator of an AI-powered research platform for student monitoring in remote education.
1 code implementation • 14 Nov 2022 • Sergio Romero-Tapiador, Ruben Tolosana, Aythami Morales, Isabel Espinosa-Salinas, Gala Freixer, Julian Fierrez, Ruben Vera-Rodriguez, Enrique Carrillo de Santa Pau, Ana Ramírez de Molina, Javier Ortega-Garcia
This study presents the AI4Food-NutritionDB database, the first nutrition database that considers food images and a nutrition taxonomy based on recommendations by national and international organisms.
The role of soft biometrics to enhance person recognition systems in unconstrained scenarios has not been extensively studied.
1 code implementation • 6 Oct 2022 • Giuseppe Stragapede, Ruben Vera-Rodriguez, Ruben Tolosana, Aythami Morales, Julian Fierrez, Javier Ortega-Garcia, Sanka Rasnayaka, Sachith Seneviratne, Vipula Dissanayake, Jonathan Liebers, Ashhadul Islam, Samir Brahim Belhaouari, Sumaiya Ahmad, Suraiya Jabin
This paper describes the experimental framework and results of the IJCB 2022 Mobile Behavioral Biometrics Competition (MobileB2C).
Different bot detectors are considered based on several supervised classifiers (Support Vector Machine, Random Forest, Gaussian Naive Bayes and a Long Short-Term Memory network) and a learning framework including human and synthetic samples.
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.
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.
Such techniques are designed to restore generic images and therefore do not exploit the specific structure found in biometric images (e. g. iris or faces), which causes the solution to be sub-optimal.
This article presents a comprehensive analysis of the different tests proposed in the recent ChildCI framework, proving its potential for generating a better understanding of children's neuromotor and cognitive development along time, as well as their possible application in other research areas such as e-Health and e-Learning.
Background- This paper summarizes the state-of-the-art and applications based on online handwritting signals with special emphasis on e-security and e-health fields.
This comparison shows that, even though FaceQgen does not surpass the best existing face quality assessment methods in terms of face recognition accuracy prediction, it achieves good enough results to demonstrate the potential of semi-supervised learning approaches for quality estimation (in particular, data-driven learning based on a single high quality image per subject), having the capacity to improve its performance in the future with adequate refinement of the model and the significant advantage over competing methods of not needing quality labels for its development.
The experimental framework is carried out using a public multimodal database for eye blink detection and attention level estimation called mEBAL, which comprises data from 38 students and multiples acquisition sensors, in particular, i) an electroencephalogram (EEG) band which provides the time signals coming from the student's cognitive information, and ii) RGB and NIR cameras to capture the students face gestures.
Cancelable biometrics refers to a group of techniques in which the biometric inputs are transformed intentionally using a key before processing or storage.
Iris recognition technology has attracted an increasing interest in the last decades in which we have witnessed a migration from research laboratories to real world applications.
The main scope of this chapter is to serve as an introduction to face presentation attack detection, including key resources and advances in the field in the last few years.
no code implementations • 17 Nov 2021 • Norman Poh, Thirimachos Bourlai, Josef Kittler, Lorene Allano, Fernando Alonso-Fernandez, Onkar Ambekar, John Baker, Bernadette Dorizzi, Omolara Fatukasi, Julian Fierrez, Harald Ganster, Javier Ortega-Garcia, Donald Maurer, Albert Ali Salah, Tobias Scheidat, Claus Vielhauer
The cost-sensitive evaluation, on the other hand, investigates how well a fusion algorithm can perform given restricted computation and in the presence of software and hardware failures, resulting in errors such as failure-to-acquire and failure-to-match.
Biometric technology has been increasingly deployed in the past decade, offering greater security and convenience than traditional methods of personal recognition.
no code implementations • 17 Nov 2021 • Javier Ortega-Garcia, Julian Fierrez, Fernando Alonso-Fernandez, Javier Galbally, Manuel R Freire, Joaquin Gonzalez-Rodriguez, Carmen Garcia-Mateo, Jose-Luis Alba-Castro, Elisardo Gonzalez-Agulla, Enrique Otero-Muras, Sonia Garcia-Salicetti, Lorene Allano, Bao Ly-Van, Bernadette Dorizzi, Josef Kittler, Thirimachos Bourlai, Norman Poh, Farzin Deravi, Ming NR Ng, Michael Fairhurst, Jean Hennebert, Andreas Humm, Massimo Tistarelli, Linda Brodo, Jonas Richiardi, Andrezj Drygajlo, Harald Ganster, Federico M Sukno, Sri-Kaushik Pavani, Alejandro Frangi, Lale Akarun, Arman Savran
It is comprised of more than 600 individuals acquired simultaneously in three scenarios: 1) over the Internet, 2) in an office environment with desktop PC, and 3) in indoor/outdoor environments with mobile portable hardware.
One of the open issues in fingerprint verification is the lack of robustness against image-quality degradation.
In this paper we develop FaceQvec, a software component for estimating the conformity of facial images with each of the points contemplated in the ISO/IEC 19794-5, a quality standard that defines general quality guidelines for face images that would make them acceptable or unacceptable for use in official documents such as passports or ID cards.
A new software-based liveness detection approach using a novel fingerprint parameterization based on quality related features is proposed.
no code implementations • 2 Nov 2021 • Julian Fierrez, Javier Galbally, Javier Ortega-Garcia, Manuel R Freire, Fernando Alonso-Fernandez, Daniel Ramos, Doroteo Torre Toledano, Joaquin Gonzalez-Rodriguez, Juan A Siguenza, Javier Garrido-Salas, E Anguiano, Guillermo Gonzalez-de-Rivera, Ricardo Ribalda, Marcos Faundez-Zanuy, JA Ortega, Valentín Cardeñoso-Payo, A Viloria, Carlos E Vivaracho, Q Isaac Moro, Juan J Igarza, J Sanchez, Inmaculada Hernaez, Carlos Orrite-Urunuela, Francisco Martinez-Contreras, Juan José Gracia-Roche
A new multimodal biometric database, acquired in the framework of the BiosecurID project, is presented together with the description of the acquisition setup and protocol.
In this contribution, the vulnerabilities of iris-based recognition systems to direct attacks are studied.
This paper is the first to explore an automatic way to detect bias in deep convolutional neural networks by simply looking at their weights.
This work presents a new deep learning approach for keystroke biometrics based on a novel Distance Metric Learning method (DML).
1 code implementation • 13 Aug 2021 • Ruben Tolosana, Ruben Vera-Rodriguez, Carlos Gonzalez-Garcia, Julian Fierrez, Aythami Morales, Javier Ortega-Garcia, Juan Carlos Ruiz-Garcia, Sergio Romero-Tapiador, Santiago Rengifo, Miguel Caruana, Jiajia Jiang, Songxuan Lai, Lianwen Jin, Yecheng Zhu, Javier Galbally, Moises Diaz, Miguel Angel Ferrer, Marta Gomez-Barrero, Ilya Hodashinsky, Konstantin Sarin, Artem Slezkin, Marina Bardamova, Mikhail Svetlakov, Mohammad Saleem, Cintia Lia Szucs, Bence Kovari, Falk Pulsmeyer, Mohamad Wehbi, Dario Zanca, Sumaiya Ahmad, Sarthak Mishra, Suraiya Jabin
This article presents SVC-onGoing, an on-going competition for on-line signature verification where researchers can easily benchmark their systems against the state of the art in an open common platform using large-scale public databases, such as DeepSignDB and SVC2021_EvalDB, and standard experimental protocols.
For these reasons, we propose and develop a Siamese Extreme Learning Machine (SELM).
1 code implementation • 1 Jun 2021 • Ruben Tolosana, Ruben Vera-Rodriguez, Carlos Gonzalez-Garcia, Julian Fierrez, Santiago Rengifo, Aythami Morales, Javier Ortega-Garcia, Juan Carlos Ruiz-Garcia, Sergio Romero-Tapiador, Jiajia Jiang, Songxuan Lai, Lianwen Jin, Yecheng Zhu, Javier Galbally, Moises Diaz, Miguel Angel Ferrer, Marta Gomez-Barrero, Ilya Hodashinsky, Konstantin Sarin, Artem Slezkin, Marina Bardamova, Mikhail Svetlakov, Mohammad Saleem, Cintia Lia Szücs, Bence Kovari, Falk Pulsmeyer, Mohamad Wehbi, Dario Zanca, Sumaiya Ahmad, Sarthak Mishra, Suraiya Jabin
This paper describes the experimental framework and results of the ICDAR 2021 Competition on On-Line Signature Verification (SVC 2021).
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.
This article provides an overview of recent research in Child-Computer Interaction with mobile devices and describe our framework ChildCI intended for: i) overcoming the lack of large-scale publicly available databases in the area, ii) generating a better understanding of the cognitive and neuromotor development of children along time, contrary to most previous studies in the literature focused on a single-session acquisition, and iii) enabling new applications in e-Learning and e-Health through the acquisition of additional information such as the school grades and children's disorders, among others.
We study the performance of Long Short-Term Memory networks for keystroke biometric authentication at large scale in free-text scenarios.
The principal contributions of this work are: (1) a novel framework to exploit deep face architectures to model hypomimia in PD patients; (2) we experimentally compare PD detection based on single images vs. image sequences while the patients are evoked various face expressions; (3) we explore different domain adaptation techniques to exploit existing models initially trained either for Face Recognition or to detect FAUs for the automatic discrimination between PD patients and healthy subjects; and (4) a new approach to use triplet-loss learning to improve hypomimia modeling and PD detection.
Machine learning methods are growing in relevance for biometrics and personal information processing in domains such as forensics, e-health, recruitment, and e-learning.
This work explores facial expression bias as a security vulnerability of face recognition systems.
In the last five years, deep learning methods, in particular CNN, have attracted considerable attention in the field of face-based recognition, achieving impressive results.
This work introduces a novel DeepFake detection framework based on physiological measurement.
We propose two face representations that are blind to facial expressions associated to emotional responses.
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.
With the aim of studying how current multimodal AI algorithms based on heterogeneous sources of information are affected by sensitive elements and inner biases in the data, this demonstrator experiments over an automated recruitment testbed based on Curriculum Vitae: FairCVtest.
The performance of face analysis and recognition systems depends on the quality of the acquired face data, which is influenced by numerous factors.
In this paper, we investigate how to detect intruders with low latency for Active Authentication (AA) systems with multiple-users.
This work presents mEBAL, a multimodal database for eye blink detection and attention level estimation.
After a gentle introduction to the general topic of biometric quality and a review of past efforts in face quality metrics, in the present work, we address the need for better face quality metrics by developing FaceQnet.
In this study we estimate the heart rate from face videos for student assessment.
This paper presents a comparative evaluation of methods for remote heart rate estimation using face videos, i. e., given a video sequence of the face as input, methods to process it to obtain a robust estimation of the subjects heart rate at each moment.
Behavioral biometrics can be powerful tools in this fight.
We propose two new mouse trajectory synthesis methods for generating realistic data: a) a function-based method based on heuristic functions, and b) a data-driven method based on Generative Adversarial Networks (GANs) in which a Generator synthesizes human-like trajectories from a Gaussian noise input.
We propose a discrimination-aware learning method to improve both accuracy and fairness of biased face recognition algorithms.
Media forensics has attracted a lot of attention in the last years in part due to the increasing concerns around DeepFakes.
With the aim of studying how current multimodal algorithms based on heterogeneous sources of information are affected by sensitive elements and inner biases in the data, we propose a fictitious automated recruitment testbed: FairCVtest.
We analyze how bias affects deep learning processes through a toy example using the MNIST database and a case study in gender detection from face images.
Blockchain technologies provide excellent architectures and practical tools for securing and managing the sensitive and private data stored in biometric templates, but at a cost.
Deep learning has become a breathtaking technology in the last years, overcoming traditional handcrafted approaches and even humans for many different tasks.
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).
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 content with its corresponding implications towards society in this era of fake news.
We present a platform for student monitoring in remote education consisting of a collection of sensors and software that capture biometric and behavioral data.
We experimentally show that demographic groups highly represented in popular face databases have led to popular pre-trained deep face models presenting strong algorithmic discrimination.
The availability of large-scale facial databases, together with the remarkable progresses of deep learning technologies, in particular Generative Adversarial Networks (GANs), have led to the generation of extremely realistic fake facial content, raising obvious concerns about the potential for misuse.
In this paper we develop a robust for heart rate (HR) estimation method using face video for challenging scenarios with high variability sources such as head movement, illumination changes, vibration, blur, etc.
We explore practical tradeoffs in blockchain-based biometric template storage.
Cryptography and Security
Several conclusions can be drawn from this work, most notably: 1) we managed to employ an existing ICAO compliance framework and a pretrained CNN to automatically label data with quality information, 2) we trained FaceQnet for quality estimation by fine-tuning a pre-trained face recognition network (ResNet-50), and 3) we have shown that the predictions from FaceQnet are highly correlated with the face recognition accuracy of a state-of-the-art commercial system not used during development.
Experiments are also reported with a database of VIS images from different smartphones.
Our method is based on an adversarial regularizer that introduces a sensitive information removal function in the learning objective.
In this paper we evaluate mobile active authentication based on an ensemble of biometrics and behavior-based profiling signals.
We report significant improvements in the rank identification accuracies when these minutiae matchers are augmented with our proposed algorithm based on rare minutiae features.