检索结果-内蒙古大学图书馆

Child-computer interaction: Recent works, new dataset, and age detection

学校读者我要写书评

暂无评论

arXiv 2021年

作者： Tolosana, Ruben Ruiz-Garcia, Juan Carlos Vera-Rodriguez, Ruben Herreros-Rodriguez, Jaime Romero-Tapiador, Sergio Morales, Aythami Fierrez, Julian Biometrics and Data Pattern Analytics - BiDA Lab Escuela Politecnica Superior Universidad Autonoma de Madrid Madrid28049 Spain Hospital Universitario Infanta Leonor Madrid28031 Spain

We overview recent research in Child-Computer Interaction and describe our framework ChildCI intended for: i) generating a better understanding of the cognitive and neuromotor development of children while interacting with mobile devices, and ii) enabling new applications in e-learning and e-health, among others. Our framework includes a new mobile application, specific data acquisition protocols, and a first release of the ChildCI dataset (ChildCIdb v1), which is planned to be extended yearly to enable longitudinal studies. In our framework children interact with a tablet device, using both a pen stylus and the finger, performing different tasks that require different levels of neuromotor and cognitive skills. ChildCIdb comprises more than 400 children from 18 months to 8 years old, considering therefore the first three development stages of the Piaget's theory. In addition, and as a demonstration of the potential of the ChildCI framework, we include experimental results for one of the many applications enabled by ChildCIdb: children age detection based on device interaction. Different machine learning approaches are evaluated, proposing a new set of 34 global features to automatically detect age groups, achieving accuracy results over 90% and interesting findings in terms of the type of features more useful for this task. © 2021, CC BY-NC-ND.

关键词： Computer vision

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

学校读者我要写书评

暂无评论

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

A comprehensive study on face recognition biases beyond demographics

学校读者我要写书评

暂无评论

arXiv 2021年

作者： Terhörst, Philipp Kolf, Jan Niklas Huber, Marco Kirchbuchner, Florian Damer, Naser Morales, Aythami Fierrez, Julian Kuijper, Arjan Fraunhofer Institute for Computer Graphics Research IGD Darmstadt Germany Technical University of Darmstadt Darmstadt Germany Biometrics and Data Pattern Analytics Lab - BiDA Lab Universidad Autonoma de Madrid Madrid 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 analyse 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 MAADFace 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 non-demographic attributes strongly affect the recognition performance, such as accessories, hair-styles and colors, face shapes, or facial anomalies. The observations of this work show the strong need for further advances in making FR system more robust, explainable, and fair. Moreover, our findings might help to a better understanding of how FR networks work, to enhance the robustness of these networks, and to develop more generalized bias-mitigating face recognition solutions. © 2021, CC BY.

关键词： Face recognition

DeepSignCX: Signature complexity detection using recurrent neural networks 15

学校读者我要写书评

暂无评论

DeepSignCX: Signature complexity detection using recurrent n...

15th IAPR International Conference on Document Analysis and Recognition, ICDAR 2019

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

ISBN: (纸本)9781728128610

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. © 2019 IEEE.

关键词： biometrics

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

学校读者我要写书评

暂无评论

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

IEEE Bigdata 2023 Keystroke Verification Challenge (KVC)

学校读者我要写书评

暂无评论

IEEE BigData 2023 Keystroke Verification Challenge (KVC)

IEEE International Conference on Big data

作者： Giuseppe Stragapede Ruben Vera-Rodriguez Ruben Tolosana Aythami Morales Ivan DeAndres-Tame Naser Damer Julian Fierrez Javier-Ortega Garcia Nahuel Gonzalez Andrei Shadrikov Dmitrii Gordin Leon Schmitt Daniel Wimmer Christoph Großmann Joerdis Krieger Florian Heinz Ron Krestel Christoffer Mayer Simon Haberl Helena Gschrey Yosuke Yamagishi Sanjay Saha Sanka Rasnayaka Sandareka Wickramanayake Terence Sim Weronika Gutfeter Adam Baran Mateusz Krzysztón Przemysław Jaskóła Biometrics and Data Pattern Analytics (BiDA) Lab Universidad Autonoma de Madrid Spain Fraunhofer Institute for Computer Graphics Research IGD Darmstadt Germany Laboratorio de Sistemas de Información Avanzados (LSIA) Universidad de Buenos Aires Argentina Verigram LLC Singapore Citix Almaty Kazakhstan Regensburg University Of Applied Sciences Germany Division of Radiology and Biomedical Engineering The University of Tokyo Japan National University of Singapore Singapore University of Moratuwa Sri Lanka NASK – National Research Warsaw Poland

Institute, Warsaw, Poland This paper describes the results of the IEEE Bigdata 2023 Keystroke Verification Challenge 1 (KVC), that considers the biometric verification performance of Keystroke Dynamics (KD), captured as tweet-long sequences of variable transcript text from over 185,000 subjects. The data are obtained from two of the largest public databases of KD up to date, the Aalto Desktop and Mobile Keystroke databases, guaranteeing a minimum amount of data per subject, age and gender annotations, absence of corrupted data, and avoiding excessively unbalanced subject distributions with respect to the considered demographic attributes. Several neural architectures were proposed by the participants, leading to global Equal Error Rates (EERs) as low as 3.33% and 3.61% achieved by the best team respectively in the desktop and mobile scenario, outperforming the current state of the art biometric verification performance for KD. Hosted on Codalab 2 , the KVC will be made ongoing to represent a useful tool for the research community to compare different approaches under the same experimental conditions and to deepen the knowledge of the field.

关键词：

Do You Need More data? The DeepSignDB On-Line Handwritten Signature biometric database

学校读者我要写书评

暂无评论

Do You Need More Data? The DeepSignDB On-Line Handwritten Si...

International Conference on Document Analysis and Recognition

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

关键词： databases Forgery Writing Training Protocols Task analysis Deep learning

DeepSignCX: Signature Complexity Detection using Recurrent Neural Networks

学校读者我要写书评

暂无评论

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

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

SensitiveNets: Learning agnostic representations with application to face recognition

学校读者我要写书评

暂无评论

arXiv 2019年

作者： Morales, Aythami Fierrez, Julian Vera-Rodriguez, Ruben Biometric and Data Pattern Analytics Lab-BiDA Escuela Politecnica Superior Universi-dad Autónoma de Madrid Campus de Cantoblanco C/ Francisco Tomás y Valiente 11 Madrid28049 Spain

This work proposes a new neural network feature representation that help to leave out sensitive information in the decision-making process of pattern recognition and machine learning algorithms. The aim of this work is to develop a learning method capable to remove certain information from the feature space without drop of performance in a recognition task based on that feature space. Our work is in part motivated by the new international regulation for personal data protection, which forces data controllers to avoid discriminative hazards while managing sensitive data of users. Our method is based on a triplet loss learning generalization that introduces a sensitive information removal process. The method is evaluated on face recognition technologies using state-of-the-art algorithms and publicly available benchmarks. In addition, we present a new annotation dataset with balanced distribution between genders and ethnic origins. The dataset includes more than 120K images from 24K identities with variety of poses, image quality, facial expressions, and illumination. The experiments demonstrate that it is possible to reduce sensitive information such as gender or ethnicity in the feature representation while retaining competitive performance in a face recognition task. Copyright © 2019, The Authors. All rights reserved.

关键词： Face recognition