检索结果-内蒙古大学图书馆

Longitudinal Analysis and Quantitative Assessment of Child Development through Mobile Interaction

学校读者我要写书评

暂无评论

arXiv 2024年

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

This article provides a comprehensive overview of recent research in the area of Child-Computer Interaction (CCI). The main contributions of the present article are two-fold. First, we present a novel longitudinal CCI database named ChildCIdbLong1, which comprises over 600 children aged 18 months to 8 years old, acquired continuously over 4 academic years (2019-2023). As a result, ChildCIdbLong comprises over 12K test acquisitions over a tablet device. Different tests are considered in ChildCIdbLong, requiring different touch and stylus gestures, enabling the evaluation of praxical and cognitive skills such as attentional, visuo-spatial, and executive, among others. In addition to the ChildCIdbLong database, we propose a novel quantitative metric called Test Quality (Q), designed to measure the motor and cognitive development of children through their interaction with a tablet device. In order to provide a better comprehension of the proposed Q metric, popular percentile-based growth representations are introduced for each test, providing a two-dimensional space to compare children's development with respect to the typical age skills of the population. The results achieved in the present article highlight the potential of the novel ChildCIdbLong database in conjunction with the proposed Q metric to measure the motor and cognitive development of children as they grow up. The proposed framework could be very useful as an automatic tool to support child experts (e.g., paediatricians, educators, or neurologists) for early detection of potential physical/cognitive impairments during children's development. © 2024, CC BY-NC-ND.

关键词： Electronic health record

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

学校读者我要写书评

暂无评论

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%.

关键词：

BioTouchPass: Handwritten Passwords for Touchscreen biometrics

学校读者我要写书评

暂无评论

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

Introduction to Presentation Attack Detection in Fingerprint biometrics

学校读者我要写书评

暂无评论

arXiv 2023年

作者： Galbally, Javier Fierrez, Julian Cappelli, Raffaele Marcialis, Gian Luca European Commission Joint Research Centre Ispra Italy Biometrics and Data Pattern Analytics - BiDA Lab Universidad Autonoma de Madrid Madrid Spain Università di Bologna Cesena Italy Università di Cagliari Italy

This chapter provides an introduction to Presentation Attack Detection (PAD) in fingerprint biometrics, also coined anti-spoofing, describes early developments in this field, and briefly summarizes recent trends and o... 详细信息

关键词： Palmprint recognition

ChildCI Framework: Analysis of Motor and Cognitive Development in Children-Computer Interaction for Age Detection

学校读者我要写书评

暂无评论

arXiv 2022年

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

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. In particular, we propose a set of over 100 global features related to motor and cognitive aspects of the children interaction with mobile devices, some of them collected and adapted from the literature. Furthermore, we analyse the robustness and discriminative power of the proposed feature set including experimental results for the task of children age group detection based on their motor and cognitive behaviours. Two different scenarios are considered in this study: i) single-test scenario, and ii) multiple-test scenario. Results over 93% accuracy are achieved using the publicly available ChildCIdb_v1 database (over 400 children from 18 months to 8 years old), proving the high correlation of children's age with the way they interact with mobile devices. © 2022, CC BY-NC-ND.

关键词：

DeepFakes evolution: Analysis of facial regions and fake detection performance

学校读者我要写书评

暂无评论

arXiv 2020年

作者： Tolosana, Ruben Romero-Tapiador, Sergio Fierrez, Julian Vera-Rodriguez, Ruben Biometrics and Data Pattern Analytics - BiDA Lab Universidad Autonoma de Madrid

Media forensics has attracted a lot of attention in the last years in part due to the increasing concerns around DeepFakes. Since the initial DeepFake databases from the 1stgeneration such as UADFV and FaceForensics++ up to the latest databases of the 2ndgeneration such as Celeb-DF and DFDC, many visual improvements have been carried out, making fake videos almost indistinguishable to the human eye. This study provides an exhaustive analysis of both 1stand 2ndDeep-Fake generations in terms of facial regions and fake detection performance. Two different approaches are considered in our experimental framework: i) selecting the entire face as input to the fake detection system, and ii) selecting specific facial regions such as the eyes or nose, among others, as input to the fake detection system. Among all the findings resulting from our experiments, we highlight the poor fake detection results achieved even by the strongest state-of-the-art fake detectors in the latest DeepFake databases of the 2ndgeneration, with Equal Error Rate results ranging from 15% to 30%. These results remark the necessity of further research in order to develop more sophisticated fake detection systems. Copyright © 2020, The Authors. All rights reserved.

关键词： Fake detection

DeepSign: Deep on-line signature verification

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 Universidad Autonoma de Madrid

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 database1, 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)2 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. Copyright © 2020, The Authors. All rights reserved.

关键词： biometrics

SELM: Siamese extreme learning machine with application to face biometrics

学校读者我要写书评

暂无评论

arXiv 2021年

作者： Kudisthalert, Wasu Pasupa, Kitsuchart Morales, Aythami Fierrez, Julian Faculty of Information Technology King Mongkut's Institute of Technology Ladkrabang Bangkok10520 Thailand Biometric and Data Pattern Analytics Lab Universidad Autonoma de Madrid Spain

Extreme Learning Machine is a powerful classification method very competitive existing classification methods. It is extremely fast at training. Nevertheless, it cannot perform face verification tasks properly because face verification tasks require comparison of facial images of two individuals at the same time and decide whether the two faces identify the same person. The structure of Extreme Leaning Machine was not designed to feed two input data streams simultaneously, thus, in 2-input scenarios Extreme Learning Machine methods are normally applied using concatenated inputs. However, this setup consumes two times more computational resources and it is not optimized for recognition tasks where learning a separable distance metric is critical. For these reasons, we propose and develop a Siamese Extreme Learning Machine (SELM). SELM was designed to be fed with two data streams in parallel simultaneously. It utilizes a dual-stream Siamese condition in the extra Siamese layer to transform the data before passing it along to the hidden layer. Moreover, we propose a Gender-Ethnicity-Dependent triplet feature exclusively trained on a variety of specific demographic groups. This feature enables learning and extracting of useful facial features of each group. Experiments were conducted to evaluate and compare the performances of SELM, Extreme Learning Machine, and DCNN. The experimental results showed that the proposed feature was able to perform correct classification at 97.87% accuracy and 99.45% AUC. They also showed that using SELM in conjunction with the proposed feature provided 98.31% accuracy and 99.72% AUC. They outperformed the well-known DCNN and Extreme Leaning Machine methods by a wide margin. Copyright © 2021, The Authors. All rights reserved.

关键词： Face recognition

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

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 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