31 research outputs found
Towards Predicting Good Users for Biometric Recognition Based on Keystroke Dynamics
The final publication is available at Springer via http://dx.doi.org/10.1007/978-3-319-16181-5_54Proceedings in Computer Vision - ECCV 2014 Workshops held in Zurich (Switzerland) on 2015.This paper studies ways to detect good users for biometric recognition based on keystroke dynamics. Keystroke dynamics is an active research field for the biometric scientific community. Despite the great efforts made during the last decades, the performance of keystroke dynamics recognition systems is far from the performance achieved by traditional hard biometrics. This is very pronounced for some users, who generate many recognition errors even with the most sophisticate recognition algorithms. On the other hand, previous works have demonstrated that some other users behave particularly well even with the simplest recognition algorithms. Our purpose here is to study ways to distinguish such classes of users using only the genuine enrollment data. The experiments comprise a public database and two popular recognition algorithms. The results show the effectiveness of the Kullback-Leibler divergence as a quality measure to categorize users in comparison with other four statistical measures.This work has been partially supported by projects Bio-Shield (TEC2012-34881) from Spanish MINECO and BEAT (FP7-SEC-284989) from EU
SELM: Siamese extreme learning machine with application to face biometrics
This version of the article has been accepted for publication, after peer review (when applicable) and is subject to Springer Nature’s AM terms of use, but is not the Version of Record and does not reflect post-acceptance improvements, or any corrections. The Version of Record is available online at: http://dx.doi.org/10.1007/s00521-022-07100-zExtreme learning machine (ELM) is a powerful classification method and is very competitive among existing classification
methods. It is speedy at training. Nevertheless, it cannot perform face verification tasks properly because face verification
tasks require the comparison of facial images of two individuals simultaneously and decide whether the two faces identify
the same person. The ELM structure was not designed to feed two input data streams simultaneously. Thus, in 2-input
scenarios, ELM methods are typically 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 to the hidden layer. Moreover, we propose a Gender-Ethnicity-dependent triplet feature
exclusively trained on various specific demographic groups. This feature enables learning and extracting useful facial
features of each group. Experiments were conducted to evaluate and compare the performances of SELM, ELM, and deep
convolutional neural network (DCNN). The experimental results showed that the proposed feature could perform correct
classification at 97:87% accuracy and 99:45% area under the curve (AUC). They also showed that using SELM in
conjunction with the proposed feature provided 98:31% accuracy and 99:72% AUC. SELM outperformed the robust
performances over the well-known DCNN and ELM methods.This work was supported by the Faculty of
Information Technology, King Mongkut’s Institute of Technology
Ladkrabang and projects BIBECA (RTI2018-101248-B-I00
MINECO/FEDER) and BBforTAI (PID2021-127641OB-I00
MICINN/FEDER)
Privacy-preserving comparison of variable-length data with application to biometric template protection
The establishment of cloud computing and big data in a wide variety of daily applications has raised some privacy concerns due to the sensitive nature of some of the processed data. This has promoted the need to develop data protection techniques, where the storage and all operations are carried out without disclosing any information. Following this trend, this paper presents a new approach to efficiently compare variable-length data in the encrypted domain using homomorphic encryption where only encrypted data is stored or exchanged. The new variable-length-based algorithm is fused with existing fixed-length techniques in order to obtain increased comparison accuracy. To assess the soundness of the proposed approach, we evaluate its performance on a particular application: a multi-algorithm biometric template protection system based on dynamic signatures that complies with the requirements described in the ISO/IEC 24745 standard on biometric information protection. Experiments have been carried out on a publicly available database and a free implementation of the Paillier cryptosystem to ensure reproducibility and comparability to other schemes.This work was supported in part by the German Federal Ministry of Education and Research (BMBF); in part by the Hessen State Ministry
for Higher Education, Research, and the Arts (HMWK) within the Center for Research in Security and Privacy (CRISP); in part by the
Spanish Ministerio de Economia y Competitividad / Fondo Europeo de Desarrollo Regional through the CogniMetrics Project under Grant
TEC2015-70627-R; and in part by Cecaban
Score Normalization for Keystroke Dynamics Biometrics
Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. A. Morales, E. Luna-Garcia, J. Fierrez and J. Ortega-Garcia, "Score normalization for keystroke dynamics biometrics," Security Technology (ICCST), 2015 International Carnahan Conference on, Taipei, 2015, pp. 223-228. doi: 10.1109/CCST.2015.7389686This paper analyzes score normalization for keystroke
dynamics authentication systems. Previous studies have shown
that the performance of behavioral biometric recognition systems
(e.g. voice and signature) can be largely improved with score
normalization and target-dependent techniques. The main
objective of this work is twofold: i) to analyze the effects of
different thresholding techniques in 4 different keystroke
dynamics recognition systems for real operational scenarios; and
ii) to improve the performance of keystroke dynamics on the
basis of target-dependent score normalization techniques. The
experiments included in this work are worked out over the
keystroke pattern of 114 users from two different publicly
available databases. The experiments show that there is large
room for improvements in keystroke dynamic systems. The
results suggest that score normalization techniques can be used to
improve the performance of keystroke dynamics systems in more
than 20%. These results encourage researchers to explore this
research line to further improve the performance of these
systems in real operational environments.A.M. is supported by a post-doctoral Juan de la Cierva contract by the Spanish MECD (JCI-2012-12357). This work has been partially supported by projects: Bio-Shield (TEC2012-34881) from Spanish MINECO, BEAT (FP7-SEC-284989) from EU, CECABANK and Cátedra UAM Telefónica
BehavePassDB: Public Database for Mobile Behavioral Biometrics and Benchmark Evaluation
Mobile behavioral biometrics have become a popular topic of research, reaching promising results in
terms of authentication, exploiting a multimodal combination of touchscreen and background sensor
data. However, there is no way of knowing whether state-of-the-art classifiers in the literature can distinguish between the notion of user and device. In this article, we present a new database, BehavePassDB,
structured into separate acquisition sessions and tasks to mimic the most common aspects of mobile
Human-Computer Interaction (HCI). BehavePassDB is acquired through a dedicated mobile app installed
on the subjects devices, also including the case of different users on the same device for evaluation. We
propose a standard experimental protocol and benchmark for the research community to perform a fair
comparison of novel approaches with the state of the art1. We propose and evaluate a system based on
Long-Short Term Memory (LSTM) architecture with triplet loss and modality fusion at score levelThis project has received funding from the European Unions
Horizon 2020 research and innovation programme under the Marie
Skodowska-Curie grant agreement no. 860315, and from Orange
Labs. R. Tolosana and R. Vera-Rodriguez are also supported by
INTER-ACTION (PID2021-126521OB-I00 MICINN/FEDER
DeepFakes Detection Based on Heart Rate Estimation: Single- and Multi-frame
This chapter describes a DeepFake detection framework based on physiological measurement. In particular, we consider information related to the heart rate using remote photoplethysmography (rPPG). rPPG methods analyze video sequences looking for subtle color changes in the human skin, revealing the presence of human blood under the tissues. This chapter explores to what extent rPPG is useful for the detection of DeepFake videos. We analyze the recent fake detector named DeepFakesON-Phys that is based on a Convolutional Attention Network (CAN), which extracts spatial and temporal information from video frames, analyzing and combining both sources to better detect fake videos. DeepFakesON-Phys has been experimentally evaluated using the latest public databases in the field: Celeb-DF v2 and DFDC. The results achieved for DeepFake detection based on a single frame are over 98% AUC (Area Under the Curve) on both databases, proving the success of fake detectors based on physiological measurement to detect the latest DeepFake videos. In this chapter, we also propose and study heuristical and statistical approaches for performing continuous DeepFake detection by combining scores from consecutive frames with low latency and high accuracy (100% on the Celeb-DF v2 evaluation dataset). We show that combining scores extracted from short-time video sequences can improve the discrimination power of DeepFakesON-PhysThis work has been supported by projects: PRIMA (H2020-MSCA-ITN2019-860315), TRESPASS-ETN (H2020-MSCA-ITN-2019-860813), BIBECA (MINECO/FEDER RTI2018-101248-B-I00), and COST CA16101 (MULTI-FORESEE). J. H.-O. is supported by a PhD fellowship from UA
Generation of enhanced synthetic off-line signatures based on real on-line data
Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. M. Díaz-Cabrera, M. Gómez-Barrero, A. Morales, M. A. Ferrer, J. Galbally, "Generation of Enhanced Synthetic Off-Line Signatures Based on Real On-Line Data" in 14th International Conference on Frontiers in Handwriting Recognition (ICFHR), Heraklion (Greece), 2014, 482 - 487One of the main challenges of off-line signature verification is the absence of large databases. A possible alternative to overcome this problem is the generation of fully synthetic signature databases, not subject to legal or privacy concerns. In this paper we propose several approaches to the synthesis of off-line enhanced signatures from real dynamic information. These synthetic samples show a performance very similar to the one offered by real signatures, even increasing their discriminative power under the skilled forgeries scenario, one of the biggest challenges of handwriting recognition. Furthermore, the feasibility of synthetically increasing the enrolment sets is analysed, showing promising results.This work has been partially supported by projects: MICINN TEC2012-38630-C04-02, Contexts (S2009/TIC-1485) from CAM, Bio-Shield (TEC2012-34881) from Spanish MINECO, TABULA RASA (FP7-ICT-257289) and BEAT (FP7-SEC-284989) from EU, and Cátedra UAM-Telefónica
On-line signature recognition through the combination of real dynamic data and synthetically generated static data
This is the author’s version of a work that was accepted for publication in Pattern Recognition . Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Pattern Recognition , 48, 9 (2005) DOI: 10.1016/j.patcog.2015.03.019On-line signature verification still remains a challenging task within biometrics. Due to their behavioral nature (opposed
to anatomic biometric traits), signatures present a notable variability even between successive realizations. This
leads to higher error rates than other largely used modalities such as iris or fingerprints and is one of the main reasons
for the relatively slow deployment of this technology. As a step towards the improvement of signature recognition
accuracy, the present paper explores and evaluates a novel approach that takes advantage of the performance boost
that can be reached through the fusion of on-line and off-line signatures. In order to exploit the complementarity of the
two modalities, we propose a method for the generation of enhanced synthetic static samples from on-line data. Such
synthetic off-line signatures are used on a new on-line signature recognition architecture based on the combination
of both types of data: real on-line samples and artificial off-line signatures synthesized from the real data. The new
on-line recognition approach is evaluated on a public benchmark containing both real versions (on-line and off-line) of
the exact same signatures. Different findings and conclusions are drawn regarding the discriminative power of on-line
and off-line signatures and of their potential combination both in the random and skilled impostors scenarios.M. D.-C. is supported by a PhD fellowship from the
ULPGC and M.G.-B. is supported by a FPU fellowship
from the Spanish MECD. This work has been partially
supported by projects: MCINN TEC2012-38630-
C04-02, Bio-Shield (TEC2012-34881) from Spanish
MINECO, BEAT (FP7-SEC-284989) from EU, CECABANK
and Cátedra UAM-Telefónic
IJCB 2022 Mobile Behavioral Biometrics Competition (MobileB2C)
© 2022 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.Artículo escrito por un elevado número de autores, solo se referencian el que aparece en primer lugar, los autores pertenecientes a la UAM y el nombre del grupo de colaboración, si lo hubiereThis paper describes the experimental framework and results of the IJCB 2022 Mobile Behavioral Biometrics Competition (MobileB2C). The aim of MobileB2C is bench-marking mobile user authentication systems based on behavioral biometric traits transparently acquired by mobile devices during ordinary Human-Computer Interaction (HCI), using a novel public database, BehavePassDB 1 1 https://github.com/BiDAlab/MobileB2C_BehavePassDE, and a standard experimental protocol. The competition is divided into four tasks corresponding to typical user activities: keystroke, text reading, gallery swiping, and tapping. The data are composed of touchscreen data and several background sensor data simultaneously acquired. “Random” (different users with different devices) and “skilled” (different user on the same device attempting to imitate the legitimate one) impostor scenarios are considered. The results achieved by the participants show the feasibility of user authentication through behavioral biometrics, although this proves to be a non-trivial challenge. MobileB2C will be established as an on-going competition 2 2 https://sites.google.com/view/mobileb2c/.This project has received funding from the European Union's Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement No 860315, and from Orange Labs. R. Vera-Rodriguez, R. Tolosana, and A. Morales are also supported by INTERACTION (PID2021-1265210B-IOO MICINN/FEDER)