Exploiting complexity in pen- and touch-based signature biometrics

Tolosana, Rubén; Vera-Rodríguez, Rubén; Guest, Richard; Fiérrez, Julián; Ortega‐Garcia, Javier

doi:10.1007/s10032-020-00351-3

Cited by 14 publications

(7 citation statements)

References 41 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In fact, it is possible to track the touch position in terms of X and Y coordinates in the screen reference system, but also pressure information and complex multi-touch gestures such as swipe, pinch, tap and scroll. Other complex features that can be extracted from touch data are velocity, acceleration, angle and trajectory [158].…”

Section: Mobile Acquisition Of Sensitive Datamentioning

confidence: 99%

A Survey of Privacy Vulnerabilities of Mobile Device Sensors

et al. 2022

Self Cite

View full text Add to dashboard Cite

The number of mobile devices, such as smartphones and smartwatches, is relentlessly increasing to almost 6.8 billion by 2022, and along with it, the amount of personal and sensitive data captured by them. This survey overviews the state of the art of what personal and sensitive user attributes can be extracted from mobile device sensors, emphasising critical aspects such as demographics, health and body features, activity and behaviour recognition, etc. In addition, we review popular metrics in the literature to quantify the degree of privacy, and discuss powerful privacy methods to protect the sensitive data while preserving data utility for analysis. Finally, open research questions are presented for further advancements in the field.

show abstract

Section: Mobile Acquisition Of Sensitive Datamentioning

confidence: 99%

A Survey of Privacy Vulnerabilities of Mobile Device Sensors

et al. 2022

Self Cite

View full text Add to dashboard Cite

show abstract

“…Some of the statistical features that can be extracted for touch signature are minimum, maximum, and mean of speed, acceleration, pressure, and size of the continuous strokes [63]. Further, for each stroke in a touch signature, touchduration, segment direction, log curvature radius, stroke length to width ratio can be extracted [64,65].…”

Section: Touch Signaturementioning

confidence: 99%

“…Tolosana et al [64] proposed an on-line signature verification system that is adaptable to the signature complexity level. In their proposed approach, a signature complexity detector based on the number of lognormals from the Sigma LogNormal writing generation model, and a time function extraction module are generated for each complexity level.…”

Section: Footstepmentioning

confidence: 99%

A Survey of Human-Computer Interaction (HCI) & Natural Habits-based Behavioral Biometric Modalities for User Recognition Schemes

Gupta¹,

Maple²,

Crispo³

et al. 2022

Preprint

View full text Add to dashboard Cite

The proliferation of Internet of Things (IoT) systems is having a profound impact across all aspects of life. Recognising and identifying particular users is central to delivering the personalised experience that citizens want to experience, and that organisations wish to deliver. This article presents a survey of human-computer interaction-based (HCI-based) and natural habits-based behavioral biometrics that can be acquired unobtrusively through smart devices or IoT sensors for user recognition purposes. Robust and usable user recognition is also a security requirement for emerging IoT ecosystems to protect them from adversaries. Typically, it can be specified as a fundamental building block for most types of human-to-things accountability principles and access-control methods. However, end-users are facing numerous security and usability challenges in using currently available knowledge- and token-based recognition (i.e., authentication and identification) schemes. To address the limitations of conventional recognition schemes, biometrics, naturally come as a first choice to supporting sophisticated user recognition solutions. We perform a comprehensive review of touch-stroke, swipe, touch signature, hand-movements, voice, gait and footstep behavioral biometrics modalities. This survey analyzes the recent state-of-the-art research of these behavioral biometrics with a goal to identify their attributes and features for generating unique identification signatures. Finally, we present security, privacy, and usability evaluations that can strengthen the designing of robust and usable user recognition schemes for IoT applications.

show abstract

“…For future work we will also investigate: i) the configuration parameters regarding the handwriting complexity (Tolosana et al 2020c), ii) the synthesis of realistic forgeries with different qualities , and iii) the application to other research lines based on time sequences such as keystroke biometrics (Morales et al 2020), and human activity recognition (Zhu et al 2015).…”

Section: Limitations and Future Workmentioning

confidence: 99%

DeepWriteSYN: On-Line Handwriting Synthesis via Deep Short-Term Representations

Tolosana

Delgado-Santos

Pérez-Uribe

et al. 2021

AAAI

Self Cite

View full text Add to dashboard Cite

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.

show abstract

Exploiting complexity in pen- and touch-based signature biometrics

Abstract: The version in the Kent Academic Repository may differ from the final published version. Users are advised to check http://kar.kent.ac.uk for the status of the paper. Users should always cite the published version of record.

Cited by 14 publications

References 41 publications

A Survey of Privacy Vulnerabilities of Mobile Device Sensors

A Survey of Privacy Vulnerabilities of Mobile Device Sensors

A Survey of Human-Computer Interaction (HCI) & Natural Habits-based Behavioral Biometric Modalities for User Recognition Schemes

DeepWriteSYN: On-Line Handwriting Synthesis via Deep Short-Term Representations

Contact Info

Product

Resources

About