SVC-onGoing: Signature verification competition

Tolosana, Rubén; Vera-Rodríguez, Rubén; González-García, Carlos; Fiérrez-Aguilar, J.; Morales, Aythami; Ortega‐Garcia, Javier; Ruiz‐Garcia, Juan Carlos; Romero-Tapiador, Sergio; Rengifo, Santiago; Caruana, Miguel; Jiang, Jiajia; Lai, Songxuan; Jin, Lianwen; Zhu, Yecheng; Galbally, Javier; Diaz, Moises; Ferrer, Miguel A.; Gomez‐Barrero, Marta; Hodashinsky, I. A.; Sarin, Konstantin; Слезкин, Артем; Bardamova, Marina; Svetlakov, Mikhail; Saleem, Mohammad; Szucs, Cintia Lia; Kővári, Bence; Pulsmeyer, Falk; Wehbi, Mohamad; Zanca, Dario; Ahmad, Sumaiya; Mishra, Sarthak; Jabin, Suraiya

doi:10.1016/j.patcog.2022.108609

Cited by 39 publications

(13 citation statements)

References 25 publications

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“…Interaction with mobile devices is not only done through the use of the finger, but also through an stylus [12], [13]. In general, writing and drawing require greater motor and cognitive development than simple touch gestures.…”

Section: A Children Interaction: Stylus Vs Fingermentioning

confidence: 99%

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

Ruiz‐Garcia¹,

Tolosana²,

Vera-Rodríguez³

et al. 2022

Preprint

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This article presents a comprehensive analysis of the different tests proposed in the recent ChildCI framework 1 , 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 behaviors. 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.

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Section: A Children Interaction: Stylus Vs Fingermentioning

confidence: 99%

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

Ruiz‐Garcia¹,

Tolosana²,

Vera-Rodríguez³

et al. 2022

Preprint

Self Cite

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“…The competition is hosted on CodaLab 4 , a powerful opensource framework for running scientific competitions and benchmarks [25], frequently used in research and education.…”

Section: Competition Set Upmentioning

confidence: 99%

“…All biometric data that enable or contribute to differentiating between individuals throughout the way they perform activities are labelled as behavioral. Behavioral biometrics are a broad category including voice [6], gait [11], keystroke [3], or handwritten signature [25], among others. They are recently gaining more attention from academia and industry as a new solution for the problem of mobile user authentication [23].…”

Section: Introductionmentioning

confidence: 99%

IJCB 2022 Mobile Behavioral Biometrics Competition (MobileB2C)

Stragapede¹,

Vera-Rodríguez²,

Tolosana³

et al. 2022

Preprint

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This paper describes the experimental framework and results of the IJCB 2022 Mobile Behavioral Biometrics Competition (MobileB2C). The aim of MobileB2C is benchmarking 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, Be-havePassDB 1 , 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 nontrivial challenge. MobileB2C will be established as an on-going competition 2 .

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“…4.1.4 DsDTW. DsDTW [13] is the latest state-of-the-art model for dynamic signature verification, which has won the ICDAR 2021 competition for online signature verification with obvious margins [42]. The DsDTW adopts a CRAN architecture to provide robust inputs for subsequent processing.…”

Section: 1mentioning

confidence: 99%

MSDS: A Large-Scale Chinese Signature and Token Digit String Dataset for Handwriting Verification

Zhang¹,

Jiang²,

Liu³

et al. 2022

Preprint

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Although online handwriting verification has made great progress recently, the verification performances are still far behind the real usage owing to the small scale of the datasets as well as the limited biometric mediums. Therefore, this paper proposes a new handwriting verification benchmark dataset named Multimodal Signature and Digit String (MSDS), which consists of two subsets: MSDS-ChS (Chinese Signatures) and MSDS-TDS (Token Digit Strings), contributed by 402 users, with 20 genuine samples and 20 skilled forgeries per user per subset. MSDS-ChS consists of handwritten Chinese signatures, which, to the best of our knowledge, is the largest publicly available Chinese signature dataset for handwriting verification, at least eight times larger than existing online datasets. Meanwhile, MSDS-TDS consists of handwritten Token Digit Strings, i.e, the actual phone numbers of users, which have not been explored yet. Extensive experiments with different baselines are respectively conducted for MSDS-ChS and MSDS-TDS. Surprisingly, verification performances of state-of-the-art methods on MSDS-TDS are generally better than those on MSDS-ChS, which indicates that the handwritten Token Digit String could be a more effective biometric than handwritten Chinese signature. This is a promising discovery that could inspire us to explore new biometric traits. The MSDS dataset is available at https://github.com/HCIILAB/MSDS.

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SVC-onGoing: Signature verification competition

Cited by 39 publications

References 25 publications

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

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

IJCB 2022 Mobile Behavioral Biometrics Competition (MobileB2C)

MSDS: A Large-Scale Chinese Signature and Token Digit String Dataset for Handwriting Verification

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