In-Air 3D Dynamic Signature Recognition using Haptic Devices

Luisa, Luca De; Hine, Gabriel Emile; Maiorana, Emanuele; Campisi, Patrizio

doi:10.1109/iwbf50991.2021.9465089

Cited by 4 publications

(4 citation statements)

References 20 publications

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“…The In-Air Hand Gesture Signature (iHGS) database was the sole dataset used in this work for experiments and performance evaluation of models [12]. It consists of both genuine and forged in-air signatures from 100 individuals.…”

Section: B Datasetmentioning

confidence: 99%

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In-Air Hand Gesture Signature Recognition Using Multi-Scale Convolutional Neural Networks

Chuen,

Wee How,

Han

et al. 2023

JOIV : Int. J. Inform. Visualization

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The hand signature is a unique handwritten name or symbol that serves as a proof of identity. Due to its practicality and widespread use, hand signature is still used by financial institutions as a means of verifying and validating the identity of their customers. The emergence of the COVID-19 global pandemic has raised hygiene concerns regarding the conventional touch-based hand signature recognition system, which often requires sharing the acquisition devices among the public. This paper presents in-air hand gesture signature recognition using convolutional neural networks to address this concern. We designed a shallow multi-scale convolutional neural network using 3x3 and 5x5 kernel filter sizes to extract features on different scales. The feature maps from these two filters are then concatenated to provide more robust features, which improve the modelâ€™s performance.Â The experiment results show that the proposed architecture outperforms other architectures, which obtained the highest accuracy of 93.00%. On the other hand, our architecture consumed significantly fewer computational resources, requiring only an average of 3 minutes and 33 seconds to train. Additionally, the performance of the proposed architecture could be further enhanced by integrating it with recurrent neural networks (RNN). This integrated architecture of convolutional recurrent neural networks (C-RNN) can capture spatio-temporal features simultaneously.

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Section: B Datasetmentioning

confidence: 99%

“…De Luisa et al [12] utilized a haptic device to effectively capture the in-air signatures for identity verification using dynamic time warping and hidden Markov models. The proposed methods were evaluated on a self-collected dataset from 52 different individuals.…”

Section: Introductionmentioning

confidence: 99%

In-Air Hand Gesture Signature Recognition Using Multi-Scale Convolutional Neural Networks

Chuen,

Wee How,

Han

et al. 2023

JOIV : Int. J. Inform. Visualization

View full text Add to dashboard Cite

show abstract

“…To date, behavioral biometrics for CA on mobile devices has been an active field of research for over a decade. Throughout the years, several studies have focused on the modalities explored within our work, employing a variety of different classification algorithms (mobile keystroke: [16,17]; touch data: [18,19,20,21]; background sensor data: [22]; multimodal biometrics: [23,24]). Regarding touch data information it is worth mentioning several studies in the literature.…”

Section: Related Workmentioning

confidence: 99%

“…In contrast, on the one hand, gesturebased related studies take into account unimodal systems [7,16,17,18,19,20,21]; on the other hand, related studies based on DL models for multimodal behavioral biometrics do not have a specific focus on human gestures [26,27]; ii) we analyze the information captured by the touchscreen in combination with simultaneous background sensor data to exploit the complementarity between taskdependent features and background sensors features (accelerometer, gravity sensor, gyroscope, linear accelerometer, magnetometer). Interaction database), a novel and public database comprising more than 5GB from a wide range of mobile sensor data acquired under unsupervised scenario for user passive authentication [30].…”

Section: System Overviewmentioning

confidence: 99%