The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. ex. Some numerals are expressed as "XNUMX".
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The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. Copyrights notice
Au meilleur de nos connaissances, il existe quelques recherches sur l'identification des auteurs au niveau des caractères d'écriture manuscrite utilisant uniquement des données d'accélération et de vitesse angulaire. Dans cet article, nous proposons une approche d'apprentissage en profondeur pour l'identification de l'écrivain en utilisant uniquement les données de capteurs inertiels d'écriture manuscrite aérienne. En particulier, nous séparons différentes représentations du degré de liberté (DoF) de l'écriture aérienne pour extraire séparément la dépendance locale et les interrelations dans différents CNN. Les expériences sur un ensemble de données public obtiennent de bonnes performances moyennes sans aucune extraction de fonctionnalités supplémentaire conçue à la main.
Yanfang DING
South China University of Technology
Yang XUE
South China University of Technology
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Yanfang DING, Yang XUE, "A Deep Learning Approach to Writer Identification Using Inertial Sensor Data of Air-Handwriting" in IEICE TRANSACTIONS on Information,
vol. E102-D, no. 10, pp. 2059-2063, October 2019, doi: 10.1587/transinf.2019EDL8070.
Abstract: To the best of our knowledge, there are a few researches on air-handwriting character-level writer identification only employing acceleration and angular velocity data. In this paper, we propose a deep learning approach to writer identification only using inertial sensor data of air-handwriting. In particular, we separate different representations of degree of freedom (DoF) of air-handwriting to extract local dependency and interrelationship in different CNNs separately. Experiments on a public dataset achieve an average good performance without any extra hand-designed feature extractions.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.2019EDL8070/_p
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@ARTICLE{e102-d_10_2059,
author={Yanfang DING, Yang XUE, },
journal={IEICE TRANSACTIONS on Information},
title={A Deep Learning Approach to Writer Identification Using Inertial Sensor Data of Air-Handwriting},
year={2019},
volume={E102-D},
number={10},
pages={2059-2063},
abstract={To the best of our knowledge, there are a few researches on air-handwriting character-level writer identification only employing acceleration and angular velocity data. In this paper, we propose a deep learning approach to writer identification only using inertial sensor data of air-handwriting. In particular, we separate different representations of degree of freedom (DoF) of air-handwriting to extract local dependency and interrelationship in different CNNs separately. Experiments on a public dataset achieve an average good performance without any extra hand-designed feature extractions.},
keywords={},
doi={10.1587/transinf.2019EDL8070},
ISSN={1745-1361},
month={October},}
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TY - JOUR
TI - A Deep Learning Approach to Writer Identification Using Inertial Sensor Data of Air-Handwriting
T2 - IEICE TRANSACTIONS on Information
SP - 2059
EP - 2063
AU - Yanfang DING
AU - Yang XUE
PY - 2019
DO - 10.1587/transinf.2019EDL8070
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E102-D
IS - 10
JA - IEICE TRANSACTIONS on Information
Y1 - October 2019
AB - To the best of our knowledge, there are a few researches on air-handwriting character-level writer identification only employing acceleration and angular velocity data. In this paper, we propose a deep learning approach to writer identification only using inertial sensor data of air-handwriting. In particular, we separate different representations of degree of freedom (DoF) of air-handwriting to extract local dependency and interrelationship in different CNNs separately. Experiments on a public dataset achieve an average good performance without any extra hand-designed feature extractions.
ER -