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".
Copyrights notice
The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. Copyrights notice
우리가 아는 한, 가속도와 각속도 데이터만을 이용한 공중 필기 문자 수준의 작가 식별에 대한 몇 가지 연구가 있습니다. 본 논문에서는 공기필기의 관성 센서 데이터만을 이용하여 작가 식별을 위한 딥러닝 접근 방식을 제안한다. 특히, 우리는 서로 다른 CNN의 로컬 종속성과 상호 관계를 별도로 추출하기 위해 공중 필기의 자유도(DoF)에 대한 다양한 표현을 분리합니다. 공개 데이터세트에 대한 실험은 추가로 직접 디자인한 특징 추출 없이 평균적으로 우수한 성능을 달성합니다.
Yanfang DING
South China University of Technology
Yang XUE
South China University of Technology
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부
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
부
@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},}
부
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 -