In-air hand gesture signature recognition system based on 3-dimensional imagery

Wee How Khoh, Ying Han Pang, Andrew Beng Jin Teoh

Research output: Contribution to journalArticle

1 Citation (Scopus)

Abstract

A traditional online handwritten signature recognition system requires direct contact to acquisition device and usually will leave a traceable print on the surface. This made a signature possible and vulnerable to certain attempts of tracking and imitated. Looking into this shortfall, this paper proposes a novel approach to recognise an individual based on his/ her in-air hand motion while signing his/her signature. In this study, a low-cost acquisition device – Microsoft Kinect sensor is adopted to capture an image sequence of hand gesture signature. Palm region is first located and segmented through a predictive palm segmentation algorithm, which are then combined to generate a volume data. The volume data is condensed and reduced into a motion representation image by means of Motion History Image (MHI), which produces rich motion and temporal information. Several features are extracted from the MHI for empirical evaluation. Two classical recognition modes – identification and verification, are testified with an in-house database (HGS database). The proposed system achieves 90.4% identification accuracy and 3.22% equal error rate in verification mode. The experimental results substantiated the potential of the proposed system.

Original languageEnglish
Pages (from-to)6913-6937
Number of pages25
JournalMultimedia Tools and Applications
Volume78
Issue number6
DOIs
Publication statusPublished - 2019 Mar 1

Fingerprint

Air
Sensors
Costs

All Science Journal Classification (ASJC) codes

  • Software
  • Media Technology
  • Hardware and Architecture
  • Computer Networks and Communications

Cite this

@article{6a01609398cd46f784fb83c5c3fa4ac2,
title = "In-air hand gesture signature recognition system based on 3-dimensional imagery",
abstract = "A traditional online handwritten signature recognition system requires direct contact to acquisition device and usually will leave a traceable print on the surface. This made a signature possible and vulnerable to certain attempts of tracking and imitated. Looking into this shortfall, this paper proposes a novel approach to recognise an individual based on his/ her in-air hand motion while signing his/her signature. In this study, a low-cost acquisition device – Microsoft Kinect sensor is adopted to capture an image sequence of hand gesture signature. Palm region is first located and segmented through a predictive palm segmentation algorithm, which are then combined to generate a volume data. The volume data is condensed and reduced into a motion representation image by means of Motion History Image (MHI), which produces rich motion and temporal information. Several features are extracted from the MHI for empirical evaluation. Two classical recognition modes – identification and verification, are testified with an in-house database (HGS database). The proposed system achieves 90.4{\%} identification accuracy and 3.22{\%} equal error rate in verification mode. The experimental results substantiated the potential of the proposed system.",
author = "Khoh, {Wee How} and Pang, {Ying Han} and Teoh, {Andrew Beng Jin}",
year = "2019",
month = "3",
day = "1",
doi = "10.1007/s11042-018-6458-7",
language = "English",
volume = "78",
pages = "6913--6937",
journal = "Multimedia Tools and Applications",
issn = "1380-7501",
publisher = "Springer Netherlands",
number = "6",

}

In-air hand gesture signature recognition system based on 3-dimensional imagery. / Khoh, Wee How; Pang, Ying Han; Teoh, Andrew Beng Jin.

In: Multimedia Tools and Applications, Vol. 78, No. 6, 01.03.2019, p. 6913-6937.

Research output: Contribution to journalArticle

TY - JOUR

T1 - In-air hand gesture signature recognition system based on 3-dimensional imagery

AU - Khoh, Wee How

AU - Pang, Ying Han

AU - Teoh, Andrew Beng Jin

PY - 2019/3/1

Y1 - 2019/3/1

N2 - A traditional online handwritten signature recognition system requires direct contact to acquisition device and usually will leave a traceable print on the surface. This made a signature possible and vulnerable to certain attempts of tracking and imitated. Looking into this shortfall, this paper proposes a novel approach to recognise an individual based on his/ her in-air hand motion while signing his/her signature. In this study, a low-cost acquisition device – Microsoft Kinect sensor is adopted to capture an image sequence of hand gesture signature. Palm region is first located and segmented through a predictive palm segmentation algorithm, which are then combined to generate a volume data. The volume data is condensed and reduced into a motion representation image by means of Motion History Image (MHI), which produces rich motion and temporal information. Several features are extracted from the MHI for empirical evaluation. Two classical recognition modes – identification and verification, are testified with an in-house database (HGS database). The proposed system achieves 90.4% identification accuracy and 3.22% equal error rate in verification mode. The experimental results substantiated the potential of the proposed system.

AB - A traditional online handwritten signature recognition system requires direct contact to acquisition device and usually will leave a traceable print on the surface. This made a signature possible and vulnerable to certain attempts of tracking and imitated. Looking into this shortfall, this paper proposes a novel approach to recognise an individual based on his/ her in-air hand motion while signing his/her signature. In this study, a low-cost acquisition device – Microsoft Kinect sensor is adopted to capture an image sequence of hand gesture signature. Palm region is first located and segmented through a predictive palm segmentation algorithm, which are then combined to generate a volume data. The volume data is condensed and reduced into a motion representation image by means of Motion History Image (MHI), which produces rich motion and temporal information. Several features are extracted from the MHI for empirical evaluation. Two classical recognition modes – identification and verification, are testified with an in-house database (HGS database). The proposed system achieves 90.4% identification accuracy and 3.22% equal error rate in verification mode. The experimental results substantiated the potential of the proposed system.

UR - http://www.scopus.com/inward/record.url?scp=85051264714&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85051264714&partnerID=8YFLogxK

U2 - 10.1007/s11042-018-6458-7

DO - 10.1007/s11042-018-6458-7

M3 - Article

AN - SCOPUS:85051264714

VL - 78

SP - 6913

EP - 6937

JO - Multimedia Tools and Applications

JF - Multimedia Tools and Applications

SN - 1380-7501

IS - 6

ER -