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 language | English |
|---|---|
| Pages (from-to) | 6913-6937 |
| Number of pages | 25 |
| Journal | Multimedia Tools and Applications |
| Volume | 78 |
| Issue number | 6 |
| DOIs | |
| Publication status | Published - 2019 Mar 1 |
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All Science Journal Classification (ASJC) codes
- Software
- Media Technology
- Hardware and Architecture
- Computer Networks and Communications
Cite this
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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 journal › Article
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.
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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 -