In-air Hand Gesture Signature Recognition: An iHGS Database Acquisition Protocol


Khoh, Wee How and Pang, Ying Han and Yap, Hui Yen (2023) In-air Hand Gesture Signature Recognition: An iHGS Database Acquisition Protocol. F1000Research, 11. p. 283. ISSN 2046-1402

[img] Text
01866364.pdf - Published Version
Restricted to Repository staff only

Download (686kB)


Background: With the advances in current technology, hand gesture recognition has gained considerable attention. It has been extended to recognize more distinctive movements, such as a signature, in human-computer interaction (HCI) which enables the computer to identify a person in a non-contact acquisition environment. This application is known as in-air hand gesture signature recognition. To our knowledge, there are no publicly accessible databases and no detailed descriptions of the acquisitional protocol in this domain. Methods: This paper aims to demonstrate the procedure for collecting the in-air hand gesture signature’s database. This database is disseminated as a reference database in the relevant field for evaluation purposes. The database is constructed from the signatures of 100 volunteer participants, who contributed their signatures in two different sessions. Each session provided 10 genuine samples enrolled using a Microsoft Kinect sensor camera to generate a genuine dataset. In addition, a forgery dataset was also collected by imitating the genuine samples. For evaluation, each sample was preprocessed with hand localization and predictive hand segmentation algorithms to extract the hand region. Then, several vector-based features were extracted. Results: In this work, classification performance analysis and system robustness analysis were carried out. In the classification analysis, a multiclass Support Vector Machine (SVM) was employed to classify the samples and 97.43% accuracy was achieved; while the system robustness analysis demonstrated low error rates of 2.41% and 5.07% in random forgery and skilled forgery attacks, respectively. Conclusions: These findings indicate that hand gesture signature is not only feasible for human classification, but its properties are also robust against forgery attacks.

Item Type: Article
Uncontrolled Keywords: Dynamic Signature, Hand Gesture Signature, Gesture Recognition, Hand Gesture Signature Database, Image Processing, Forgeries Attack
Subjects: Q Science > QA Mathematics > QA71-90 Instruments and machines > QA75.5-76.95 Electronic computers. Computer science
Divisions: Faculty of Information Science and Technology (FIST)
Depositing User: Ms Nurul Iqtiani Ahmad
Date Deposited: 05 Oct 2023 01:16
Last Modified: 05 Oct 2023 01:16


Downloads per month over past year

View ItemEdit (login required)