Faculty of Information Science and Technology, Multimedia University, Bukit Beruang, Melaka, 75450, Malaysia.
F1000Res. 2023 May 2;11:283. doi: 10.12688/f1000research.74134.2. eCollection 2022.
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. 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. 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. These findings indicate that hand gesture signature is not only feasible for human classification, but its properties are also robust against forgery attacks.
随着当前技术的进步，手势识别已经引起了相当大的关注。它已经扩展到识别更独特的动作，例如签名，在人机交互（HCI）中，使计算机能够在非接触式采集环境中识别个人。这种应用被称为空中手势签名识别。据我们所知，在这个领域没有公开的可访问数据库，也没有关于采集协议的详细描述。本文旨在演示采集空中手势签名数据库的过程。该数据库作为参考数据库分发给相关领域，用于评估目的。该数据库由 100 名志愿者的签名构建而成，他们在两个不同的会话中贡献了自己的签名。每个会话提供 10 个使用 Microsoft Kinect 传感器相机注册的真实样本，以生成真实数据集。此外，还通过模仿真实样本收集了伪造数据集。为了进行评估，每个样本都经过手部定位和预测手部分割算法的预处理，以提取手部区域。然后，提取了几个基于向量的特征。在这项工作中，进行了分类性能分析和系统鲁棒性分析。在分类分析中，使用多类支持向量机（SVM）对样本进行分类，实现了 97.43%的准确率；而系统鲁棒性分析表明，在随机伪造和熟练伪造攻击中，错误率分别低至 2.41%和 5.07%。这些发现表明，手势签名不仅对手部分类可行，而且其特性也对伪造攻击具有很强的鲁棒性。
