Hand Grasping Synergies As Biometrics.
Hand Grasping Synergies As Biometrics.
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DOI：
10.3389/fbioe.2017.00026
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发表时间：
2017
影响因子：
5.7
通讯作者：
Vinjamuri R
中科院分区：
文献类型：
--
作者：
Patel V;Thukral P;Burns MK;Florescu I;Chandramouli R;Vinjamuri R
最近，对更安全的身份验证系统的需求促使研究人员探索其他生物识别来源。这包括虹膜模式、掌纹、手部几何形状、面部识别和运动模式（手部运动、步态和眼部运动）。身份验证系统可以受益于人体运动的复杂性，其集成了多个控制级别（神经，肌肉和运动学）。使用主成分分析，我们提取时空的手的协同作用（运动协同作用），从一个物体抓取数据集，探索其作为一个潜在的生物识别。这些运动协同作用的形式是10个关节的关节角速度曲线。我们探讨了关节类型，数字，物体的数量和把握类型的影响。在其最佳配置下，运动协同实现了8.19%的相等错误率。虽然运动协同可以集成到具有动作捕捉能力的身份验证系统中，但我们还探索了手协同-姿势协同的相机就绪版本。在这个概念验证系统中，姿势协同作用表现良好，但只有在选择特定姿势时。基于这些结果，手协同显示出作为一种潜在的生物识别技术的希望，可以与其他基于手的生物识别技术相结合，以提高安全性。
Recently, the need for more secure identity verification systems has driven researchers to explore other sources of biometrics. This includes iris patterns, palm print, hand geometry, facial recognition, and movement patterns (hand motion, gait, and eye movements). Identity verification systems may benefit from the complexity of human movement that integrates multiple levels of control (neural, muscular, and kinematic). Using principal component analysis, we extracted spatiotemporal hand synergies (movement synergies) from an object grasping dataset to explore their use as a potential biometric. These movement synergies are in the form of joint angular velocity profiles of 10 joints. We explored the effect of joint type, digit, number of objects, and grasp type. In its best configuration, movement synergies achieved an equal error rate of 8.19%. While movement synergies can be integrated into an identity verification system with motion capture ability, we also explored a camera-ready version of hand synergies—postural synergies. In this proof of concept system, postural synergies performed well, but only when specific postures were chosen. Based on these results, hand synergies show promise as a potential biometric that can be combined with other hand-based biometrics for improved security.