Hand Grasping Synergies As Biometrics

Frontiers in Bioengineering and Biotechnology
Vrajeshri PatelRamana Vinjamuri

Abstract

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 ...Continue Reading

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Dec 15, 2015·Medical & Biological Engineering & Computing·Vrajeshri PatelRamana Vinjamuri

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Citations

May 30, 2019·Journal of Neuroengineering and Rehabilitation·Néstor J Jarque-BouHenning Müller
Jan 11, 2020·Scientific Data·Néstor J Jarque-BouHenning Müller
Feb 17, 2019·Journal of Neuroengineering and Rehabilitation·Francesca StivalManfredo Atzori

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Methods Mentioned

BETA
PCA
feature extraction

Software Mentioned

Image Analysis Toolbox
MATLAB
Simulink 3D Animation
LabVIEW

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