Hand Grasping Synergies As Biometrics.

Vrajeshri Patel,Ionut Florescu,Rajarathnam Chandramouli,Poojita Thukral,Martin K Burns,Ramana Vinjamuri

doi:10.3389/fbioe.2017.00026

Vrajeshri Patel, Ionut Florescu + Show 4 more

Open Access

https://doi.org/10.3389/fbioe.2017.00026

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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 combined with other hand-based biometrics for improved security.

Highlights

Identity theft has become a common crime that affects about 7% of the population each year (Harrell, 2015)
We explore 10 synergies extracted from grasping data for their potential use as biometrics
We have found that synergies derived from principal component analysis (PCA) are able to better capture inherent joint patterns that can reconstruct movements (Patel et al, 2016)

Summary

Introduction

Identity theft has become a common crime that affects about 7% of the population each year (Harrell, 2015). Experian, a company commonly used for credit checks and even identity theft protection, was the target of a hack, resulting in the theft of records for approximately 15 million people (Nasr, 2015) This included encrypted social security numbers, passport numbers, Hand Grasping Synergies As Biometrics and driver’s license numbers. A data breach of The United States Office of Personnel Management led to the loss of social security numbers, fingerprints, and other identifiable information, of 21.5 million people (Nakashina, 2015) Certain biometrics, such as iris scans, can potentially be forged in order to gain entry into biometric-based systems (Ruiz-Albacete et al, 2008). These reports reveal the need for identity verifications systems that do rely on static images, scans, or numbers

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