Journal
IEEE ACCESS
Volume 5, Issue -, Pages 23320-23326Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/ACCESS.2017.2764471
Keywords
Authentication; biometrics; hand tremors; human computer interaction; leap motion; machine learning; neurophysiology; neuroscience; recognition; security
Ask authors/readers for more resources
In this paper, the applicability of hand tremor-based biometric recognition via leap motion device is investigated. The hypothesis is that the hand tremor is unique for humans and can be utilized as a biometric identification. In order to verify our hypothesis, spatiotemporal hand tremor signals are acquired from subjects. The objective is to establish a live and secure identification system to avoid mimic and cloning of password by attackers. Various feature extraction methods, including statistical, fast Fourier transform, discrete wavelet transform, and 1-D local binary pattern are used. For evaluating recognition performance, Naive Bayes and Multi-Layer Perceptron are utilized as linear-simple and nonlinear-complex classifiers, respectively. Since the conducted experiments produced promising results (above 95% of classification accuracy rate), it is considered that the proposed approach has the potential to be used as a new biometric identification manner in the field of security.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available