Hand Tremor Based Biometric Recognition Using Leap Motion Device

• Published in: IEEE access Vol. 5; pp. 23320 - 23326
• Main Author: Atas, Musa
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 01.01.2017; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Authentication; Biometric identification; Biometrics; Biometrics (access control); Cloning; Data acquisition; Discrete Wavelet Transform; Discrete wavelet transforms; Fast Fourier transformations; Feature extraction; Fourier transforms; hand tremors; human computer interaction; Hypotheses; leap motion; machine learning; Multilayers; neurophysiology; neuroscience; Object recognition; recognition; security; Software; Statistical methods; Tremors; Wavelet transforms
• ISSN: 2169-3536; 2169-3536
• DOI: 10.1109/ACCESS.2017.2764471

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, Naïve 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.