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Personal Authentication through Finger Knuckle Geometric and Texture Feature Measurements: Finger Knuckle Biometrics

Personal Authentication through Finger Knuckle Geometric and Texture Feature Measurements: Finger Knuckle Biometrics

Usha Kazhagamani, M. Ezhilarasan
Copyright: © 2017 |Pages: 25
ISBN13: 9781522524236|ISBN10: 1522524231|EISBN13: 9781522524243
DOI: 10.4018/978-1-5225-2423-6.ch009
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MLA

Kazhagamani, Usha, and M. Ezhilarasan. "Personal Authentication through Finger Knuckle Geometric and Texture Feature Measurements: Finger Knuckle Biometrics." Recent Advances in Applied Thermal Imaging for Industrial Applications, edited by V. Santhi, IGI Global, 2017, pp. 249-273. https://doi.org/10.4018/978-1-5225-2423-6.ch009

APA

Kazhagamani, U. & Ezhilarasan, M. (2017). Personal Authentication through Finger Knuckle Geometric and Texture Feature Measurements: Finger Knuckle Biometrics. In V. Santhi (Ed.), Recent Advances in Applied Thermal Imaging for Industrial Applications (pp. 249-273). IGI Global. https://doi.org/10.4018/978-1-5225-2423-6.ch009

Chicago

Kazhagamani, Usha, and M. Ezhilarasan. "Personal Authentication through Finger Knuckle Geometric and Texture Feature Measurements: Finger Knuckle Biometrics." In Recent Advances in Applied Thermal Imaging for Industrial Applications, edited by V. Santhi, 249-273. Hershey, PA: IGI Global, 2017. https://doi.org/10.4018/978-1-5225-2423-6.ch009

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Abstract

Finger Knuckle biometric is an emerging automated human identification approach that has received extensive significance in the area of research and real time applications in the recent past. Generally, a typical finger knuckle biometric system investigates the finger knuckle patterns present in the outer bend surface of the finger back region i.e., proximal phalanx region. In contrast, this paper focuses on the entire finger back region which includes proximal and distal phalanx of the finger knuckle surface for recognition. Further, this paper investigates a novel approach to achieve improved performance by simultaneous extraction and integration of finger knuckle geometric and texture features from a captured finger knuckle region. The geometric measures are derived by means of angular geometric analysis method which extracts angular-based feature information for unique identification. Similarly, texture measures are derived through statistical-based texture analysis methods.

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