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Multimodal biometric method based on vein and geometry of a single finger

Multimodal biometric method based on vein and geometry of a single finger

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  • Author(s): B.J. Kang 1  and  K.R. Park 2
    • View affiliations
    • Affiliations: 1: Electronics and Telecommunications Research Institute, Daejeon, Republic of Korea
      2: Division of Electronics and Electrical Engineering, Dongguk University-Seoul, Seoul, Republic of Korea
  • Source: Volume 4, Issue 3, September 2010, p. 209 – 217
    DOI:  10.1049/iet-cvi.2009.0081 , Print ISSN 1751-9632, Online ISSN 1751-9640
© The Institution of Engineering and Technology
Published

There are limits to a single biometric observation such as variation in an individual biometric feature due to the condition of a sensor, the health condition of a human, illumination variation and so on. To overcome such limitations, the authors propose a new multimodal biometric approach integrating finger vein recognition and finger geometry recognition at the score level. The method presents three advantages compared to previous works: (i) the proposed multimodal biometric system can be constructed as a tiny device, which uses a finger vein and finger geometry features acquired from a single finger; (ii) the proposed finger geometry recognition, based on Fourier descriptors, is robust to the translation and rotation of a finger; and (iii) the authors obtained better recognition accuracy using the score-level fusion method based on a support vector machine than by other score-level fusion methods such as the MAX, MIN and SUM rules. The results showed that the equal error rate of the proposed method decreased by as much as 1.089 and 1.627% compared with finger vein recognition and finger geometry recognition methods, respectively.

Inspec keywords: feature extraction; Fourier analysis; computational geometry; support vector machines; object recognition; biometrics (access control); sensor fusion

Other keywords: score level fusion method; finger vein recognition; finger geometry recognition; Fourier descriptors; support vector machine; multimodal biometric method

Subjects: Computational geometry; Combinatorial mathematics; Combinatorial mathematics; Sensor fusion; Knowledge engineering techniques; Mathematical analysis; Computer vision and image processing techniques; Mathematical analysis; Image recognition

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