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Volume 10
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Article

Vein Pattern Verification and Identification Based on Local Geometric Invariants Constructed from Minutia Points and Augmented with Barcoded Local Feature

by
Yutthana Pititheeraphab
1,
Nuntachai Thongpance
2,
Hisayuki Aoyama
3 and
Chuchart Pintavirooj
1,*
1
Faculty of Engineering, King Mongkut’s Institute of Technology Ladkrabang, Bangkok 10520, Thailand
2
College of Biomedical Engineering, Rangsit University, Pathum Thani 12000, Thailand
3
Faculty of Engineering, University of Electro-Communications, Tokyo 182-8585, Japan
*
Author to whom correspondence should be addressed.
Appl. Sci. 2020, 10(9), 3192; https://doi.org/10.3390/app10093192
Submission received: 30 March 2020 / Revised: 23 April 2020 / Accepted: 27 April 2020 / Published: 3 May 2020
(This article belongs to the Section Computing and Artificial Intelligence)
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Abstract

This paper presents the development of a hybrid feature—dorsal hand vein and dorsal geometry—modality for human recognition. Our proposed hybrid feature extraction method exploits two types of features: dorsal hand geometric-related and local vein pattern. Using geometric affine invariants, the peg-free system extracts minutia points and vein termination and bifurcation and constructs a set of geometric invariants, which are then used to establish the correspondence between two sets of minutiae—one for the query vein image and the other for the reference vein image. When the correspondence is established, geometric transformation parameters are computed to align the query with the reference image. Once aligned, hybrid features are extracted for identification. In this study, the algorithm was tested on a database of 140 subjects, in which ten different dorsal hand geometric-related images were taken for each individual, and yielded the promising results. In this regard, we have achieved an equal error rate (EER) of 0.243%, indicating that our method is feasible and effective for dorsal vein recognition with high accuracy. This hierarchical scheme significantly improves the performance of personal verification and/or identification.
Keywords: vein pattern; biometric; verification; identification; affine invariant; hybrid feature; dorsal hand vein recognition; non-contact; contactless vein pattern; biometric; verification; identification; affine invariant; hybrid feature; dorsal hand vein recognition; non-contact; contactless
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MDPI and ACS Style

Pititheeraphab, Y.; Thongpance, N.; Aoyama, H.; Pintavirooj, C. Vein Pattern Verification and Identification Based on Local Geometric Invariants Constructed from Minutia Points and Augmented with Barcoded Local Feature. Appl. Sci. 2020, 10, 3192. https://doi.org/10.3390/app10093192

AMA Style

Pititheeraphab Y, Thongpance N, Aoyama H, Pintavirooj C. Vein Pattern Verification and Identification Based on Local Geometric Invariants Constructed from Minutia Points and Augmented with Barcoded Local Feature. Applied Sciences. 2020; 10(9):3192. https://doi.org/10.3390/app10093192

Chicago/Turabian Style

Pititheeraphab, Yutthana, Nuntachai Thongpance, Hisayuki Aoyama, and Chuchart Pintavirooj. 2020. "Vein Pattern Verification and Identification Based on Local Geometric Invariants Constructed from Minutia Points and Augmented with Barcoded Local Feature" Applied Sciences 10, no. 9: 3192. https://doi.org/10.3390/app10093192

APA Style

Pititheeraphab, Y., Thongpance, N., Aoyama, H., & Pintavirooj, C. (2020). Vein Pattern Verification and Identification Based on Local Geometric Invariants Constructed from Minutia Points and Augmented with Barcoded Local Feature. Applied Sciences, 10(9), 3192. https://doi.org/10.3390/app10093192

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