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Vein pattern verification and identification based on local geometric invariants constructed from minutia points and augmented with barcoded local feature
(2020-05-01)
Pititheeraphab, Yutthana
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Thongpance, Nuntachai
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Aoyama, Hisayuki
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Pintavirooj, Chuchart
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.
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Footprint Identification using Deep Learning
(2019-01-10)
Keatsamarn, Tanapon
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Pintavirooj, Chuchart
Human footprint is the biometric system of the individual person. Everyone has specific footprints. It can be used instead of password-based authentication in the security system such as a user authentication for the financial transaction. The password-based system cannot verify that the person who entered the password is valid or not. Therefore the biometric system is more secure than the password-based system. For that reason, it's interesting to use footprint image in the creating of the footprint-based identification system. In this paper, the convolutional neural network training is used for deep learning classification. Convolutional neural networks are essential for deep learning and suited for image recognition.
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Distinctiveness, complexity, and repeatability of online signature templates
(2018-12-01)
Sae-Bae, Napa
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Memon, Nasir
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Sooraksa, Pitikhate
This paper proposes three measures to quantify the characteristics of online signature templates in terms of distinctiveness, complexity and repeatability. A distinctiveness measure of a signature template is computed from a set of enrolled signature samples and a statistical assumption about random signatures. Secondly, a complexity measure of the template is derived from a set of enrolled signature samples. Finally, given a signature template, a measure to quantify the repeatability of the online signature is derived from a validation set of samples. These three measures can then be used as an indicator for the performance of the system in rejecting random forgery samples and skilled forgery samples and the performance of users in providing accepted genuine samples, respectively. The effectiveness of these three measures and their applications are demonstrated through experiments performed on three online signature datasets and one keystroke dynamics dataset using different verification algorithms.
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Study of ECG variation in daily activity
(2017-12-19)
Samona, Yanisa
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Pintavirooj, C.
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Visitsattapongse, S.
Electrocardiogram (ECG) records electrical activity of the heart spreading through the heart muscle to make the heart contract. Recently ECG has been captured attention as biometric feature due to its uniqueness and large reliabilities for human identifications. In this study we aimed to verify the conservative ECG of human in their activities to ensure whether it is suitable to be used as biometric devices. Experiment studies involved 6 participants of which the age ranges is between 21 and 23. We test the robustness of ECG under various situation including health condition, emotional state and heart rate variation. The recorded ECG signal is forwarded for analysis using Matlab. Correlation coefficient of ECG Fourier transform is used as criterion to validate the ECG robustness. The result indicates that ECG is not stable and seems to vary with daily activity and emotional state. This will hampers ECG to be used as Biometric.
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Contactless palmprint alignment based on intrinsic local affine-invariant feature points
(2014-02-12)
Phromsuthirak, Choopol
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Tangsuksant, Watcharin
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Sanpanich, Arthorn
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Pintavirooj, Chuchart
A Palmprint, biométrie characteristics, was mostly found in civil and commercial applications for security system because it has more reliable and easy to capture by low resolution devices. This paper was to develop a new contactless palmprint alignment with general USB camera on tripod. The palmprint image is acquired by this camera and using intrinsic local affine-invariant key points residing on the area patches spanning between two successive fingers to align palmprint image. The key points are relative affine invariant to affine transformations so this algorithm does not need the guidance pegs in acquisition process to fix hand position to avoid the scaling, translation and rotation problems for correctly palmprint image alignment. Finally, the developed algorithm was tested by 10 left-handed palmprint images collected from different subjects. The simulation results indicate by distance map error of 1.4899 pixels.
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Fingerprint image enhancement with second derivative Gaussian filter and directional wavelet transform
(2010-06-01)
Sihalath, Keokanlaya
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Choomchuay, Somsak
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Wada, Shatoshi
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Hamamoto, Kazuhiko
In this paper, we propose a technique for enhancing the quality of fingerprint images. Directional wavelet transform and second derivative of a Gaussian filter are applied. The original fingerprint image is decomposed into approximation and detail sub-images. To each sub-dimension a directional filter: second derivative of Gaussian filter is applied for tuning up the image features. The enhanced image is measured for its improvement by testing the success of core point identification where Poincare technique is used. The commonly-well-known database FVC-2004 is used in this study. The obtained results offer clean visualization as well as the increase the success of true core point detection. © 2010 IEEE.
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Directional filter bank: An enhancement for fingerprint feature detection
(2006-12-01)
Tantachun, S.
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Pintavirooj, C.
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Sangworasil, M.
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Kitjaidure, Y.
Fingerprints is the most popular biometric modality. Fingerprint features include core, delta, ridge bifurcation, ridge ending, enclosure and short edge. In order to increase the performance of fingerprint identification system, it is essential that these directional-related features are needed to be enhanced. In this paper, we purpose the directional filter bank to enhance the fingerprint features which later used in classification step. Our 2D FIR filter is designed using a 2D frequency-transform method which is easily implemented and easily imposed a zero phase response. Our proposed technique demonstrates the promising results. © 2006 IEEE.

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