Pintavirooj, Chuchart

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Pintavirooj, Chuchart
Alternative Name
Pintavirooj, C.
Pintavirooj, Chuchat
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Email
chuchart.pi@kmitl.ac.th

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    Item type:Publication,
    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
    ;
    Thongpance, Nuntachai
    ;
    Aoyama, Hisayuki
    ;
    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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    Item type:Publication,
    Fingerprint verification and identification based on local geometric invariants constructed from minutiae points and augmented with global directional filterbank features
    (2014-01-01)
    Pintavirooj, Chuchart
    ;
    Cohen, Fernand S.
    ;
    Iampa, Woranut
    This paper addresses the problems of fingerprint identification and verification when a query fingerprint is taken under conditions that differ from those under which the fingerprint of the same person stored in a database was constructed. This occurs when using a different fingerprint scanner with a different pressure, resulting in a fingerprint impression that is smeared and distorted in accordance with a geometric transformation (e.g., affine or even non-linear). Minutiae points on a query fingerprint are matched and aligned to those on one of the fingerprints in the database, using a set of absolute invariants constructed from the shape and/or size of minutiae triangles depending on the assumed map. Once the best candidate match is declared and the corresponding minutiae points are flagged, the query fingerprint image is warped against the candidate fingerprint image in accordance with the estimated warping map. An identification/verification cost function using a combination of distance map and global directional filterbank (DFB) features is then utilized to verify and identify a query fingerprint against candidate fingerprint(s). Performance of the algorithm yields an area of 0.99967 (perfect classification is a value of 1) under the receiver operating characteristic (ROC) curve based on a database consisting of a total of 1680 fingerprint images captured from 240 fingers. The average probability of error was found to be 0.713%. Our algorithm also yields the smallest false non-match rate (FNMR) for a comparable false match rate (FMR) when compared to the well-known technique of DFB features and triangulation-based matching integrated with modeling non-linear deformation. This work represents an advance in resolving the fingerprint identification problem beyond the state-of-the-art approaches in both performance and robustness. Copyright © 2014 The Institute of Electronics, Information and Communication Engineers.
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    Item type:Publication,
    Shape matching using set of curve geometric invariant point
    (2005-12-01)
    Pintavirooj, C.
    ;
    Nantivatana, P.
    ;
    Putjarupong, P.
    ;
    Withayachumnankul, W.
    ;
    Sangworasil, M.
    We introduce a non-iterative geometric-based method for shape matching using a novel set of geometric landmarks residing on a 2D contours. These landmarks are intrinsic and are computed from the differential geometry of the curve. We exploit the invariant properties of geometric landmarks that are local and preserved under the affine and some perspective transformation. Geometric invariant exploits coplanar five-point invariant and ration of area constructed from a sequence of consecutive landmarks. These invariants are preserved not only in affine map but weak perspective map as well. To reduce the sensitivity of the landmarks to noise, we use a B-Spline surface representation that smoothes out the curve prior to the computation of the landmarks. The matching is achieved by establishing correspondences between the landmarks after a conformal sorting based on derived absolute invariant and registering the contours. The experiments have shown that the purposed methods are robust and promising even in the presence of noise. Copyright UNION Agency - Science Press.