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Author: search.filters.author.Phromsuthirak, ChoopolSubject: search.filters.subject.Palmprint alignment

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    Contactless palmprint alignment based on intrinsic local affine-invariant feature points
    (2014-02-12)
    Phromsuthirak, Choopol
    ;
    Tangsuksant, Watcharin
    ;
    Sanpanich, Arthorn
    ;
    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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    Hand shape identification using palmprint alignment based on intrinsic local affine-invariant fiducial points
    (2014-01-20)
    Phromsuthirak, Choopol
    ;
    Suwan, Supakorn
    ;
    Sanpanich, Arthorn
    ;
    Pintavirooj, Chuchart
    Palmprint is the mostly popular biometrics used in security system. However, it is difficult to acquire the palmprint features with the common problems of pose, lighting, orientation, gesture etc. of palmprint image. So, these problems have the effect to reduce the level of confidence in personal authentication. In this paper, we proposed a new hand shape identification using palmprint alignment without guidance pegs algorithm for improving the level of confidence in palmprint identification system. The palmprint alignment based on a set of fiducial points which are intrinsic, local and preserved under affine transformation. The fiducial points are relative affine invariant to affine transformations, they allow for alignment where position of the palm relative to camera orientation can be arbitrary set. Moreover, before palmprint alignment process, the web camera which was used to capture the palmprint image was calibrated by Camera Calibration Toolbox developed by Jean-Yves Bouguet. The performance of the identification algorithm was tested in 2 types: intra-class identification and inter-class identification. The intra-class identification has the most of distance map error was started from 1.4 pixels to 4.5 pixels and the inter-class identification has 18 percent equal error rate.