Palmprint alignment using intrinsic local affine-invariant fiducial points residing on area patches spanning between two successive fingers

Phromsuthirak, Choopol; Sanpanich, A.; Pintavirooj, C.

doi:10.1109/ecticon.2014.6839770

Cited by 3 publications

(4 citation statements)

References 23 publications

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“…The captured palmprint image system [22] was consisted of the OKER full HD 386 (HD 1920 x 1080 pixels) web camera on the tripod and the black screen used to place the user hand. Fig.…”

Section: Experimental and Resultsmentioning

confidence: 99%

“…After camera calibration process, the palmprint alignment algorithm from our previous work [22] was used to identify hand shape of each person. The algorithm was started from the extracting a set of affine invariant which is the area patches spanned between two successive fingers and the absolute invariant was computed by taking the ratio the ratio of consecutive area patches.…”

Section: B Palmprint Alignment Based On Intrinsic Local Affineinvarimentioning

confidence: 99%

“…Unlike the previous palmprint alignment, our palmprint alignment [22] does not required the guidance pegs. In our method, the hand position relative to camera orientation and the palmprint was aligned by a set of fiducial points which are the vertices of the area-patch spanning between two successive fingers convex hull.…”

Section: Introductionmentioning

confidence: 99%

“…Section IV provides some discussion and conclusion. The proposed hand shape identification algorithm is based on our previous work "Palmprint Alignment using Intrinsic Local Affine-invariant Fiducial Points Residing on Area Patches Spanning between Two Successive Fingers algorithm" [22]. This algorithm was using the corresponding fiducial points which are extracted from the four relatively affine-invariant area patches spanning between two successive fingers to align inquiry against reference palmprint image.…”

Section: Introductionmentioning

confidence: 99%

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Hand shape identification using palmprint alignment based on intrinsic local affine-invariant fiducial points

Phromsuthirak

Suwan

Sanpanich

et al. 2014

The 7th 2014 Biomedical Engineering International Conference

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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.

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“…The captured palmprint image system [22] was consisted of the OKER full HD 386 (HD 1920 x 1080 pixels) web camera on the tripod and the black screen used to place the user hand. Fig.…”

Section: Experimental and Resultsmentioning

confidence: 99%

Section: B Palmprint Alignment Based On Intrinsic Local Affineinvarimentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Hand shape identification using palmprint alignment based on intrinsic local affine-invariant fiducial points

Phromsuthirak

Suwan

Sanpanich

et al. 2014

The 7th 2014 Biomedical Engineering International Conference

Self Cite

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Contactless palmprint alignment based on intrinsic local affine-invariant feature points

Phromsuthirak

Tangsuksant

Sanpanich

et al. 2014

Signal and Information Processing Association Annual Summit and Conference (APSIPA), 2014 Asia-Pacific

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A Palmprint, biometric 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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Alphabetic hand sign interpretation using geometric invariance

Adhan

Pintavirooj

2014

The 7th 2014 Biomedical Engineering International Conference

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Palmprint alignment using intrinsic local affine-invariant fiducial points residing on area patches spanning between two successive fingers

Cited by 3 publications

References 23 publications

Hand shape identification using palmprint alignment based on intrinsic local affine-invariant fiducial points

Hand shape identification using palmprint alignment based on intrinsic local affine-invariant fiducial points

Contactless palmprint alignment based on intrinsic local affine-invariant feature points

Alphabetic hand sign interpretation using geometric invariance

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