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The preliminary investigation of ear recognition using hybrid technique
(2017-02-21)
Taertulakarn, S.
;
Pintavirooj, C.
;
Tosranon, P.
;
Hamamoto, K.
Ear recognition is one of the new patterns of biometrics. Ear structure is believed to contain specific and unique anatomical markers, which can be used both to distinguish it from others. In this paper, we derive preliminary of Ear identification based on the geometric features on 3D ear surface for 2D ear image. Principal components analysis (PCA) is used in this study for feature extraction to identify a person. The result is shown 92% recognition rate of 50 volunteers.
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3D ear alignment based on geometry invariant
(2016-02-04)
Taertulakarn, S.
;
Tosranon, P.
;
Pintavirooj, C.
Nowadays, ear shape study is one of the most significant trends in the biometric research. In this study, we present a novel surface fiducial point's detection that is computed from the differential surface geometry. The fiducial points are intrinsic, local, and relative invariants, i.e., they are preserved under similarity, affine, and nonlinear transformations that are piecewise affine. In our experiment, the fiducial points are used in a non-iterative geometric-based method for 3D ear alignment. The matching is achieved by establishing correspondences between fiducial points after a sorting based on a set of absolute local affine invariants derived from them. Experimental results showed that our purposed surface feature is suitable for further application to 3D ear identification.
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Using intrinsic surface geometry invariant for 3D ear alignment
(2015-01-01)
Taertulakarn, S.
;
Tosranon, P.
;
Pintavirooj, C.
In this study we derive novel surface fiducial point's detection that is computed from the differential surface geometry. The fiducial points are intrinsic, local, and relative invariants, i.e., they are preserved under similarity, affine, and nonlinear transformations that are piecewise affine. In our experiment, the fiducial points, computed from high order surface shape derivatives, are used in a non-iterative geometric-based method for 3D ear registration and alignment. The matching is achieved by establishing correspondences between fiducial points after a sorting based on a set of absolute local affine invariants derived from them. Experimental results showed that our purposed surface feature is suitable for further application to 3D ear identification because its robustness to geometric transformation.
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Gaussian curvature-based geometric invariance for ear recognition
(2014-01-20)
Taertulakarn, S.
;
Tosranon, P.
;
Pintavirooj, C.
Ear recognition is one of the new patterns of biometrics. In this paper we derive a novel geometric invariance on ear surfaces that it is preserved under affine and weak perspective transformations. Our 3D shape features are based on the Gaussian curvature and Mean curvature. When a surface undergoes an affine transformation, the shape features are the affine transformed shape features of the original surface; they are preserved and hence can be for shape matching. We have tested robustness of the shape feature on the 3D ear data for various linear geometric transformations. The experiment results show that our purposed shape feature is suitable for further application to 3D ear identification because its robustness to geometric transformation.
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3D modeling from multiple projections: Parallel-beam to helical cone-beam trajectory
(2005-12-01)
Narkbuakaew, W.
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Pintavirooj, C.
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Withayachumnankul, W.
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Sangworasil, M.
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Taertulakarn, S.
Tomographic imaging is a technique for exploration of a cross-section of an inspected object without destruction. Normally, the input data, known as the projections, are gathered by repeatedly radiating coherent waveform through the object in a number of viewpoints, and receiving by an array of corresponding detector in the opposite position. In this research, as a replacement of radiographs, the series of photographs taken around the opaque object under the ambient light is completely served as the projections. The purposed technique can be adopted with various beam geometry including parallel-beam, cone-beam and spiral cone-beam geometry. From the process of tomography, the outcome is the stack of pseudo cross-sectional image. Not the internal of cross section is authentic, but the edge or contour is valid. Copyright UNION Agency - Science Press.
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Ultrasonic refractive index and sound velocity tomography
(2004-12-01)
Pintavirooj, C.
;
Jaruwongrungsee, K.
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Withayachumnankul, W.
;
Hamamoto, K.
;
Taertulakarn, S.
Ultrasonic computed tomography (UCT) is one of the methods capable of tissue characterization. It is expected to provide not only a quantitative image but also diagnostic information. As in X-ray CT, UCT requires a projection data to reconstruct a cross-sectional image. The projection data of Ultrasonic Tomography is based on measurement the time delay, which is time difference between ultrasound traverse with and without object. In this paper, we investigate two different types of quantitative UCT image, refractive-index and sound-velocity image. We purpose the new method of measurement the time delay by converting the received ultrasonic pulse to frequency domain and measuring the phase shift of the center frequency of the broadband pulse. The method seems more robust to noise. Two image reconstruction techniques are used for comparison purpose including traditional filtered back-projection and simultaneous algebraic reconstruction technique (SART). © 2004 IEEE.

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