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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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3D face alignment and registration in the presence of facial expression diff{ligature}erences
(2013-01-01)
Pintavirooj, Chuchart
;
Cohen, Fernand S.
;
Tosranon, Prasong
This paper deals with the problem of 3D alignment of faces in the presence of some facial expression changes. The data are three dimensional and obtained using a laser scanner. Our approach is based on the diff{ligature}erential geometry of the surface and computes the intrinsic local fi{ligature}ducial points on the surface and on curves that reside on the surface. Because these fi{ligature}ducial points are local, they allow partial alignment, where part of the face is viewed. Moreover, since the fi{ligature}ducial points are relatively affi{ligature}ne-invariant to local affi{ligature}ne transformations, they allow matching and alignment when facial expression changes aff{ligature}ect part of the face. A fast, noniterative alignment procedure is presented in this paper that establishes reliable correspondences between fi{ligature}ducial points without any prior knowledge of the overall nonlinear global transformation that takes place after the changes in facial expressions. This is achieved through the construction of a set of ordered novel absolute local affi{ligature}ne invariants. With enough fi{ligature}ducial points set as correspondents, the overall nonlinear transformation is computed and the face before and after the transformation are aligned. For comparison, we also compare the alignment performance of our method with that of the iterative closest point (ICP) method and the coherent point drift (CPD) method which is based on the Gaussian mixture model by looking at the between-to-within variation (signal-to-noise ratio, SNR) between these two classes (match vs nonmatch) based on the average alignment errors for the genuine matches and nonmatches, normalized by their respective within variation, and by running it on the 3D face database GavabDB. The separation between the true match and nonmatch is best for our zero-torsion method (SNR of 1.2), followed by the ICP method (SNR of 0.37)and the CPD method (SNR of 0.15). It is interesting to observe here that, although the alignment error for the true match cases is lowest for the CPD method, it is also extremely low for the nonmatch cases, which would mean that the method would force a query into a wrong face, yielding poor specificity (lots of false alarms). © 2013 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.
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Using intrinsic surface geometry and absolute invariants for 3D face alignment and registration
(2010-12-01)
Pintavirooj, Chuchart
;
Tosranon, Prasong
;
Cohen, Fernand S.
In this paper we derive novel surface fiducial points that are computed from the differential geometry of the surface. The fiducial intrinsic points are intrinsic, local, and relative invariants, i.e., they are preserved under similarity, affine, and nonlinear transformations that are piecewise affine. As the fiducial points are computed from high order surface shape derivatives, their sensitivity to any noise either due to measurement error or local distortion is high. To reduce these effects, we use a B-Spline curve/surface representation that smoothes out the curve/surface prior to the computation of these intrinsic invariant points. The fiducial points are used in a non-iterative geometric-based method for 3D shape matching and registration of human faces. 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. The performance of the matching based on these fiducial points although shown for face alignment, the overall approach is generic to the alignment a variety of objects for which these intrinsic fiducial points exist and for scenarios where the classes of nonlinear transformations are piecewise affine. © 2010 IEEE.
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Fingerprint alignment based on local feature combined with affine geometric invariant
(2010-12-01)
Pintavirooj, Chuchart
;
Cohen, Fernand S.
;
Iampa, Woranut
In this paper we introduce a novel method of fingerprint alignment that uses the intrinsic geometric properties of minutiae-based triangles combined with the geometric invariant. The minutiae points are extracted from the fingerprint image and a Delaunay (DL) triangulation is constructed from these minutiae points resulting in a series of triangles. Corresponding minutiae points are established using local affine invariants constructed from the local minutia-based triangles. Triangles that are distorted by noise or have no counter part on the query are discarded. We rely only on "strong" matches that are reliable and present, for example, where the error metric between the local absolute invariants is below a set threshold. The correspondences of such matches are then used to estimate transformation parameters. The performance of our method is represented by computing the distance map error between a template and a query fingerprint after undoing the transformation, computed from the ridge structures of the two fingerprints. In conclusion, the proposed method can be used to find the corresponding minutiae and align any fingerprints considered into affine transformation, in the presence of noise including the partial occlusion.

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