US 6,876,757 B2Granted

Fingerprint recognition system

AssigneeGeometric Informatics, Inc.

InventorsShing-Tung Yau, Xianfeng Gu, Zhiwu Zhang

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Abstract

A method of analyzing and recognizing fingerprint images that utilizes vector processing of a vector field that is defined as the tangential vector of the fingerprint ridge curves is disclosed. The raw fingerprint image is divided into blocks, filtered to remove noise, and the orientation direction of each block is found. This allows the ridge curves to be enhanced and approximated by piece-wise linear approximations. The piece-wise linear approximations to the ridge curves allow the minutiae to be extracted and classified and a fingerprint minutiae template to be constructed. An enrollment process gathers multiple fingerprint images, creates fingerprint minutiae templates corresponding to the acquired fingerprint images, and stores the templates and other data associated with the respective individual or the enrolled fingerprint in a fingerprint database. In an identification process, an unknown raw fingerprint image is obtained via a fingerprint scanner and processed similarly to the enrollment process such that the fingerprint minutiae template of the unknown fingerprint is compared to one or more previously enrolled fingerprint minutiae templates. The identity of the individual associated with the unknown fingerprint is thereby ascertained. In addition, live finger detection can be accomplished in conjunction with the verification or identification process through analysis of the fingerprint image thus enhancing the security of the overall system.

A method for fingerprint recognition, the method comprising the steps of:

acquiring an enrolled fingerprint having a plurality of ridge curves and valleys;
blocking the enrolled fingerprint to form a blocked enrolled fingerprint;
separating enrolled foreground blocks from enrolled background blocks of the blocked enrolled fingerprint thereby forming an enhanced enrolled image;
determining for each of the enrolled foreground blocks in the enhanced enrolled image the corresponding orientation angle and amplitude, thereby forming an orientation field of the enrolled fingerprint;
extracting the minutiae from the orientation field of the enrolled fingerprint by
dividing each of the enrolled foreground blocks into a plurality sub-blocks,
creating a core mask,
convolving each of the sub-blocks of the enrolled foreground blocks with the core mask,
normalizing the results of the convolution of each of the sub-blocks of the enrolled foreground blocks with the core mask,
estimating the curvature in each sub-block as proportional to the convolution of the respective sub-block,
determining Poincare indices of sub-blocks having a curvature that is greater than a predetermined curvature threshold,
grouping the sub-blocks having a curvature that is greater than a predetermined curvature threshold according to the corresponding Poincare index,
identifying the sub-blocks having a curvature that is greater than a predetermined curvature threshold as cores and deltas according to the corresponding Poincare index,
if the estimate of the curvature of a sub-block exceeds a predetermined curvature threshold, surrounding the respective sub-block with a closed curve and calculating the direction integration of the closed curve, and
if the calculated direction integration is substantially zero then reducing the diameter of the closed curve and recalculating the direction integration and continue reducing the diameter of the closed curve until the value of the direction integration is non-zero;
creating an enrolled fingerprint template of the enrolled fingerprint from the extracted minutiae;
and storing the enrolled fingerprint template in a database.

14 more elements

Facts

Application

10/156,447Filed 28 May 2002 · us provisional application

Priority

25 May 2001

Published

US 2003/0039382 A127 Feb 2003

Granted

5 Apr 2005

Term ends

Not estimatedThe source records don’t support an estimate.

Classification6 codes

G06V 40/1353
Preprocessing; Feature extraction: extracting features related to minutiae or pores
G06V 10/993
Detection or correction of errors, e.g. by rescanning the pattern or by human intervention; Evaluation of the quality of the acquired patterns:…
G06V 40/1347
Fingerprints or palmprints: preprocessing; Feature extraction
G06V 40/1359
Preprocessing; Feature extraction: extracting features related to ridge properties; Determining the fingerprint type, e.g. whorl or loop

Abstract

Claims68