Patent US6901155B2 successfully refreshed from Google Patents.
US6901155B2
Expired / Lapsed
US
⏱️ 0.0 years remaining
Wavelet-enhanced automated fingerprint identification system
Assignee: National University of Singapore
•
Filing: 2000-12-22
•
Granted: 2005-05-31
•
Expires: 2020-12-22
Abstract
Abstract A method and system for performing automated biological identification. The system including a preprocessing module with a histogram transform for locally and globally enhancing biological data such as fingerprints. An enhancement module with a fast smoothing and enhancement function. A feature extraction module with a fingerprint oriented thinning function. A matching module with a resolution-enhanced Hough transform function for fingerprint registration and matching score function.
Inventors
- 👤 Tao Xia
- 👤 Bao Liu
- 👤 Jo Yew Tham
- 👤 Lixin Shen
- 👤 Seng Luan Lee
Bibliographic Details
Application Number
—
Jurisdiction
US
Data Source
google_patents
Last Synchronized
2026-11-09 16:49
CPC Classifications
G; G06; G06V; G06V40/00; G06V40/10; G06V40/12; G06V40/1347; G06V40/1365
Claims Analysis
Total Claims: 13
Claim 1
Independent Claim
109 words
A finger print minutiae extraction method comprising: acquiring fingerprint image data; partitioning said fingerprint image data into at least one data block corresponding to a local area of said image data; generating a histogram function of a contrast level of said image data corresponding to said at least one data block, wherein a histogram of pixel intensity on a pixel by pixel basis is generated for said at least one data block; performing a histogram transformation of said histogram function, wherein said histogram transformation is adapted to the contrast level of said local area of said fingerprint image data and pre-enhanced fingerprint image data is generated with local enhancement.
Claim 2
Dependent Claim
109 words
A finger print minutiae extraction method comprising: acquiring fingerprint image data; partitioning said fingerprint image data into at least one data block corresponding to a local area of said image data; generating a histogram function of a contrast level of said image data corresponding to said at least one data block, wherein a histogram of pixel intensity on a pixel by pixel basis is generated for said at least one data block; performing a histogram transformation of said histogram function, wherein said histogram transformation is adapted to the contrast level of said local area of said fingerprint image data and pre-enhanced fingerprint image data is generated with local enhancement.
Claim 3
Dependent Claim
93 words
The method of claim 1 , further comprising: partitioning said fingerprint image data into a plurality of data blocks, each of said plurality of blocks corresponding to a different local area of said image data and at least one of said plurality blocks having a contrast level different than a second of said plurality of data blocks, wherein said histogram transformation is adapted to said different contrast levels of said plurality of blocks and pre-enhanced fingerprint image data is generated with local enhancement for a plurality of local areas of said image data.
Claim 4
Dependent Claim
39 words
The method of claim 1 , wherein said histogram transformation includes using an objective function with a relatively high value at both endpoints of an intensity interval and a relatively low value at a middle of said intensity interval.
Claim 5
Dependent Claim
16 words
The method of claim 1 , wherein noise and distortions in said image data are reduced.
Claim 6
Dependent Claim
53 words
The method of claim 1 , wherein said histogram transform maps said histogram function to a specific function according to a mapping algorithm including wherein f(x) is a target histogram function and said target histogram function has low value at the mid-point and has a high value at the endpoint of the interval.
Claim 7
Dependent Claim
33 words
The method of claim 1 , further comprising: performing orientation filtering on said pre-enhanced data using directional convolution for two dimensional digital image processing, wherein said pre-enhanced image data is smoothed and enhanced.
Claim 8
Dependent Claim
16 words
The method of claim 6 , wherein the following algorithm is used in said orientation filtering.
Claim 9
Dependent Claim
42 words
The method of claim 1 , further comprising: thinning said fingerprint image data to remove false connections of ridges in said data, wherein said thinning includes applying a first table and a second table to a plurality of pixels using an algorithm.
Claim 10
Dependent Claim
24 words
The method of claim 1 , further comprising generating a first table and a second table using rules for character data and biological data.
Claim 11
Dependent Claim
176 words
The method of claim 9 , wherein said rules for biological data include If P 1 *P 7 *P 8 =1 and P 2 +P 6 >0 and P 3 +P 5 =0 then LUT 1 (P)=0; If P 5 *P 6 *P 7 =1 and P 4 +P 8 >0 and P 1 +P 3 =0 then LUT 1 (P)=0; If P 1 *P 2 *P 3 =1 and P 4 +P 8 >0 and P 5 +P 7 =0 then LUT 2 (P)0; and If P 3 *P 4 *P 5 =1 and P 2 +P 6 >0 and P 1 +P 7 =0 then LUT 2 (P)=0, wherein A(P) is a number of 0-1 patterns in an order set P 1 , P 2 , P 3 , P 4 , P 5 , P 6 , P 7 , P 8 , P 1 , where Pi, i=1, . . . , 8, are 8-neighbors of a pixel in a clockwise direction, and B(P) is a number of nonzero neighbors of P.
Claim 12
Dependent Claim
88 words
A finger print minutiae extraction method comprising: acquiring fingerprint image data; partitioning said fingerprint image data into at least one data block corresponding to a local area of said image data; generating a histogram function of a contrast level of said image data corresponding to said at least one data block; performing a histogram transformation of said histogram function, wherein said histogram transformation is adapted to the contrast level of said local area of said fingerprint image data and pre-enhanced fingerprint image data is generated with local enhancement.
Claim 13
Dependent Claim
88 words
A finger print minutiae extraction method comprising: acquiring fingerprint image data; partitioning said fingerprint image data into at least one data block corresponding to a local area of said image data; generating a histogram function of a contrast level of said image data corresponding to said at least one data block; performing a histogram transformation of said histogram function, wherein said histogram transformation is adapted to the contrast level of said local area of said fingerprint image data and pre-enhanced fingerprint image data is generated with local enhancement.
References Cited (Backward Citations)
US5799098A
Cited
US5832102A
Cited
US5892838A
Cited
US5978495A
Cited
US5822453A
Cited
US6289112B1
Cited
US6597802B1
Cited
USPTO Annuity & Maintenance Windows
| Annuity Stage | Window Opens | Payment Deadline | Surcharge Period Until | Estimated Fee | Status |
|---|---|---|---|---|---|
| 3.5 Years (Year 4) | 2008-05-30 | 2008-11-29 | 2009-05-31 | $2000.00 | Window Closed / Lapsed |
| 7.5 Years (Year 8) | 2012-05-30 | 2012-11-29 | 2013-05-31 | $3760.00 | Window Closed / Lapsed |
| 11.5 Years (Year 12) | 2016-05-30 | 2016-11-29 | 2017-05-31 | $7700.00 | Window Closed / Lapsed |