WO1995016974B1 - Method of automated signature verification - Google Patents

Method of automated signature verification

Info

Publication number
WO1995016974B1
WO1995016974B1 PCT/US1994/014588 US9414588W WO9516974B1 WO 1995016974 B1 WO1995016974 B1 WO 1995016974B1 US 9414588 W US9414588 W US 9414588W WO 9516974 B1 WO9516974 B1 WO 9516974B1
Authority
WO
WIPO (PCT)
Prior art keywords
signature
features
signatures
responsive
pixels
Prior art date
Application number
PCT/US1994/014588
Other languages
French (fr)
Other versions
WO1995016974A1 (en
Filing date
Publication date
Application filed filed Critical
Priority to EP95908421A priority Critical patent/EP0737342A1/en
Priority to AU16747/95A priority patent/AU1674795A/en
Priority to JP7516998A priority patent/JPH09506730A/en
Publication of WO1995016974A1 publication Critical patent/WO1995016974A1/en
Publication of WO1995016974B1 publication Critical patent/WO1995016974B1/en

Links

Abstract

A method of signature verification in which a test signature entered by an operator (204) may be preprocessed and examined for test features. The test features may be compared against features of a set of template signatures (206), and verified in response to the presence or absence of the test features in the template signatures (207). The test signature may be preprocessed, so as to normalize it and remove artifacts which are irrelevant to verification (205). The features of the template signatures may be stored in an associative memory or a data structure with associative memory capabilities (203).

Claims

95/16974 AMENDED CLAIMS PCT/US94/ 14588[received by the International Bureau on 10 July 1995 (10.07.95); original claims 5-8, 13 and 14 cancelled; new claims 2 and 10 added; remaining claims amended; claims renumbered 1-23 (9 pages)]
1. A method of automated signature verification, said method comprising
receiving a plurality of first signals, each representative of a template signature; transforming each said first signal into a first structure representative of said
template signature; normalizing each said first structure, responsive to spatial features of said first structure, to generate a first normalized signature; defining a first set of signature features responsive to each one of said plurality of first normalized signatures; receiving a second signal, representative of a test signature; transforming said second signal into a second structure representative of said test signature; normalizing said second structure, responsive to spatial features of said second structure, to generate a second normalized signature; defining a second set of signature features responsive to said second normal- ized signature;
comparing said second set of signature features with each one of said recorded
first set of signature features; and
verifying said test signature responsive to a result of said step of comparing.
2. A method as in claim 1, wherein said step of comparing comprises the
step of computing a Levenshtein distance.
3. A method as in any previous claim, wherein said step of comparing
comprises
• 44 T selecting a set of pairs of said first normalized signatures; computing a measure of distance for each said selected pair; and selecting an acceptance threshold, responsive to said computed measures of
distance for said first normalized signatures; for said second normalized signature, computing said measure of distance from
each said first normalized signature; defining a minimum distance for said computed measures of distance for said second normalized signature; and
determining whether said minimum distance exceeds said acceptance threshold.
4. A method as in claim 3, wherein said step of selecting an acceptance
threshold comprises
sorting said computed measures of distance for said first normalized signatures into by magnitude, to generate an ordered list of computed measures; selecting one said computed measure in said ordered list, responsive to a posi¬ tion on said ordered list for said computed measure; and
defining said acceptance threshold responsive to said selected one said com¬
puted measure.
5. A method as any previous claim, wherein said step of recording said first set of signature features comprises the step of storing a bit vector associated with said signature features in an associative memory or
in a data structure with associative memory capabilities; and
45
AMEJfoEO SHEET (ARTICLE 19) wherein said step of comparing comprises the step of retrieving at least a part of said bit vector from said associative memory or data structure with associative memory ca¬ pabilities.
6. A method as in any previous claim, wherein said step of recording said first set of signature features comprises the step of defining a set of weights for a discrete Hopfield artificial neural network weight matrix, responsive to a bit vector associated with said signature features; and wherein said step of comparing comprises the step of retrieving at least a part of said bit vector from said discrete Hopfield artificial neural network weight matrix.
7. A method as in any previous claim, wherein said step of defining a first set of signature features comprises constructing a binary vector for a first signature represent¬ ing said signature features for said first signature, wherein said binary vector constitutes less than about 300 bits.
8. A method as in any previous claim, wherein said first structure com¬ prises a plurality of pixels, each said pixel having spatial information associated therewith, and wherein said step of normalizing each said first structure comprises for a first said pixel, defining at least one second pixel as a neighborhood for said first pixel; modifying said spatial information associated with said first pixel, responsive to said spatial information associated with at least one pixel in said neighborhood;
computing a distance measure for said first pixel, responsive to at least one pixel in said neighborhood; and
46 repeating said steps of defining and modifying, responsive to said distance
measure.
9. A method as in any previous claim, wherein said first structure com- prises a plurality of pixels, each said pixel having spatial information associated therewith, and wherein said step of normalizing each said first structure comprises dividing said first structure into a plurality of segments; computing a measure of dispersion for each said segment; defining, for a first said segment, a set of retained pixels less than all pixels in said first segment, responsive to said measure of dispersion for said first segment; computing a regression line responsive to said retained pixels; and rotating said first signature responsive to said regression line.
10. A method as in any previous claim, wherein said first structure com- prises a plurality of pixels, each said pixel having spatial information associated therewith, and wherein said step of normalizing each said first structure comprises scaling said spatial infor¬ mation uniformly for the entire said first structure.
11. A method as in any previous claim, wherein said first structure com- prises a plurality of pixels, each said pixel having writing information associated therewith, and wherein said step of defining a first set of signature features comprises dividing said first structure into a plurality of segments; computing a summary measure for each said segment, responsive to a set of pixels in said segment; quantizing said summary measure for each said segment to generate a quan¬ tized measure, wherein said quantized measure comprises fewer than nine bits; and constructing a bit vector comprising said quantized measures; wherein said writing information is in the set composed of a set of pen-up/pen-down status bits, at known time intervals, of said writing
implement; a set of measures of pixel dispersion, at known time intervals, of said first set of
signatures; a euclidean coordinate map of at least one of said first set of signatures; a polar coordinate map of at least one of said first set of signatures; and a set of stroke turning positions in a euclidean coordinate map of at least one of said first set of signatures.
12. A method as in any previous claim, wherein said step of identifying a second set of features comprises recording data about at least three features in the set com¬ posed of a set of pen-up/pen-down status bits, at known time intervals, of said writing implement; a set of measures of pixel dispersion, at known time intervals, of said first set of signatures; a euclidean coordinate map of at least one of said first set of signatures; a polar coordinate map of at least one of said first set of signatures; and a set of stroke turning positions in a euclidean coordinate map of at least one of said first set of signatures.
48
13. A method as in any previous claim, wherein said step of comparing
comprises inputting said second set of features to an artificial neural network, said artifi- cial neural network comprising a set of weights generated in response to said recorded data; and generating an output from said artificial neural network.
14. A method as in any previous claim, wherein said step of comparing comprises inputting said second set of features to an artificial neural network, said artifi¬ cial neural network comprising a set of weights generated in response to a Hopfield weight matrix; and generating an output from said artificial neural network.
15. A method as in any previous claim, wherein said step of comparing comprises the step of generating a difference value in response to a set of differences between said first and second set of features; said step if generating comprises the step of determining a difference threshold in response to said first set of features, and the step of comparing said difference value with said difference threshold.
16. A method as in any previous claim, wherein said step of verifying com¬
prises the steps of
49 associating a set of authorization privileges with at least one said template sig¬ nature; identifying said test signature with at least one said template signature; and verifying said test signature responsive to said set of authorization privileges
associated with said at least one template signature.
17. A method of smoothing an input signature, comprising the steps of determining a time sequence of points in response to said input signature; altering each point in response to a predecessor point and a successor point, for substantially every point in said time sequence; determining a measure of smoothness of said time sequence after said step of altering; and repeating said step of altering in response to said measure of smoothness.
18. A method for rotating an input signature to a known axis line, com¬ prising
selecting a set of pixels to represent an input signature, which set of pixels con¬ sists of fewer than all the pixels of the signature; generating a line representing an axis of said selected set of pixels; determining an angle between said line and a known axis line; and rotating all the pixels of said signature through said angle.
19. A method as in claim 18, wherein said step of selecting comprises dis¬ carding statistical outlier pixels.
50
20. A method as in any one of claims 18-19, wherein said step of generating a line comprises regression over said selected set of pixels.
21. A method as in any one of claims 18-20, wherein said step of selecting
comprises allocating the pixels of the signature into a set of bins; determining a statistical measure of pixels in each such bin; selecting a subset of such bins in response to said statistical measures.
22. A data structure for recording a set of features of a written signature, comprising means for recording at least four features in the set composed of a set of pen-up/pen-down status bits, at known time intervals, of said writing implement; a set of measures of pixel dispersion, at known time intervals, of said first set of signatures; a euclidean coordinate map of at least one of said first set of signatures; a polar coordinate map of at least one of said first set of signatures; and a set of stroke turning positions in a euclidean coordinate map of at least one of said first set of signatures.
23. A method of determining whether a first person is a particular individ¬ ual, said method comprising
51 creating and storing signature features representing selected characteristics of a plurality of template signatures for the particular individual, said template signatures having been normalized responsive to spatial features thereof; providing information representing said selected characteristics of a test signa- ture made on a signature transducer by said first person, said test signature having been nor¬ malized responsive to spatial features thereof; comparing said selected characteristics of said test signature with said selected characteristics of each one of said template signatures; and generating a signal indicating a match if said selected characteristics of said test signature and said selected characteristics of said template signature match each other to a se¬ lected degree.
52 STATEMENT UNDER ARTICLE 19
Claims 1 and 23 recite comparing the test signature against each one of a plurality of template signatures. In Crane, only a single template signature is compared. In Fan, only a sin¬ gle "most typical" template signature is compared, and a comparison is performed for a computed "mean" signature. This recited feature involves an inventive step, in that it is possible to accept greater variance in the template signatures without excessively relaxing a comparison threshold for the test signature.
Dependent claims also recite features not shown or suggested by any cited art:
Claim 3 recites computing a measure of distances between each template signature, and determining an acceptance threshold responsive to those measures. Claim 4 recites that the acceptance threshold is selected from the inter-template signature distances. Claim 2 recites that comparisons use a Levenshtein distance measure.
Claim 5 recites storing a bit vector associated with said signature features in an associative memory or in a data structure with associative memory capabilities; claim 6 recites a similar step using a Hopfield matrix.
Although US A 5,299,269 (Gaborski et al.) shows an optical character recognition device which uses an associative memory, this feature of Gaborski is not capable of application to Crane or Fan. In Gaborski, the neural network simply determines if a particular set of pixels, lo¬ cated in a movable window, represent a printed character or not. Thus, the comparison is with a predetermined set of printed characters; the neural network may be pre-trained to recognize those printed characters and nothing else.
An aspect of the invention is its ability to represent a signature in a compact data structure. Claim 7 recites constructing a binary vector for the features for each signature, wherein each said binary vector constitutes less than about 300 bits.
Claim 8 recites a method of smoothing signatures which is not shown or suggested in any cited art. Although Fan shows smoothing signatures, the method is quite different.
Claim 9 recites a method of rotating signature elements which is not shown or suggested in any cited art. Although Fan shows rotating individual "letters" using affine trans¬ forms, the method is quite different.
Claim 10 recites scaling the spatial information for an entire signature. Crane only shows scaling time information for signatures, and only as part of the direct comparison process. Fan only shows scaling individual letters, only as part of the comparison process or of computing the "mean" signature . This recited feature involves an inventive step, in that the scaled spatial
53 information is used prior to determining features of the invention, rather than as part of the com¬ parison process.
Claim 11 recites constructing bit vectors representing features of the template sig¬ natures responsive to these aspects of the signature signals: a set of pen-up/pen-down status bits, a set of measures of pixel dispersion, a euclidean coordinate map, a polar coordinate map, a set of stroke turning positions in a euclidean coordinate map. Claim 12 recites a similar step for the test signature.
/ / /
54
PCT/US1994/014588 1993-12-17 1994-12-14 Method of automated signature verification WO1995016974A1 (en)

Priority Applications (3)

Application Number Priority Date Filing Date Title
EP95908421A EP0737342A1 (en) 1993-12-17 1994-12-14 Method of automated signature verification
AU16747/95A AU1674795A (en) 1993-12-17 1994-12-14 Method of automated signature verification
JP7516998A JPH09506730A (en) 1993-12-17 1994-12-14 Automatic signature verification method

Applications Claiming Priority (2)

Application Number Priority Date Filing Date Title
US16965493A 1993-12-17 1993-12-17
US169,654 1993-12-17

Publications (2)

Publication Number Publication Date
WO1995016974A1 WO1995016974A1 (en) 1995-06-22
WO1995016974B1 true WO1995016974B1 (en) 1995-08-10

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Family Applications (1)

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Country Status (6)

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US (1) US5680470A (en)
EP (1) EP0737342A1 (en)
JP (1) JPH09506730A (en)
AU (1) AU1674795A (en)
CA (1) CA2179302A1 (en)
WO (1) WO1995016974A1 (en)

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