CN102262730B - Fingerprint matching method based on multiple reference point pairs - Google Patents

Abstract

The invention discloses a fingerprint matching method based on multiple reference point pairs, which comprises the following steps: determining template fingerprints and input fingerprints, acquiring the matching scores of every detailed point pair by a fingerprint matching algorithm based on three-value characteristic vector, and ranking all the detailed point pairs according to the matching scores in a descending order; selecting the first m detailed point pairs as the initial reference point pairs, and computing the rotation/translation parameters of the initial reference point pairs; selecting the reference point pairs according to the conformity, carrying out global matching by using each reference point pair as the reference to acquire a matched detailed point pair set, and fusing information of all the matched detailed point pair sets by using a voting fusion strategy; and acquiring the number of final matched detailed point pairs, computing the matching scores according to the number of final matched detailed point pairs, and judging whether the template fingerprints are matched with the input fingerprints according to the matching scores and the matching threshold. The invention overcomes and solves the problem of nonlinear deformation of fingerprints in the existing fingerprint matching algorithm, and has favorable fingerprint matching performance.

Description

A kind of based on the right finger print matching method of multiple reference points

Technical field

The present invention relates to automatic fingerprint recognition field, specifically proposed a kind of based on the right finger print matching method of multiple reference points.

Background technology

The last stage of (fingerprint collecting, fingerprint pre-service and the fingerprint matching) processing stage that fingerprint matching being three of automatic algorithm for recognizing fingerprint is one of committed step of decision algorithm performance.The problem that fingerprint matching will solve is that the characteristic information that extracts from two given fingerprint images is carried out measuring similarity, judges that finally whether these two pieces of fingerprints are from same finger.

The realization of fingerprint matching algorithm and performance all are closely related with the fingerprint characteristic of choosing.Fingerprint characteristic can be divided into global characteristics usually, local feature and fine feature.Global characteristics such as texture information, central point (being commonly called as the Core point) and trigpoint (being commonly called as the Delta point); Local feature comprises end points, bifurcation (Bifurcation), isolated point, ring, island, burr and bridge etc., and fine feature is like the characteristics such as pore on the fingerprint that extracts at the fingerprint image that obtains from high resolution sensor.Different according to the characteristic of choosing, fingerprint matching mainly is classified as figure coupling, texture coupling, streakline coupling and minutiae point coupling etc.Wherein minutiae point matching process expression way is simple, has made full use of the difference of fingerprint image on minutia, and matching accuracy is higher, thereby has obtained widespread use.However, to still have some problems to solve perfect for existing minutiae point matching algorithm.In existing fingerprint minutiae matching process; Mostly utilize the information such as position, direction, type and minutiae point density of minutiae point during feature selecting; Structure can characterize the proper vector or the neighbour structure of minutiae point characteristic, and then the matching problem of fingerprint is converted into the measuring similarity problem of proper vector or neighbour structure.Wherein, a key factor that influences the minutiae point matching performance is exactly the non-linear deformation problems of fingerprint image.

Be embodied in: because there is non-linear deformation in fingerprint; When be RP with a pair of minutiae point to the time; Big more from RP to minutiae point far away more right position difference and direction difference; When difference acquires a certain degree, the minutiae point that originally can mate to maybe be because gap be excessive can't Satisfying Matching Conditions, refuse to know thereby possibly cause.Existing most of fingerprint minutiae matching algorithm has all been ignored this problem.Important process step as automatic algorithm for recognizing fingerprint system; The non-linear deformation problems of fingerprint image should fully be paid attention in the fingerprint matching; A good minutiae point matching algorithm; Should be able to take into full account and handle the non-linear deformation problems of fingerprint, thereby eliminate the right erroneous matching of minutiae point that non-linear deformation causes, improve the minutiae point matching performance.

Summary of the invention

Technical matters to be solved by this invention is the non-linear deformation problems of fingerprint that exists in the existing fingerprint matching algorithm in order to overcome; Provide a kind of based on the right minutiae point matching process of multiple reference points; This method has not only solved the non-linear deformation problems of fingerprint image to a certain extent, and has good fingerprint matching performance.

For solving the problems of the technologies described above, technical scheme of the present invention is: a kind of based on the right finger print matching method of multiple reference points, it is characterized in that comprising step:

1) confirms template fingerprint and input fingerprint;

2) adopt fingerprint matching algorithm to obtain template fingerprint and import all minutiae point of fingerprint to matching score based on three value tags vector;

3) with all minutiae point to by the matching score descending sort, choose the forward m of score right as initial reference point;

4) calculate the right rotation translation parameters of initial reference point, the minutiae point of choosing correct match according to consistance is right to being RP;

5) with every pair of RP to being benchmark, carry out the global registration of template fingerprint and input fingerprint, obtain coupling minutiae point pair set;

6) adopt the ballot convergence strategy, merge all coupling minutiae point pair set information, obtain final matching minutiae point logarithm;

7) calculate matching score according to final matching minutiae point logarithm;

8), judge whether template fingerprint and input fingerprint mate based on matching score and matching threshold.

Of the present invention based on the right finger print matching method of multiple reference points when carrying out fingerprint matching, need obtain the right feature selecting of many correct RP earlier:

Of the present inventionly adopt fingerprint matching algorithm (the fingerprint matching algorithm [Zhang Liming of Liming Zhang based on three value tags vector based on three value tags vector based on the right finger print matching method of multiple reference points; Yin Yilong. based on fingerprint matching algorithm [C] the .Chinese Conference on Pattern Recognition of three value tags vector; 2009; 563-567.]) and rotation translation parameters consistance to choose the details reference point of many correct couplings right; Adopt the ballot convergence strategy according to a plurality of coupling minutiae point pair set information of a plurality of RPs afterwards, obtain the fingerprint matching result obtaining.Should solve the non-linear deformation problems of fingerprint based on the right minutiae point matching algorithm of multiple reference points.Fingerprint minutiae each item information definition that coupling is used is following:

A. minutiae point type

General minutiae point is divided into end points and bifurcation, and the minutiae point type refers to that minutiae point belongs to end points or bifurcation.The minutiae point type of correct match should be consistent.Minutiae point type ω defined formula is following:

Wherein, when ω=0, represent that this minutiae point is an end points; When ω=1, represent that this minutiae point is a bifurcation.

B. minutiae point density

Minutiae point density i.e. the interior number of minutiae point on every side of the certain neighborhood of this minutiae point, and selecting R here is the neighborhood of radius.Because the minutiae point information and the fingerprint quality of fingerprint image are closely related, therefore density is defined as one three value vector, promptly sparse, general, intensive, use-1,0,1 expression respectively.The density σ defined formula of minutiae point is following:

$\mathrm{\&sigma;}=\left\{\begin{array}{cc}-1,& \mathrm{num}<t-\mathrm{\&Delta;}\\ 0,& t-\mathrm{\&Delta;}<\mathrm{num}<t+\mathrm{\&Delta;}\\ 1,& \mathrm{num}>t+\mathrm{\&Delta;}\end{array}\right.---\left(2\right)$

Wherein, num be in the minutiae point R radius circle territory around the number of minutiae point, the threshold value of the neighborhood minutiae point number that t confirms for experiment, Δ is the error of permission.When σ=-1, represent that this minutiae point density is less, belong to the sparse details point; When σ=0, expression minutiae point density is general; When σ=1, expression minutiae point density is bigger, belongs to intensive minutiae point.

C. minutiae point dispersion

Minutiae point dispersion δ is the center with this minutiae point promptly, R be in the neighborhood of radius around minutiae point to the distance of this minutiae point with average

$\mathrm{\&delta;}=\left\{\begin{array}{cc}-1,& \frac{\underset{i=1}{\overset{n}{\mathrm{\&Sigma;}}}{d}_i}{n}<\frac{R}{2}-d_0\\ 0,& \frac{R}{2}-d_0<\frac{\underset{i=1}{\overset{n}{\mathrm{\&Sigma;}}}{d}_i}{n}<\frac{R}{2}+d_0\\ 1,& \frac{\underset{i=1}{\overset{n}{\mathrm{\&Sigma;}}}{d}_i}{n}>\frac{R}{2}+d_0\end{array}\right.---\left(3\right)$

Wherein, d _iBe the distance of i minutiae point minutiae point to the center, n is the number of minutiae point in the scope, and R is the radius in circle territory, d ₀Be constant threshold, confirm by experiment.When δ=-1, represent this minutiae point around minutiae point nearer from the mean distance of this minutiae point, promptly the dispersion of this minutiae point is less; When δ=0, represent that this minutiae point dispersion is placed in the middle; When δ=1, represent this minutiae point around minutiae point far away from the mean distance of this minutiae point, promptly the dispersion of this minutiae point is bigger.

D. the streakline bending direction at minutiae point place

Utilize the difference of the angle of minutiae point direction and field of direction direction to represent the bending direction of streakline; Represent with λ; The minutiae point direction is meant the direction of line of starting point and the terminal point of streakline after the refinement, and field of direction direction is meant the direction of the field of direction of end points (being starting point), the positive and negative bending direction that reflects streakline of the angle of both direction; No matter how image rotates the minutiae point direction all the time in the inboard of crestal line, field of direction direction is all the time in the outside of crestal line.If field of direction direction deducts the value of minutiae point direction for just, represent the streakline downwarping, for negative, the expression streakline is bent upwards, otherwise streakline convergence level.

Wherein, Ω is an angle threshold.When λ=-1, represent then that streakline is bent upwards and be last arc; Near 0 o'clock, the acquiescence streakline was a horizontal direction; When λ=1, represent that then the streakline downwarping is arc down.

In the said step 3), to all minutiae point to according to matching score according to descending sort, be expressed as χ ₁, χ ₂..., χ _m..., χ _I+1, χ _I+2..., χ _I+n, forward more expression is that the right possibility of correct match minutiae point is big more, coming a most forward m minutiae point to (being χ ₁, χ ₂..., χ _m) right as initial reference point, χ wherein _iRepresent i minutiae point, m is the minutiae point number;

In the said step 4), the rotation translation parameters be to the input fingerprint with respect to the template fingerprint registration after the quantification of translational movement and rotation amount.Because angle, the position not equal factor of finger when fingerprint capturer becomes the shadow zone to push, the input fingerprint has certain rotation translational movement with respect to template fingerprint, and the rotation translation parameters promptly is the measurement to this amount.Calculating the right rotation translation parameters of initial reference point is: initial reference point centering, at first establish a minutiae point T (X of template fingerprint _T, Y _T, θ _T), X wherein _TThe horizontal ordinate of representation template fingerprint minutiae on fingerprint image, Y _TThe ordinate of representation template fingerprint minutiae on fingerprint image, θ _TThe direction of representation template fingerprint minutiae on fingerprint image, and θ _T∈ (0, π), establish input fingerprint one minutiae point I (X again _I, Y _I, θ _I), X wherein _IThe horizontal ordinate of expression input fingerprint minutiae on fingerprint image, Y _IThe ordinate of expression input fingerprint minutiae on fingerprint image, θ _IThe direction of expression input fingerprint minutiae on fingerprint image, and θ _I∈ (0, π); Then minutiae point is to (T; I) rotation translation parameters is (Δ X, Δ Y, Δ θ); (Δ X wherein; Δ Y) expression input fingerprint minutiae is with respect to the offset of template fingerprint minutiae point on horizontal ordinate, and Δ θ representes to import fingerprint minutiae with respect to the skew of template fingerprint minutiae point direction, and each item component computing formula of rotation translation parameters is following:

Δθ＝θ _I-θ _T (5)

ΔX＝X _I×cos(Δθ)+Y _I×sin(Δθ)-X _T (6)

ΔY＝-X _I×sin(Δθ)+Y _I×cos(Δθ)-Y _T (7)

Minutiae point corresponding on input fingerprint and the template fingerprint same position is to AB (Δ X _AB, Δ Y _AB, Δ θ _AB) and CD (Δ X _CD, Δ Y _CD, Δ θ _CD) between Euclidean distance Dis (AB, CD) formula does

$\mathrm{Dis}(\mathrm{AB},\mathrm{CD})=\sqrt{({({\mathrm{\&Delta;X}}_{\mathrm{AB}}-{\mathrm{\&Delta;X}}_{\mathrm{CD}})}^2+{({\mathrm{\&Delta;Y}}_{\mathrm{AB}}-{\mathrm{\&Delta;Y}}_{\mathrm{CD}})}^2+{({\mathrm{\&Delta;\&theta;}}_{\mathrm{AB}}-{\mathrm{\&Delta;\&theta;}}_{\mathrm{CD}})}^2)}---\left(8\right)$

These two pairs of minutiae point are to AB (Δ X _AB, Δ Y _AB, Δ θ _AB) and CD (Δ X _CD, Δ Y _CD, Δ θ _CD) have rotation translation parameters consistance and then should satisfy following formula

Dis(AB，CD)＜Ψ (9)

Wherein Ψ is apart from experimental threshold values.According to formula (5)-(8), calculate right rotation translation parameters of initial reference point and the Euclidean distance between parameter like this, according to formula (9), the minutiae point that utilization rotation translation parameters consistance is chosen correct match is right to putting as a reference.

In the said step 5) template fingerprint with the input fingerprint global registration for respectively with every pair of RP to being reference point; Utilization is carried out template fingerprint and the global registration of importing fingerprint based on the fingerprint matching algorithm of three value tags vector with rotation translation parameters consistance; Wherein satisfy three value tags vector matching condition and the conforming minutiae point of rotation translation parameters simultaneously to being that correct coupling minutiae point is right, all obtain the coupling minutiae point pair set of a correspondence to every pair of RP.

Afterwards, right coupling minutiae point set is merged to RP:

In said step 6), adopt the ballot convergence strategy, to the fusion of voting, obtain the final minutiae point coupling logarithm PairNum of template fingerprint and input fingerprint, to all coupling minutiae point if the right votes T＞T of certain minutiae point ₀, then think this minutiae point to being that the minutiae point of correct match is right, i.e. PairNum=PairNum+1, wherein, T ₀Relevant with the number of coupling set, be experimental threshold values.

In the said step 7), according to final minutiae point coupling logarithm, the final score Socre of calculation template fingerprint and input fingerprint, Socre ∈ [0,100] wherein, computing formula is following:

$\mathrm{Socre}=\frac{\mathrm{PairNum}}{\sqrt{M*N}}\&times;100---\left(10\right)$

Wherein, M, N are respectively the minutiae point number of template fingerprint and input fingerprint.

In the said step 8),, judge whether template fingerprint and input fingerprint mate according to matching score and matching threshold; When Socre >=μ; Then think not match two width of cloth fingerprint successful match otherwise be regarded as two width of cloth fingerprints, wherein μ is matching threshold; Matching threshold can be adjusted the specific requirement of reject rate and misclassification rate according to practical application, generally gets 55.

It is a kind of based on the right minutiae point matching process of multiple reference points that this patent proposes, and the minutiae point of choosing many correct couplings is right to putting as a reference, respectively with each to RP to being benchmark; Carry out the fingerprint global registration, obtain a plurality of coupling minutiae point pair sets, select the ballot convergence strategy; Promptly to the minutiae point of all appearance to combination; Maximum polls of voting are identical with the set number, and the ballot that certain a pair of minutiae point obtains is many more, explain that this minutiae point is big more to being the right possibility of the minutiae point of correct match; Otherwise more little, thereby guarantee the right accuracy of coupling minutiae point.Calculating the matching score stage; Set matching threshold; Judge the fingerprint matching result, multiple reference points to coupling made full use of many groups based on different, correct match point to the mutual relationship between a plurality of coupling minutiae point set that obtain, overcome the non-linear deformation problems of the fingerprint that exists in the existing fingerprint matching algorithm; Not only solve the non-linear deformation problems of fingerprint image to a certain extent, and had good fingerprint matching performance.

Description of drawings

Below in conjunction with accompanying drawing and embodiment the present invention is further specified:

Accompanying drawing is that the present invention is a kind of based on the right finger print matching method schematic diagram of multiple reference points.

Embodiment

Below in conjunction with accompanying drawing and embodiment, further set forth the present invention.Should be understood that these embodiment only to be used to the present invention is described and be not used in the restriction scope of the present invention.Should be understood that in addition those skilled in the art can do various changes or modification to the present invention after the content of having read the present invention's instruction, these equivalent form of values fall within the application's appended claims institute restricted portion equally.

Shown in accompanying drawing, technical scheme of the present invention is: a kind of based on the right finger print matching method of multiple reference points, comprise step:

1) confirms template fingerprint and input fingerprint;

2) adopt fingerprint matching algorithm to obtain template fingerprint and import all minutiae point of fingerprint to matching score based on three value tags vector;

3) with all minutiae point to by the matching score descending sort, choose the forward m of score right as initial reference point;

4) calculate the right rotation translation parameters of initial reference point, the minutiae point of choosing correct match according to consistance is right to being RP;

5) with every pair of RP to being benchmark, carry out the global registration of template fingerprint and input fingerprint, obtain coupling minutiae point pair set;

6) adopt the ballot convergence strategy, merge all coupling minutiae point pair set information, obtain final matching minutiae point logarithm;

7) calculate matching score according to final matching minutiae point logarithm;

8), judge whether template fingerprint and input fingerprint mate based on matching score and matching threshold.

Wherein, of the present invention based on the right finger print matching method of multiple reference points when carrying out fingerprint matching,

At first, obtain the right feature selecting of many correct minutiae point RP:

Fingerprint minutiae each item information definition of using is following

A. minutiae point type

General minutiae point is divided into end points and bifurcation,, minutiae point type ω defined formula is following:

Wherein, when ω=0, represent that this minutiae point is an end points; When ω=1, represent that this minutiae point is a bifurcation.

B. minutiae point density

Minutiae point density i.e. the interior number of minutiae point on every side of the certain neighborhood of this minutiae point, and selecting R (R generally gets 5 ridge distances, about 50 pixels) here is the neighborhood of radius.Minutiae point density is defined as one three value vector, promptly sparse, general, intensive, use-1,0,1 expression respectively.The density σ defined formula of minutiae point is following:

$\mathrm{\&sigma;}=\left\{\begin{array}{cc}-1,& \mathrm{num}<t-\mathrm{\&Delta;}\\ 0,& t-\mathrm{\&Delta;}<\mathrm{num}<t+\mathrm{\&Delta;}\\ 1,& \mathrm{num}>t+\mathrm{\&Delta;}\end{array}\right.---\left(2\right)$

Wherein, num (value of num is between 0-8) be in the minutiae point R radius circle territory around the number of minutiae point, the threshold value of the neighborhood minutiae point number that t (t generally gets 3) confirms for experiment, Δ (Δ generally gets 1) is the error of permission.When σ=-1, represent that this minutiae point density is less, belong to the sparse details point; When σ=0, expression minutiae point density is general; When σ=1, expression minutiae point density is bigger, belongs to intensive minutiae point.

C. minutiae point dispersion

Minutiae point dispersion δ is the center with this minutiae point promptly, R be in the neighborhood of radius around minutiae point to the distance of this minutiae point with average.

$\mathrm{\&delta;}=\left\{\begin{array}{cc}-1,& \frac{\underset{i=1}{\overset{n}{\mathrm{\&Sigma;}}}{d}_i}{n}<\frac{R}{2}-d_0\\ 0,& \frac{R}{2}-d_0<\frac{\underset{i=1}{\overset{n}{\mathrm{\&Sigma;}}}{d}_i}{n}<\frac{R}{2}+d_0\\ 1,& \frac{\underset{i=1}{\overset{n}{\mathrm{\&Sigma;}}}{d}_i}{n}>\frac{R}{2}+d_0\end{array}\right.---\left(3\right)$

Wherein, d _iBe the distance of i minutiae point minutiae point to the center, n (n is generally 4) is the number of minutiae point in the scope, and R (R generally gets 5 ridge distances) is the radius in circle territory, d ₀(d ₀Generally get 2 ridge distances) be constant threshold, confirm by experiment.When δ=-1, represent this minutiae point around minutiae point nearer from the mean distance of this minutiae point, promptly the dispersion of this minutiae point is less; When δ=0, represent that this minutiae point dispersion is placed in the middle; When δ=1, represent this minutiae point around minutiae point far away from the mean distance of this minutiae point, promptly the dispersion of this minutiae point is bigger.

D. the streakline bending direction at minutiae point place

Utilize the difference of the angle of minutiae point direction and field of direction direction to represent the bending direction of streakline, represent with λ, the value that field of direction direction deducts the minutiae point direction is being for just, the downwarping of expression streakline, and for negative, the expression streakline is bent upwards, otherwise streakline convergence level.

Wherein, Ω (Ω generally gets 5 degree) is angle threshold.When λ=-1, represent then that streakline is bent upwards and be last arc; Near 0 o'clock, the acquiescence streakline was a horizontal direction; When λ=1, represent that then the streakline downwarping is arc down.

E. rotate the translation parameters consistance

Calculating the right rotation translation parameters of initial reference point is: initial reference point centering, at first establish a minutiae point T (X of template fingerprint _T, Y _T, θ _T), X wherein _TThe horizontal ordinate of representation template fingerprint minutiae on fingerprint image, Y _TThe ordinate of representation template fingerprint minutiae on fingerprint image, θ _TThe direction of representation template fingerprint minutiae on fingerprint image, and θ _T∈ (0, π), establish input fingerprint one minutiae point I (X again _I, Y _I, θ _I), X _IWherein the horizontal ordinate of fingerprint minutiae on fingerprint image, Y are imported in expression _IThe ordinate of expression input fingerprint minutiae on fingerprint image, θ _IThe direction of expression input fingerprint minutiae on fingerprint image, and θ _I∈ (0, π); Then minutiae point is to (T; I) rotation translation parameters is (Δ X, Δ Y, Δ θ); (Δ X wherein; Δ Y) expression input fingerprint minutiae is with respect to the offset of template fingerprint minutiae point on horizontal ordinate, and Δ θ representes to import fingerprint minutiae with respect to the skew of template fingerprint minutiae point direction, and each item component computing formula of rotation translation parameters is following:

Δθ＝θ _I-θ _T (5)

ΔX＝X _I×cos(Δθ)+Y _I×sin(Δθ)-X _T (6)

ΔY＝-X _I×sin(Δθ)+Y _I×cos(Δθ)-Y _T (7)

Minutiae point corresponding on input fingerprint and the template fingerprint same position is to AB (Δ X _AB, Δ Y _AB, Δ θ _AB) and CD (Δ X _CD, Δ Y _CD, Δ θ _CD) between Euclidean distance Dis (AB, CD) formula does

$\mathrm{Dis}(\mathrm{AB},\mathrm{CD})=\sqrt{({({\mathrm{\&Delta;X}}_{\mathrm{AB}}-{\mathrm{\&Delta;X}}_{\mathrm{CD}})}^2+{({\mathrm{\&Delta;Y}}_{\mathrm{AB}}-{\mathrm{\&Delta;Y}}_{\mathrm{CD}})}^2+{({\mathrm{\&Delta;\&theta;}}_{\mathrm{AB}}-{\mathrm{\&Delta;\&theta;}}_{\mathrm{CD}})}^2)}---\left(8\right)$

These two pairs of minutiae point are to AB (Δ X _AB, Δ Y _AB, Δ θ _AB) and CD (Δ X _CD, Δ Y _CD, Δ θ _CD) have rotation translation parameters consistance and then should satisfy following formula

Dis(AB，CD)＜Ψ (9)

Wherein Ψ (Ψ approximately gets 20) is apart from experimental threshold values.According to formula (5)-(8), calculate right rotation translation parameters of initial reference point and the Euclidean distance between parameter like this, according to formula (9), the minutiae point that utilization rotation translation parameters consistance is chosen correct match is right to putting as a reference.

Carry out fingerprint matching afterwards:

1) confirms template fingerprint and input fingerprint;

2) employing obtains template fingerprint and imports all minutiae point of fingerprint to matching score based on the vectorial fingerprint matching algorithm of three value tags, and the characteristic formula that wherein matees use is shown in (1)-(4); Specific as follows: with the minutiae point type is example, if two minutiae point belong to same type, then the matching score of these two minutiae point adds 1; In like manner; For minutiae point density, if two minutiae point belong to the minutiae point of degree of the same race, promptly be both intensive minutiae point, be both general minutiae point or be both sparse details point then the matching score of two minutiae point add 1; Otherwise score is constant, continues relatively next characteristic;

3) with all minutiae point to by the matching score descending sort, be expressed as χ ₁, χ ₂..., χ _m..., χ _I+1, χ _I+2..., χ _I+n, forward more expression is that the right possibility of correct match minutiae point is big more, choosing the forward m of score (m generally gets 30) (is χ ₁, χ ₂..., χ _m) right as initial reference point; χ wherein _iRepresent i minutiae point, m is the minutiae point number;

4) calculate the right rotation translation parameters of initial reference point; Based on formula (5)-(8); Calculate right rotation translation parameters of initial reference point and the Euclidean distance between parameter,, utilize the minutiae point of rotation translation parameters uniformity correct match right being reference point based on formula (9);

5) with every pair of RP to being benchmark; Carry out the global registration of template fingerprint and input fingerprint; Utilization is carried out template fingerprint and the global registration of importing fingerprint based on the fingerprint matching algorithm of three value tags vector with rotation translation parameters consistance; Wherein satisfy three value tags vector matching condition and the conforming minutiae point of rotation translation parameters simultaneously to being that correct coupling minutiae point is right, all obtain the coupling minutiae point pair set of a correspondence to every pair of RP.

6) adopt the ballot convergence strategy, to the fusion of voting, obtain the final minutiae point coupling logarithm PairNum of template fingerprint and input fingerprint, if the right votes T＞T of certain minutiae point to all coupling minutiae point ₀, then think this minutiae point to being that the minutiae point of correct match is right, i.e. PairNum=PairNum+1, wherein, T is the actual votes that obtains, generally between 1-15; T ₀Relevant with the number of coupling set, be experimental threshold values, generally get the numerical value between the 6-15, according to knowing and miss the requirement of knowing and can dynamically adjust to refusing.

7) according to the final matching score Socre of final matching minutiae point logarithm calculation template fingerprint with the input fingerprint, Socre ∈ [0,100] wherein, computing formula is following:

$\mathrm{Socre}=\frac{\mathrm{PairNum}}{\sqrt{M*N}}\&times;100---\left(10\right)$

Wherein, M, N are respectively the minutiae point number of template fingerprint and input fingerprint, and the value of M, N is between 30-100;

8) according to matching score and matching threshold; Judge whether template fingerprint and input fingerprint mate, when Socre >=μ, then think two width of cloth fingerprint successful match; Otherwise being regarded as two width of cloth fingerprints does not match; Wherein μ is matching threshold, and matching threshold can be adjusted the specific requirement of reject rate and misclassification rate according to practical application, generally gets 55.

More than show and described ultimate principle of the present invention and principal character and advantage of the present invention.The technician of the industry should understand; The present invention is not restricted to the described embodiments; That describes in the foregoing description and the instructions just explains principle of the present invention; Under the prerequisite that does not break away from spirit and scope of the invention, the present invention also has various changes and modifications, and these variations and improvement all fall in the scope of the invention that requires protection.The present invention requires protection domain to be defined by appending claims and equivalent thereof.

All are from design of the present invention, and the structure conversion of having done without creative work all drops within protection scope of the present invention.

Claims (5)

1. one kind based on the right finger print matching method of multiple reference points, it is characterized in that comprising step:

1) confirms template fingerprint and input fingerprint;

2) adopt fingerprint matching algorithm to obtain template fingerprint and import all minutiae point of fingerprint to matching score based on three value tags vector;

3) with all minutiae point to by the matching score descending sort, choose the forward m of score right as initial reference point;

4) calculate the right rotation translation parameters of initial reference point, the minutiae point of choosing correct match according to consistance is right to being RP;

5) with every pair of RP to being benchmark, carry out the global registration of template fingerprint and input fingerprint, obtain coupling minutiae point pair set;

6) adopt the ballot convergence strategy, merge all coupling minutiae point pair set information, obtain final matching minutiae point logarithm;

7) calculate matching score according to final matching minutiae point logarithm;

8), judge whether template fingerprint and input fingerprint mate based on matching score and matching threshold;

Calculating the right rotation translation parameters of initial reference point in the said step 4) is: initial reference point centering, at first establish a minutiae point T (X of template fingerprint _T, Y _T, θ _T), X wherein _TThe horizontal ordinate of representation template fingerprint minutiae on fingerprint image, Y _TThe ordinate of representation template fingerprint minutiae on fingerprint image, θ _TThe direction of representation template fingerprint minutiae on fingerprint image, and θ _T∈ (0, π), establish input fingerprint one minutiae point I (X again _I, Y _I, θ _I), X wherein _IThe horizontal ordinate of expression input fingerprint minutiae on fingerprint image, Y _IThe ordinate of expression input fingerprint minutiae on fingerprint image, θ _IThe direction of expression input fingerprint minutiae on fingerprint image, and θ _I∈ (0, π); Then minutiae point is to (Τ; I) rotation translation parameters is (Δ X, Δ Y, Δ θ); (Δ X wherein; Δ Y) expression input fingerprint minutiae is with respect to the offset of template fingerprint minutiae point on horizontal ordinate, and Δ θ representes to import fingerprint minutiae with respect to the skew of template fingerprint minutiae point direction, and each item component computing formula of rotation translation parameters is following:

Δθ=θ _I-θ _T (5)

ΔX=X _I×cos(Δθ)+Y _I×sin(Δθ)-X _T (6)

ΔY=-X _I×sin(Δθ)+Y _I×cos(Δθ)-Y _T (7)

Minutiae point corresponding on input fingerprint and the template fingerprint same position is to AB (Δ X _AB, Δ Y _AB, Δ θ _AB) and CD (Δ X _CD, Δ Y _CD, Δ θ _CD) between Euclidean distance Dis (AB, CD) formula does

$\mathrm{Dis}(\mathrm{AB},\mathrm{CD})=\sqrt{({(\mathrm{\&Delta;}{X}_{\mathrm{AB}}-\mathrm{\&Delta;}{X}_{\mathrm{CD}})}^2+{(\mathrm{\&Delta;}{Y}_{\mathrm{AB}}-\mathrm{\&Delta;}{Y}_{\mathrm{CD}})}^2+{(\mathrm{\&Delta;}{\mathrm{\&theta;}}_{\mathrm{AB}}-{\mathrm{\&Delta;\&theta;}}_{\mathrm{CD}})}^2)}---\left(8\right)$

These two pairs of minutiae point are to AB (Δ X _AB, Δ Y _AB, Δ θ _AB) and CD (Δ X _CD, Δ Y _CD, Δ θ _CD) have rotation translation parameters consistance and then should satisfy following formula

Dis(AB,CD)<Ψ (9)

The minutiae point that utilization rotation translation parameters consistance is chosen correct match is right to putting as a reference, and wherein Ψ is apart from experimental threshold values.

2. as claimed in claim 1 a kind of based on the right finger print matching method of multiple reference points; It is characterized in that; In the said step 5) template fingerprint with the input fingerprint global registration for respectively with every pair of RP to being reference point; Utilization is carried out template fingerprint and the global registration of importing fingerprint based on the fingerprint matching algorithm of three value tags vector with rotation translation parameters consistance; Wherein satisfy three value tags vector matching condition and the conforming minutiae point of rotation translation parameters simultaneously to being that correct coupling minutiae point is right, all obtain the coupling minutiae point pair set of a correspondence to every pair of RP.

3. as claimed in claim 1 a kind of based on the right finger print matching method of multiple reference points; It is characterized in that; In the said step 6), adopt the ballot convergence strategy, mate minutiae point to the fusion of voting to all; Obtain the final minutiae point coupling logarithm PairNum of template fingerprint and input fingerprint, if the right votes T > of certain minutiae point; T ₀, then think this minutiae point to being that the minutiae point of correct match is right, i.e. PairNum=PairNum+1, wherein, T ₀Relevant with the number of coupling set, be experimental threshold values.

4. as claimed in claim 3 a kind ofly it is characterized in that based on the right finger print matching method of multiple reference points is in the said step 7); According to final minutiae point coupling logarithm, the final score Socre of calculation template fingerprint and input fingerprint, wherein Socre ∈ [0; 100], computing formula is following:

$\mathrm{Socre}=\frac{\mathrm{PairNum}}{\sqrt{M*N}}\&times;100---\left(10\right)$

Wherein, M, N are respectively the minutiae point number of template fingerprint and input fingerprint.

5. as claimed in claim 4 a kind ofly it is characterized in that based on the right finger print matching method of multiple reference points is in the said step 8); According to matching score and matching threshold, judge whether template fingerprint and input fingerprint mate, when Socre >=μ; Then think two width of cloth fingerprint successful match; Do not match otherwise be regarded as two width of cloth fingerprints, wherein μ is matching threshold, generally gets 55.

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