CN102521575A - Iris identification method based on multidirectional Gabor and Adaboost - Google Patents

Iris identification method based on multidirectional Gabor and Adaboost Download PDF

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CN102521575A
CN102521575A CN2011104217964A CN201110421796A CN102521575A CN 102521575 A CN102521575 A CN 102521575A CN 2011104217964 A CN2011104217964 A CN 2011104217964A CN 201110421796 A CN201110421796 A CN 201110421796A CN 102521575 A CN102521575 A CN 102521575A
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iris
gabor
iris image
formula
adaboost
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CN102521575B (en
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王琪
张祥德
单成坤
周军
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Beijing Techshino Technology Co Ltd
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    • G06V40/10—Human or animal bodies, e.g. vehicle occupants or pedestrians; Body parts, e.g. hands
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Abstract

The invention relates to an iris identification method based on multidirectional Gabor and Adaboost. The method comprises the following steps that: (1), block division is carried out on a normalized iris image and two-dimensional Gabor characteristics are extract to carry out coding; and a Hanmming distance between corresponding blocks is calculated; and (2), an Adaboost algorithm is used to carry out classification and identification on the block Hanmming distance obtained in the step (1). More particularly, in the characteristic extraction process, Gabor wavelets of eight directions under a same scale are employed; and block division is carried out on the expanded iris image; Gabor characteristics of the whole iris image and submodules of the iris image are simultaneously extracted by combining integral and local information of the iris and then coding is carried out; the whole and local combination is carried out to form a multi-dimensional characteristic vector; the Adaboost algorithm is introduced to carry out characteristic selection; and a classifier is constructed to carry out identification. According to the invention, beneficial effects of the method are as follows: a noise influence is reduced; an identification problem of a low quality iris image can be solved; and the identification performance is good.

Description

Based on multi-direction Gabor and Adaboost iris identification method
Technical field
The present invention relates to Digital Image Processing and pattern-recognition, relate in particular to a kind of iris identification method, belong to living things feature recognition and secure authentication technology field based on multi-direction Gabor and Adaboost.
Background technology
In modern society, sharply frequent along with the high development of network technology, flow of personnel, a kind of safe and reliable, convenient identity authorization system efficiently seems particularly important.Traditional identification mainly contains two kinds: sign article (key, identity document etc.) and sign knowledge (user name, password etc.).But deficiencies such as in practical application, its existence is prone to losing property, be prone to forgery property, nonuniqueness and range of application are less relatively make people press for a kind of personal identification method that can overcome above-mentioned defective.Under the driving of demand, arise at the historic moment based on the recognition technology of biological characteristics such as people's face, fingerprint, iris, hand shape, person's handwriting.
At present, a lot of iris authentication systems are arranged both at home and abroad.Wherein the iris authentication system realized of doctor Daugman based on Gabor be propose the earliest can practical application iris authentication system.In iris authentication system, at first to gather iris image, in the iris image that collects, cut apart iris region then, on iris image after the normalization, extract characteristic at last and mate.Wherein the iris feature extraction is one of principal element that influences system performance, and existing Algorithm of Iris Recognition is higher to the quality requirements of iris image mostly.Yet; The influence of factor such as the iris image that collects under the natural light can receive eyelashes, eyelid, illumination, rock; Cause the iris image quality that collects not good enough,, need a kind of effective iris feature to extract and recognizer to this type inferior quality iris image.
Summary of the invention
The purpose of this invention is to provide and a kind ofly have good recognition performance, overcome the deficiency of the above-mentioned aspect of prior art based on multi-direction Gabor and Adaboost iris identification method.
The objective of the invention is to realize through following technical scheme:
A kind of based on multi-direction Gabor and Adaboost iris identification method, it comprises the steps:
1) to the normalized iris image piecemeal and extract the two-dimensional Gabor characteristic, and the Hamming distance that calculates between the corresponding blocks leaves, and it specifically may further comprise the steps:
1.1) iris image that launches is divided into evenly M is capable, the N row, obtain M * N iris image submodule;
1.2) use the Gabor wave filter of eight directions of same yardstick to act on step 1.1) and in iris image submodule after the normalization, according to the Gabor real part the positive and negative of image filtering result encoded then; Wherein the expression formula of Gabor wave filter is following:
Figure 2011104217964100002DEST_PATH_IMAGE001
,
Figure 731301DEST_PATH_IMAGE002
,
Figure 20463DEST_PATH_IMAGE003
, Be the direction of Gabor wave filter,
Figure 890516DEST_PATH_IMAGE005
,
Figure 76909DEST_PATH_IMAGE006
, UWith VBe respectively the level and the vertical centre frequency of Gabor wave filter,
Figure 707610DEST_PATH_IMAGE007
With
Figure 861511DEST_PATH_IMAGE008
Be respectively gaussian envelope along
Figure 432432DEST_PATH_IMAGE009
The axle with
Figure 909549DEST_PATH_IMAGE010
The space constant of axle, it represents the yardstick of Gabor wave filter;
The expression formula of feature coding is following:
Figure 524202DEST_PATH_IMAGE011
；
Wherein IBe iris image, GBe the Gabor wave filter,
Figure 638833DEST_PATH_IMAGE012
The real imaginary part of symbol of expression filtered,
Figure 262712DEST_PATH_IMAGE013
Be feature coding;
1.3) according to formula:
, calculating needs corresponding M * N the iris image submodule Hamming distance separately of two iris images of coupling to leave, and for each iris image submodule, can obtain eight Hamming distances and leave, and eight Hamming distances are designated as V from the proper vector that constitutes Whole,
Wherein code A and code B represent the Gabor feature coding of two iris images respectively; Mask A and mask B represent the noise template of two iris images respectively, and its value is " 0 " interval scale noise for the effective iris portion of " 1 " interval scale;
1.4) make L=M * N, according to step 1.3) in Hamming distance obtain 8 * L Hamming distance from computing formula and leave V Part, be designated as:
Figure 259115DEST_PATH_IMAGE016
；
1.5) Hamming distance of whole iris image is left V PartLeave V with the Hamming distance of iris image submodule WholeMerge, form the proper vector V that dimension is 8+8 * M * N, V=[V Whole,V Part], that is:
Figure 608057DEST_PATH_IMAGE017
；
2) use the Adaboost algorithm that the piecemeal distance that obtains in the step 1) is carried out Classification and Identification, it specifically may further comprise the steps:
2.1) a sample training collection
Figure 623549DEST_PATH_IMAGE018
is set;
Figure 845583DEST_PATH_IMAGE019
;
Figure 179481DEST_PATH_IMAGE020
; I=1; 2; N; M is the dimension of vector , wherein:
Figure 952844DEST_PATH_IMAGE022
is the proper vector of whole iris image formation and the multidimensional characteristic vectors of the proper vector be combined into after the piecemeal; When two iris image submodules during from same human eye;
Figure 595047DEST_PATH_IMAGE023
, otherwise
Figure 432553DEST_PATH_IMAGE024
;
2.2) pass through formula:
; The sample training collection is carried out weight go out beginningization; Then the sample training collection that weight distribution is arranged is carried out training study, obtain a Weak Classifier
Figure 847802DEST_PATH_IMAGE026
;
Figure 411638DEST_PATH_IMAGE027
; T=1,2 ..., T, T are iterations;
2.3) select a characteristic in the step 1), pass through formula then:
Figure 221594DEST_PATH_IMAGE028
; M=1; 2; M; Calculate the classification error rate
Figure 872204DEST_PATH_IMAGE029
that Weak Classifier
Figure 529078DEST_PATH_IMAGE026
is concentrated at sample training; Note ; During as
Figure 2011104217964100002DEST_PATH_IMAGE031
; Make T=t-1, and jump out circulation;
2.4) pass through formula:
calculates the weight
Figure 874423DEST_PATH_IMAGE034
of Weak Classifier
Figure 2011104217964100002DEST_PATH_IMAGE033
;
2.5) pass through formula:
Figure 2011104217964100002DEST_PATH_IMAGE035
; It is heavy to upgrade the sample training centralization of state power, and the sample training collection is carried out normalization;
2.6) at last according to formula:
Figure 511203DEST_PATH_IMAGE036
; Obtain final sorter; The characteristic of this sorter is the characteristic of proper vector V in the iris image, and identification is accomplished.
Beneficial effect of the present invention is: only use noisy in the iris less a part or a plurality of parts to discern, reduced The noise, well solved the identification problem of inferior quality iris image, have good recognition performance.
Description of drawings
According to accompanying drawing the present invention is done further explain below.
Fig. 1 be the embodiment of the invention described based on multi-direction Gabor and Adaboost iris identification method process flow diagram;
Fig. 2 is the synoptic diagram of the Gabor real part wave filter of eight directions;
Fig. 3 is the cataloged procedure figure of Gabor;
Fig. 4 is the synoptic diagram of iris piecemeal.
Embodiment
As shown in Figure 1, present embodiment is described a kind of based on multi-direction Gabor and Adaboost iris identification method, and it comprises the steps:
Step 1: normalized iris image is extracted the two-dimensional Gabor characteristic
2D Gabor wave filter is owing to have good resolution characteristic in time domain and frequency domain, and expression formula is following:
Figure 666110DEST_PATH_IMAGE001
（1）
Wherein
Figure 699925DEST_PATH_IMAGE002
;
Figure 111663DEST_PATH_IMAGE003
;
Figure 180113DEST_PATH_IMAGE004
is the direction of Gabor wave filter;
Figure 2011104217964100002DEST_PATH_IMAGE037
and is respectively the level and the vertical centre frequency of Gabor wave filter;
Figure 449869DEST_PATH_IMAGE007
and
Figure 89797DEST_PATH_IMAGE008
is respectively the space constant of gaussian envelope axle axle with
Figure 10928DEST_PATH_IMAGE010
along
Figure 12754DEST_PATH_IMAGE009
, represents the yardstick of Gabor wave filter.
The present invention uses the Gabor wave filter (Fig. 2) of eight directions of same yardstick; Respectively corresponding
Figure 753756DEST_PATH_IMAGE005
,
Figure 994114DEST_PATH_IMAGE006
.
These eight two-dimensional Gabor filter are acted on the iris image after the normalization, use the quadrant of filtered to encode, expression as follows:
Figure 771577DEST_PATH_IMAGE011
（2）
Wherein
Figure 940652DEST_PATH_IMAGE039
is iris image; is the Gabor wave filter; The real imaginary part of symbol of
Figure 214825DEST_PATH_IMAGE041
expression filtered,
Figure DEST_PATH_IMAGE043A
is feature coding.
Whole iris region has comprised noises such as hot spot, eyelashes, therefore when certain part of only using few noisy in the iris is discerned, can reduce The noise to a certain extent, might improve recognition performance on the contrary.Like Fig. 4; The iris image that launches is divided into evenly M is capable, the N row, so just obtains
Figure 925423DEST_PATH_IMAGE044
sub-block.To this a little iris implementation step 1.
The present invention adopts Hamming distance to leave as similarity measurement; Like Fig. 3; The Gabor of two width of cloth images coding is carried out XOR, considers the noise in two width of cloth images, with the XOR result of effective pixel points add and divided by the number of effective pixel points as an eigenwert; Be Hamming distance and leave, formula is following:
（3）
Where and
Figure 315712DEST_PATH_IMAGE047
are iris iris image a and B, Gabor feature encoding;
Figure 317035DEST_PATH_IMAGE048
and
Figure 77181DEST_PATH_IMAGE049
represent the iris image A and image B iris of the noise pattern, the value of "1" represents the effective portion of the iris, is "0" represents noise.
At first, the Hamming distance that calculates the Gabor coding of whole iris unfolded image leaves, because the present invention has chosen the Gabor wave filter of eight directions, so can obtain eight Hamming distances from the proper vector that constitutes, is designated as:
Figure 32630DEST_PATH_IMAGE015
（4）
Then; Calculating needs corresponding
Figure 169213DEST_PATH_IMAGE044
the individual piecemeal Hamming distance separately of two irises of coupling to leave; For every sub-block; Can obtain eight Hamming distances leaves; Make , it is individual just can to obtain
Figure 956089DEST_PATH_IMAGE051
Hamming distance leaves, and is designated as:
Figure 398834DEST_PATH_IMAGE016
（5）
At last; merged with
Figure 987127DEST_PATH_IMAGE053
; Obtain proper vector
Figure 828089DEST_PATH_IMAGE054
, that is:
Figure 7398DEST_PATH_IMAGE017
（6）
The dimension of proper vector is
Figure 2011104217964100002DEST_PATH_IMAGE055
.
Step 2: use the Adaboost algorithm that characteristic is carried out Classification and Identification
The Adaboost algorithm is the simple classification device (being called Weak Classifier) that utilizes a large amount of classification capacities general, combines the sorter that composition and classification is very capable through certain method.Detailed process is following:
Input: training set
Figure 672734DEST_PATH_IMAGE018
; Wherein
Figure 739042DEST_PATH_IMAGE019
;
Figure 277470DEST_PATH_IMAGE020
; ,
Figure 209840DEST_PATH_IMAGE057
are the dimensions of vector
Figure 130654DEST_PATH_IMAGE058
; Iterations T and weak learning algorithm.
Initialization: weight
Figure 839984DEST_PATH_IMAGE059
.（7）
Operation: for
Figure 977573DEST_PATH_IMAGE060
1) to the training set study of weight distribution is arranged, obtains a Weak Classifier
（8）
2) select best characteristic to make classification error rate
Figure 838661DEST_PATH_IMAGE029
minimum, wherein
Figure 905843DEST_PATH_IMAGE061
（9）
Figure 281461DEST_PATH_IMAGE062
, and note .If
Figure 800746DEST_PATH_IMAGE031
makes
Figure 101146DEST_PATH_IMAGE063
and jumps out circulation.
3) weight of calculating Weak Classifier :
Figure 876783DEST_PATH_IMAGE032
（10）
4) upgrade sample weights:
Figure 328493DEST_PATH_IMAGE035
（11）
Wherein is normalized factor.
Output:
Figure 385890DEST_PATH_IMAGE065
.
The iris recognition problem is a typical classification problem.As long as make each Weak Classifier corresponding to 1 characteristic (also being Hamming), and according to the judgement of classifying of the size of eigenwert, then the iris recognition process is exactly the process that Adaboost selects characteristic, just selects the process of Weak Classifier.In the process that two width of cloth images are classified, use
Figure 351572DEST_PATH_IMAGE066
（12）
As characteristic, i.e.
Figure 657789DEST_PATH_IMAGE009
in the training set; Take two images that obtain for identical human eye; Make
Figure 801456DEST_PATH_IMAGE067
; Different human eyes are taken two images that obtain, and make
Figure 373383DEST_PATH_IMAGE068
.Adopt the AdaBoost algorithm to train then, the characteristic of division of choosing is combined into stronger sorter with these characteristic of divisions again.
The present invention is not limited to above-mentioned preferred forms; Anyone can draw other various forms of products under enlightenment of the present invention; No matter but on its shape or structure, do any variation; Every have identical with a application or akin technical scheme, all drops within protection scope of the present invention.

Claims (3)

1. one kind based on multi-direction Gabor and Adaboost iris identification method, it is characterized in that it comprises the steps:
1) to the normalized iris image piecemeal and extract the two-dimensional Gabor characteristic, and the Hamming distance that calculates between the corresponding blocks leaves; And
2) use the Adaboost algorithm that the piecemeal distance that obtains in the step 1) is carried out Classification and Identification.
2. according to claim 1 based on multi-direction Gabor and Adaboost iris identification method, it is characterized in that step 1) specifically may further comprise the steps:
1.1) iris image that launches is divided into evenly M is capable, the N row, obtain M * N iris image submodule;
1.2) use the Gabor wave filter of eight directions of same yardstick to act on step 1.1) and in the iris image submodule, according to the Gabor real part the positive and negative of image filtering result encoded then, wherein,
The expression formula of Gabor wave filter is following:
Figure 996868DEST_PATH_IMAGE001
,
Figure 221656DEST_PATH_IMAGE002
,
Figure 463675DEST_PATH_IMAGE003
,
Figure 531426DEST_PATH_IMAGE004
Be the direction of Gabor wave filter,
Figure 740953DEST_PATH_IMAGE005
,
Figure 52986DEST_PATH_IMAGE006
, UWith VBe respectively the level and the vertical centre frequency of Gabor wave filter,
Figure 162893DEST_PATH_IMAGE007
With
Figure 974116DEST_PATH_IMAGE008
Be respectively gaussian envelope along
Figure 985453DEST_PATH_IMAGE009
The axle with The space constant of axle, it represents the yardstick of Gabor wave filter;
The expression formula of feature coding is following:
Figure 394753DEST_PATH_IMAGE011
；
Wherein IBe iris image, GBe the Gabor wave filter,
Figure 439195DEST_PATH_IMAGE012
The real imaginary part of symbol of expression filtered, Be feature coding;
1.3) according to formula:
Figure 891529DEST_PATH_IMAGE014
, calculating needs corresponding M * N the iris image submodule Hamming distance separately of two iris images of coupling to leave, and for each iris image submodule, can obtain eight Hamming distances and leave, and eight Hamming distances are designated as V from the proper vector that constitutes Whole,
Wherein code A and code B represent the Gabor feature coding of two iris images respectively; Mask A and mask B represent the noise template of two iris images respectively, and its value is " 0 " interval scale noise for the effective iris portion of " 1 " interval scale;
1.4) make L=M * N, according to step 1.3) in Hamming distance obtain 8 * L Hamming distance from computing formula and leave V Part, be designated as:
Figure 988109DEST_PATH_IMAGE016
；
1.5) Hamming distance of whole iris image is left V PartLeave V with the Hamming distance of iris image submodule WholeMerge, form the proper vector V that dimension is 8+8 * M * N, V=[V Whole,V Part], that is:
Figure 95743DEST_PATH_IMAGE017
。
3. according to claim 2 based on multi-direction Gabor and Adaboost iris identification method, it is characterized in that step 2) specifically may further comprise the steps:
2.1) a sample training collection
Figure 553269DEST_PATH_IMAGE018
is set; ;
Figure 3153DEST_PATH_IMAGE020
; I=1; 2; N; M is the dimension of vector ; Wherein:
Figure 157502DEST_PATH_IMAGE022
is the proper vector of whole iris image formation and the multidimensional characteristic vectors of the proper vector be combined into after the piecemeal; When two iris image submodules during from same human eye;
Figure 186900DEST_PATH_IMAGE023
, otherwise ;
2.2) pass through formula:
Figure 324806DEST_PATH_IMAGE025
; The sample training collection is carried out weight go out beginningization; Then the sample training collection that weight distribution is arranged is carried out training study, obtain a Weak Classifier
Figure 124135DEST_PATH_IMAGE026
;
Figure 211302DEST_PATH_IMAGE027
; T=1,2 ..., T, T are iterations;
2.3) select a characteristic in the step 1); Pass through formula then:
Figure 703463DEST_PATH_IMAGE028
; M=1; 2; M; Calculate the classification error rate
Figure 908628DEST_PATH_IMAGE029
that Weak Classifier
Figure 804143DEST_PATH_IMAGE026
is concentrated at sample training; Note
Figure 614415DEST_PATH_IMAGE030
; During as
Figure 11899DEST_PATH_IMAGE031
; Make T=t-1, and jump out circulation;
2.4) pass through formula:
Figure 803137DEST_PATH_IMAGE032
calculates the weight
Figure 142380DEST_PATH_IMAGE034
of Weak Classifier ;
2.5) pass through formula: ; It is heavy to upgrade the sample training centralization of state power, and the sample training collection is carried out normalization;
2.6) according to formula:
Figure 490764DEST_PATH_IMAGE036
; Obtain final sorter; The characteristic of this sorter is the characteristic of proper vector V in the iris image, and identification is accomplished.
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CN105095880A (en) * 2015-08-20 2015-11-25 中国民航大学 LGBP encoding-based finger multi-modal feature fusion method
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