CN102521575B - 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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CN102521575B
CN102521575B CN201110421796.4A CN201110421796A CN102521575B CN 102521575 B CN102521575 B CN 102521575B CN 201110421796 A CN201110421796 A CN 201110421796A CN 102521575 B CN102521575 B CN 102521575B
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iris
iris image
gabor
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wave filter
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CN102521575A (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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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V40/00—Recognition of biometric, human-related or animal-related patterns in image or video data
    • G06V40/10—Human or animal bodies, e.g. vehicle occupants or pedestrians; Body parts, e.g. hands
    • G06V40/18—Eye characteristics, e.g. of the iris
    • G06V40/193—Preprocessing; Feature extraction

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 based on multi-direction Gabor and Adaboost, belong to living things feature recognition and secure authentication technology field.
Background technology
In modern society, sharply frequent along with the high development of network technology, flow of personnel, a kind of identity authorization system safe and reliable, convenience and high-efficiency seems particularly important.Traditional identification mainly contains two kinds: mark article (key, identity document etc.) and mark knowledge (user name, password etc.).But in actual applications,, there is easily the deficiencies such as loss property, easily forgery property, nonuniqueness and range of application be relatively little in it, makes people in the urgent need to a kind of personal identification method that can overcome above-mentioned defect.Under the driving of demand, the recognition technology based on biological characteristics such as face, fingerprint, iris, hand shape, person's handwritings is arisen at the historic moment.
At present, there are a lot of iris authentication systems both at home and abroad.The iris authentication system based on Gabor that wherein doctor Daugman realizes be propose the earliest can practical application iris authentication system.In iris authentication system, first to gather iris image, then in the iris image collecting, cut apart iris region, finally after normalization, on iris image, extract feature and mate.Wherein iris feature extraction is one of principal element affecting system performance, and the quality requirements of the large multipair iris image of existing Algorithm of Iris Recognition is higher.But, the impact of the factor such as the iris image collecting under natural light can be subject to eyelashes, eyelid, illumination, rock, cause the iris image quality collecting not good enough, for this class inferior quality iris image, need a kind of effectively iris feature to extract and recognizer.
Summary of the invention
The object of this invention is to provide one based on multi-direction Gabor and Adaboost iris identification method, there is good recognition performance, overcome the deficiency of the above-mentioned aspect of prior art.
The object of the invention is to be achieved through the following technical solutions:
A kind of based on multi-direction Gabor and Adaboost iris identification method, it comprises the steps:
1) to normalized iris image piecemeal and extract two-dimensional Gabor feature, and calculate Hamming distance between corresponding blocks from, it specifically comprises the following steps:
1.1) iris image of expansion is evenly divided into M is capable, 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) in iris image submodule after normalization, then according to Gabor real part, the positive and negative of image filtering result encoded; Wherein the expression formula of Gabor wave filter is as follows:
, ,
Figure 20463DEST_PATH_IMAGE003
,
Figure 608439DEST_PATH_IMAGE004
the direction of Gabor wave filter, ,
Figure 76909DEST_PATH_IMAGE006
, uwith vbe respectively the horizontal and vertical centre frequency of Gabor wave filter,
Figure 707610DEST_PATH_IMAGE007
with
Figure 861511DEST_PATH_IMAGE008
respectively Gaussian envelope along axle and
Figure 909549DEST_PATH_IMAGE010
the space constant of axle, it represents the yardstick of Gabor wave filter;
The expression formula of feature coding is as follows:
Figure 524202DEST_PATH_IMAGE011
；
Wherein iiris image, ggabor wave filter,
Figure 638833DEST_PATH_IMAGE012
represent the real imaginary part of symbol of filtering result,
Figure 262712DEST_PATH_IMAGE013
for feature coding;
1.3) according to formula:
Figure 594336DEST_PATH_IMAGE014
, calculate need M × N iris image submodule corresponding to two iris images of coupling Hamming distance separately from, for each iris image submodule, can obtain eight Hamming distances from, eight Hamming distances are designated as to V from the proper vector of formation whole,
Figure 396201DEST_PATH_IMAGE015
;
Wherein code A and code B represent respectively the Gabor feature coding of two iris images; Mask A and mask B represent respectively the noise template of two iris images, and its value, for the effective iris portion of " 1 " interval scale, is " 0 " interval scale noise;
1.4) make L=M × N, according to step 1.3) in Hamming distance obtain 8 × L Hamming distance from V from computing formula part, be designated as:
Figure 259115DEST_PATH_IMAGE016
；
1.5) by the Hamming distance of whole iris image from V partwith the Hamming distance of iris image submodule from V wholemerge, form a proper vector V that dimension is 8+8 × M × N, V=[V whole,v part], that is:
Figure 608057DEST_PATH_IMAGE017
；
2) use Adaboost algorithm to carry out Classification and Identification to the piecemeal distance obtaining in step 1), it specifically comprises the following steps:
2.1) a sample training collection is set
Figure 623549DEST_PATH_IMAGE018
, ,
Figure 179481DEST_PATH_IMAGE020
, i=1,2 ..., N, M is vector
Figure 833578DEST_PATH_IMAGE021
dimension, wherein:
Figure 952844DEST_PATH_IMAGE022
the multidimensional characteristic vectors that to be the proper vector that forms of whole iris image be combined into piecemeal proper vector afterwards, when two iris image submodules are during from same human eye, , otherwise
Figure 432553DEST_PATH_IMAGE024
;
2.2) pass through formula:
Figure 624762DEST_PATH_IMAGE025
, sample training collection is carried out to weight and go out beginningization, then the sample training collection that has weight distribution is carried out to training study, obtain a Weak Classifier
Figure 847802DEST_PATH_IMAGE026
;
Figure 411638DEST_PATH_IMAGE027
; T=1,2 ..., T, T is iterations;
2.3) select a feature in step 1), then pass through formula:
Figure 221594DEST_PATH_IMAGE028
, m=1,2 ..., M, calculates Weak Classifier in the concentrated classification error rate of sample training
Figure 872204DEST_PATH_IMAGE029
, note
Figure 606942DEST_PATH_IMAGE030
, when
Figure 2011104217964100002DEST_PATH_IMAGE031
time, make T=t-1, and jump out circulation;
2.4) pass through formula:
, calculate Weak Classifier
Figure 2011104217964100002DEST_PATH_IMAGE033
weight
Figure 874423DEST_PATH_IMAGE034
;
2.5) pass through formula:
Figure 2011104217964100002DEST_PATH_IMAGE035
, upgrade sample training centralization of state power weight, and sample training collection be normalized;
2.6) finally according to formula:
Figure 511203DEST_PATH_IMAGE036
, obtaining final sorter, the feature of this sorter is the feature of proper vector V in iris image, and identification completes.
Beneficial effect of the present invention is: only use a less part or the multiple part of Noise in iris to identify, reduced the impact of noise, well solved the identification problem of inferior quality iris image, have good recognition performance.
Accompanying drawing explanation
With reference to the accompanying drawings the present invention is described in further detail below.
Fig. 1 be described in the embodiment of the present invention based on multi-direction Gabor and Adaboost iris identification method process flow diagram;
Fig. 2 is the schematic diagram of the Gabor real part wave filter of eight directions;
Fig. 3 is the cataloged procedure figure of Gabor;
Fig. 4 is the schematic diagram of iris piecemeal.
Embodiment
As shown in Figure 1, the one described in the present embodiment is based on multi-direction Gabor and Adaboost iris identification method, and it comprises the steps:
Step 1: normalized iris image is extracted to two-dimensional Gabor feature
2D Gabor wave filter is owing to having good resolution characteristic in time domain and frequency domain, and expression formula is as follows:
Figure 666110DEST_PATH_IMAGE001
（1）
Wherein
Figure 699925DEST_PATH_IMAGE002
,
Figure 111663DEST_PATH_IMAGE003
,
Figure 180113DEST_PATH_IMAGE004
the direction of Gabor wave filter,
Figure 2011104217964100002DEST_PATH_IMAGE037
with
Figure 443604DEST_PATH_IMAGE038
be respectively the horizontal and vertical centre frequency of Gabor wave filter,
Figure 449869DEST_PATH_IMAGE007
with respectively Gaussian envelope along
Figure 12754DEST_PATH_IMAGE009
axle and the space constant of axle, represents the yardstick of Gabor wave filter.
The present invention uses the Gabor wave filter (Fig. 2) of eight directions of same yardstick, corresponding respectively ,
Figure 994114DEST_PATH_IMAGE006
.
These eight two-dimensional Gabor filter are acted on to the iris image after normalization, use the quadrant of filtering result to encode, be expressed as follows:
Figure 771577DEST_PATH_IMAGE011
（2）
Wherein
Figure 940652DEST_PATH_IMAGE039
iris image,
Figure 436356DEST_PATH_IMAGE040
gabor wave filter,
Figure 214825DEST_PATH_IMAGE041
represent the real imaginary part of symbol of filtering result,
Figure DEST_PATH_IMAGE043A
for feature coding.
Whole iris region has comprised the noise such as hot spot, eyelashes, therefore, in the time only using certain part of few Noise in iris to identify, can reduce to a certain extent the impact of noise, likely improves on the contrary recognition performance.As Fig. 4, by the iris image of expansion be evenly divided into that M is capable, N row, so just obtain individual sub-block.To these sub-block iris implementation steps 1.
The present invention adopts Hamming distance from as similarity measurement, as Fig. 3, the Gabor coding of two width images is carried out to XOR, consider the noise in two width images, using the XOR result of effective pixel points add and divided by the number of effective pixel points as an eigenwert, be Hamming distance from, formula is as follows:
Figure 514667DEST_PATH_IMAGE045
（3）
Wherein
Figure 982820DEST_PATH_IMAGE046
with
Figure 315712DEST_PATH_IMAGE047
it is respectively the Gabor feature coding of iris image A and iris image B;
Figure 317035DEST_PATH_IMAGE048
with represent respectively the noise template of iris image A and iris image B, its value, for the effective iris portion of " 1 " interval scale, is " 0 " interval scale noise.
First, calculate whole iris unfolded image Gabor coding Hamming distance from, because the present invention has chosen the Gabor wave filter of eight directions, so can obtain eight Hamming distances from the proper vector forming, be designated as:
Figure 32630DEST_PATH_IMAGE015
（4）
Then, calculate and need two irises of coupling corresponding
Figure 169213DEST_PATH_IMAGE044
individual piecemeal Hamming distance separately from, for each sub-block, can obtain eight Hamming distances from, order
Figure 25042DEST_PATH_IMAGE050
, just can obtain
Figure 956089DEST_PATH_IMAGE051
individual
Hamming distance from, be designated as:
Figure 398834DEST_PATH_IMAGE016
（5）
Finally, will with
Figure 987127DEST_PATH_IMAGE053
merge, obtain proper vector
Figure 828089DEST_PATH_IMAGE054
, that is:
（6）
The dimension of proper vector is
Figure 2011104217964100002DEST_PATH_IMAGE055
.
Step 2: use Adaboost algorithm to carry out Classification and Identification to feature
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 by certain method.Detailed process is as follows:
Input: training set
Figure 672734DEST_PATH_IMAGE018
, wherein , ,
Figure 927763DEST_PATH_IMAGE056
,
Figure 209840DEST_PATH_IMAGE057
it is vector
Figure 130654DEST_PATH_IMAGE058
dimension; Iterations T and weak learning algorithm.
Initialization: weight
Figure 839984DEST_PATH_IMAGE059
.（7）
Operation: for
Figure 977573DEST_PATH_IMAGE060
1) to there being the training set study of weight distribution, obtain a Weak Classifier
Figure 797761DEST_PATH_IMAGE027
（8）
2) select best feature to make classification error rate
Figure 838661DEST_PATH_IMAGE029
minimum, wherein
（9）
Figure 281461DEST_PATH_IMAGE062
, and note
Figure 656072DEST_PATH_IMAGE030
.If
Figure 800746DEST_PATH_IMAGE031
, order
Figure 101146DEST_PATH_IMAGE063
and jump out circulation.
3) calculate Weak Classifier
Figure 964060DEST_PATH_IMAGE033
weight:
Figure 876783DEST_PATH_IMAGE032
（10）
4) upgrade sample weights:
Figure 328493DEST_PATH_IMAGE035
（11）
Wherein
Figure 284948DEST_PATH_IMAGE064
it is normalized factor.
Output:
Figure 385890DEST_PATH_IMAGE065
.
Iris recognition problem is a typical classification problem.As long as make each Weak Classifier corresponding to 1 feature (being also Hamming), and according to the judgement of classifying of the size of eigenwert, iris recognition process is exactly the process that Adaboost selects feature, namely selects the process of Weak Classifier.In the process of classifying at two width images, use
Figure 351572DEST_PATH_IMAGE066
（12）
As feature, in training set
Figure 657789DEST_PATH_IMAGE009
; Take two images that obtain for identical human eye, order
Figure 801456DEST_PATH_IMAGE067
, different human eyes are taken two images that obtain, order
Figure 373383DEST_PATH_IMAGE068
.Then adopt AdaBoost algorithm to train, the characteristic of division of choosing, then these characteristic of divisions are combined into stronger sorter.
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 do any variation in its shape or structure; every have identical with a application or akin technical scheme, within all dropping on protection scope of the present invention.

Claims (1)

1. based on multi-direction Gabor and an Adaboost iris identification method, it is characterized in that, it comprises the steps:
1) to normalized iris image piecemeal and extract two-dimensional Gabor feature, and calculate Hamming distance between corresponding blocks from, step 1) specifically comprises the following steps:
1.1) iris image of expansion is evenly divided into M is capable, 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) in iris image submodule, then according to Gabor real part, the positive and negative of image filtering result encoded, wherein,
The expression formula of Gabor wave filter is as follows:
,
Figure 2011104217964100001DEST_PATH_IMAGE003
, , θthe direction of Gabor wave filter,
Figure 857443DEST_PATH_IMAGE006
, , uwith vbe respectively the horizontal and vertical centre frequency of Gabor wave filter,
Figure 291835DEST_PATH_IMAGE008
with
Figure 2011104217964100001DEST_PATH_IMAGE009
respectively Gaussian envelope along
Figure 421946DEST_PATH_IMAGE010
axle and
Figure 2011104217964100001DEST_PATH_IMAGE011
the space constant of axle, it represents the yardstick of Gabor wave filter;
The expression formula of feature coding is as follows:
Figure 2011104217964100001DEST_PATH_IMAGE013
；
Wherein iiris image, ggabor wave filter,
Figure 662304DEST_PATH_IMAGE014
represent the real imaginary part of symbol of filtering result,
Figure 2011104217964100001DEST_PATH_IMAGE015
for feature coding;
1.3) according to formula:
Figure 2011104217964100001DEST_PATH_IMAGE017
, calculate need M × N iris image submodule corresponding to two iris images of coupling Hamming distance separately from, for each iris image submodule, can obtain eight Hamming distances from, eight Hamming distances are designated as to V from the proper vector of formation whole,
Figure 2011104217964100001DEST_PATH_IMAGE019
;
Wherein code aand code brepresent respectively the Gabor feature coding of two iris images; Mask aand mask brepresent respectively the noise template of two iris images, its value, for the effective iris portion of " 1 " interval scale, is " 0 " interval scale noise;
1.4) make L=M × N, according to step 1.3) in Hamming distance obtain 8 × L Hamming distance from V from computing formula part, be designated as:
Figure 2011104217964100001DEST_PATH_IMAGE021
；
1.5) by the Hamming distance of whole iris image from V partwith the Hamming distance of iris image submodule from V wholemerge, form a proper vector V that dimension is 8+8 × M × N, V=[V whole,v part], that is:
Figure 2011104217964100001DEST_PATH_IMAGE023
; And
2) use Adaboost algorithm to carry out classification based training to the piecemeal distance obtaining in step 1), obtain the sorter that formed by multiple Weak Classifiers, for identification, wherein step 2) specifically comprise the following steps:
2.1) a sample training collection is set
Figure 816598DEST_PATH_IMAGE024
,
Figure 2011104217964100001DEST_PATH_IMAGE025
,
Figure 359575DEST_PATH_IMAGE026
, i=1,2 ..., N, M is vector
Figure 2011104217964100001DEST_PATH_IMAGE027
dimension, wherein:
Figure 776650DEST_PATH_IMAGE028
the multidimensional characteristic vectors that to be the proper vector that forms of whole iris image be combined into piecemeal proper vector afterwards, when two iris image submodules are during from same human eye,
Figure 2011104217964100001DEST_PATH_IMAGE029
, otherwise ;
2.2) pass through formula:
Figure 452667DEST_PATH_IMAGE032
, sample training collection is carried out to weight initialization, then the sample training collection that has weight distribution is carried out to training study, obtain a Weak Classifier
Figure 166546DEST_PATH_IMAGE034
;
Figure 211862DEST_PATH_IMAGE036
; T=1,2 ..., T, T is iterations;
2.3) select a feature in step 1), then pass through formula:
Figure 669388DEST_PATH_IMAGE038
, m=1,2 ..., M, calculates Weak Classifier
Figure 277568DEST_PATH_IMAGE034
in the concentrated classification error rate of sample training
Figure 2011104217964100001DEST_PATH_IMAGE039
, note
Figure 427927DEST_PATH_IMAGE040
, when
Figure 2011104217964100001DEST_PATH_IMAGE041
time, make T=t-1, and jump out circulation;
2.4) pass through formula: , calculate Weak Classifier
Figure 819594DEST_PATH_IMAGE044
weight
Figure 2011104217964100001DEST_PATH_IMAGE045
;
2.5) pass through formula:
Figure 2011104217964100001DEST_PATH_IMAGE047
, upgrade sample training centralization of state power weight, and sample training collection be normalized;
2.6) according to formula: , obtaining the strong classifier that formed by multiple Weak Classifiers, this strong classifier is from M × N iris piece preferably the iris piece for identifying out, and training completes.
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