CN110647796A - Iris identification method and device - Google Patents
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- G—PHYSICS
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- 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
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- 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
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Abstract
The invention relates to the technical field of image processing, in particular to an iris identification method and device, which comprises the steps of firstly obtaining a plurality of iris images, and normalizing the iris images into rectangular images with fixed sizes; further calculating the definition of the rectangular image, and eliminating the rectangular image with the definition lower than a threshold value; and then dividing the remaining rectangular images into a plurality of groups of sample sets, wherein each group of sample sets comprises rectangular images with equal quantity, calculating an in-group dispersion matrix of each group of sample sets, selecting the sample set with the minimum in-group dispersion matrix as a characteristic set, and finally generating an iris characteristic vector according to the characteristic set to perform iris recognition.
Description
Technical Field
The invention relates to the technical field of image processing, in particular to an iris identification method and device.
Background
An iris has about 266 quantization feature points, while the general biometric identification technology has only 13 to 60 feature points. The 266 quantization feature point iris recognition algorithm is taught in many iris recognition technical documents, and the algorithm can obtain 173 independent feature points with binary degrees of freedom when the algorithm and the human eye feature allow. In biometric identification techniques, the number of such feature points is considerable. Thereby having great guarantee on the safety.
However, in the process of iris image acquisition, motion wire drawing phenomenon is often generated, or noise level of the iris image is changed under the influence of different illumination conditions.
Therefore, how to describe iris features more accurately so as to improve the accuracy of iris recognition becomes a problem to be solved urgently.
Disclosure of Invention
In order to solve the above problems, the present invention provides an iris identification method and apparatus, which can describe iris features more accurately, thereby improving the accuracy of iris identification.
In order to achieve the purpose, the invention provides the following technical scheme:
an iris recognition method comprising:
acquiring a plurality of iris images, and normalizing the iris images into rectangular images with fixed sizes;
calculating the definition of the rectangular image, and removing the rectangular image with the definition lower than a threshold value;
dividing the rest rectangular images into a plurality of groups of sample sets, wherein each group of sample sets comprises rectangular images with equal number;
calculating an intra-group dispersion matrix of each group of sample sets, and selecting the sample set with the minimum intra-group dispersion matrix as a characteristic set;
and generating an iris feature vector according to the feature set so as to perform iris recognition.
Further, the normalizing the iris image into a rectangular image of a fixed size includes:
and carrying out spatial transformation on the iris image by adopting a bilinear interpolation mode, and normalizing the iris image into a rectangular image with a fixed size.
Further, the calculating the definition of the rectangular image and eliminating the rectangular image with the definition lower than a threshold value includes:
calculating the sharpness of the rectangular image by the following formula:
Si＝∑y∑x(|f(x+1,y)-f(x,y)|2+|f(x,y+1)-f(x,y)|2)
wherein f (x, y) represents the gray value of a pixel point with coordinates (x, y) in the rectangular image, f (x +1, y) represents the brightness value of the pixel point with coordinates (x +1, y) in the rectangular image in a red channel, f (x, y +1) represents the brightness value of the pixel point with coordinates (x, y +1) in the rectangular image in a green channel, and SiI.e. of said rectangular imageDefinition;
comparing the sharpness S of the rectangular imageiAnd a threshold value S0Size of (2), will definition SiBelow a threshold value S0And (4) eliminating the rectangular image.
Further, the calculating of the intra-group dispersion matrix of each group of sample sets, and selecting the sample set with the minimum intra-group dispersion matrix as a feature set specifically include:
let sample set Y ═ Y1，y2，...，yn×mY is an n × m matrix, n is the number of rectangular images in each set of samples, and m is the number of sets of samples;
calculating the mean vector of each group of rectangular images by the following formula:
wherein, ciIs the mean vector of the ith set of rectangular images, yijSamples with the number j in the ith group of sample sets;
calculating an intra-group dispersion matrix P of each group of rectangular images by the following formulai：
And comparing the sizes of the dispersion matrixes in the groups, and selecting the sample set with the minimum dispersion matrix in the groups as a characteristic set.
Further, the generating an iris feature vector according to the feature set for iris recognition includes:
binarizing all rectangular images in the feature set to generate binarized images corresponding to the rectangular images;
taking the average value of all the binary images as an iris feature vector;
and carrying out characteristic matching on the iris characteristic vector and a preset iris characteristic template so as to carry out iris recognition.
An iris recognition apparatus, the apparatus comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, the processor executing the computer program to operate in modules of:
the normalization module is used for acquiring a plurality of iris images and normalizing the iris images into rectangular images with fixed sizes;
the eliminating module is used for calculating the definition of the rectangular image and eliminating the rectangular image with the definition lower than a threshold value;
the dividing module is used for dividing the residual rectangular images into a plurality of groups of sample sets, and each group of sample sets comprises rectangular images with equal quantity;
the characteristic set selection module is used for calculating an in-group dispersion matrix of each group of sample sets and selecting the sample set with the minimum in-group dispersion matrix as a characteristic set;
and the identification module is used for generating an iris feature vector according to the feature set so as to carry out iris identification.
Further, the normalization module is specifically configured to:
and carrying out spatial transformation on the iris image by adopting a bilinear interpolation mode, and normalizing the iris image into a rectangular image with a fixed size.
The invention has the beneficial effects that: the invention discloses an iris identification method and device, firstly, acquiring a plurality of iris images, and normalizing the iris images into rectangular images with fixed sizes; further calculating the definition of the rectangular image, and eliminating the rectangular image with the definition lower than a threshold value; and then dividing the remaining rectangular images into a plurality of groups of sample sets, wherein each group of sample sets comprises rectangular images with equal quantity, calculating an intra-group dispersion matrix of each group of sample sets, selecting the sample set with the minimum intra-group dispersion matrix as a feature set, and finally generating an iris feature vector according to the feature set to perform iris identification. The invention can describe the iris characteristics more accurately, thereby improving the accuracy of iris recognition.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings needed to be used in the embodiments will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings without inventive exercise.
FIG. 1 is a schematic flow chart of an iris identification method of the present invention;
fig. 2 is a schematic structural diagram of an iris recognition apparatus according to the present invention.
Detailed Description
The conception, specific structure and technical effects of the present disclosure will be clearly and completely described below in conjunction with the embodiments and the accompanying drawings to fully understand the objects, aspects and effects of the present disclosure. It should be noted that the embodiments and features of the embodiments in the present application may be combined with each other without conflict.
As shown in fig. 1, an iris identification method includes the following steps:
s100, acquiring a plurality of iris images, and normalizing the iris images into rectangular images with fixed sizes;
s200, calculating the definition of the rectangular image, and removing the rectangular image with the definition lower than a threshold value;
step S300, dividing the residual rectangular images into a plurality of groups of sample sets, wherein each group of sample sets comprises rectangular images with equal number;
s400, calculating an intra-group dispersion matrix of each group of sample sets, and selecting the sample set with the minimum intra-group dispersion matrix as a characteristic set;
and S500, generating an iris feature vector according to the feature set to perform iris recognition.
In the embodiment, the iris image is firstly normalized into a rectangular image with a fixed size, so that standard calculation is facilitated, the rectangular image with the definition lower than a threshold value is removed, and an abnormal iris image is preliminarily removed; and dividing the rest rectangular images into a plurality of groups of sample sets, carrying out primary classification, further selecting the sample set with the minimum dispersion matrix in the groups as a characteristic set, thereby extracting the sample set with the optimal stability, and finally generating an iris characteristic vector according to the characteristic set, thereby carrying out iris identification on iris characteristics more accurately and improving the precision of iris identification.
In one embodiment, the step S100 of normalizing the iris image into a rectangular image with a fixed size includes:
and carrying out spatial transformation on the iris image by adopting a bilinear interpolation mode, and normalizing the iris image into a rectangular image with a fixed size.
As a further improvement of this embodiment, the step S200 includes:
calculating the sharpness of the rectangular image by the following formula:
Si＝∑y∑x(|f(x+1,y)-f(x,y)|2+|f(x,y+1)-f(x,y)|2)
wherein f (x, y) represents the gray value of a pixel point with coordinates (x, y) in the rectangular image, f (x +1, y) represents the brightness value of the pixel point with coordinates (x +1, y) in the rectangular image in a red channel, f (x, y +1) represents the brightness value of the pixel point with coordinates (x, y +1) in the rectangular image in a green channel, and SiThe definition of the rectangular image is obtained;
comparing the sharpness S of the rectangular imageiAnd a threshold value S0Size of (2), will definition SiBelow a threshold value S0And (4) eliminating the rectangular image. Wherein the threshold value S0The preset value can be set according to actual conditions, and the value range is [0.9,0.99 ]]。
The definition calculation method adopted by the embodiment can generate the definition of the rectangular image in real time, and improves the processing efficiency when a large number of rectangular images are processed.
As a further improvement of this embodiment, the step S400 specifically includes:
let sample set Y ═ Y1，y2，...，yn×mY is an n × m matrix, n is the number of rectangular images in each set of samples, and m is the number of sets of samples in the set；
Calculating the mean vector c of each group of rectangular images by the following formulai：
Wherein, ciIs the mean vector of the ith set of rectangular images, yijSamples with the number j in the ith group of sample sets;
calculating an intra-group dispersion matrix P of each group of rectangular images by the following formulai：
And comparing the sizes of the dispersion matrixes in the groups, and selecting the sample set with the minimum dispersion matrix in the groups as a characteristic set.
As a further improvement of this embodiment, the generating an iris feature vector according to the feature set for iris recognition includes:
binarizing all rectangular images in the feature set to generate binarized images corresponding to the rectangular images;
taking the average value of all the binary images as an iris feature vector;
and carrying out characteristic matching on the iris characteristic vector and a preset iris characteristic template so as to carry out iris recognition.
In this embodiment, the average value of all the binarized images is used as the iris feature vector, so that image noise can be further eliminated for the preferred feature set, and iris features can be described more accurately.
The preset iris feature templates are iris features prestored in a database, the database can be a local database or a cloud database, the iris features can be manually preset, and the iris feature templates can be one or more.
Carrying out feature matching on the iris feature vector and a preset iris feature template, which is the prior art; performing iris recognition may be understood as when feature matching is successful, the iris feature templates in the database may be used as the result of iris recognition.
Referring to fig. 2, the present invention also provides an iris recognition apparatus, including: a memory, a processor, and a computer program stored in the memory and executable on the processor, the processor executing the computer program to operate in modules of:
a normalization module 100, configured to acquire a plurality of iris images and normalize the iris images into a rectangular image with a fixed size;
a removing module 200, configured to calculate a definition of the rectangular image, and remove the rectangular image with the definition lower than a threshold;
a dividing module 300, configured to divide the remaining rectangular images into multiple groups of sample sets, where each group of sample sets includes equal number of rectangular images;
the feature set selection module 400 is configured to calculate an intra-group dispersion matrix of each group of sample sets, and select a sample set with a minimum intra-group dispersion matrix as a feature set;
and the identification module 500 is configured to generate an iris feature vector according to the feature set to perform iris identification.
Further, the normalization module 100 is specifically configured to:
and carrying out spatial transformation on the iris image by adopting a bilinear interpolation mode, and normalizing the iris image into a rectangular image with a fixed size.
The iris recognition device can be operated in computing equipment such as desktop computers, notebooks, palm computers and cloud servers. The device capable of operating the iris recognition device can comprise a processor and a memory. It will be appreciated by those skilled in the art that the example is merely an example of an iris recognition apparatus and does not constitute a limitation of an iris recognition apparatus, and may include more or less components than the iris recognition apparatus, or combine some components, or different components, for example, the iris recognition apparatus may further include an input-output device, a network access device, a bus, etc.
The Processor may be a Central-Processing Unit (CPU), other general-purpose Processor, a Digital Signal Processor (DSP), an Application-Specific-Integrated-Circuit (ASIC), an off-the-shelf Programmable Gate Array (FPGA) or other Programmable logic device, a discrete Gate or transistor logic device, a discrete hardware component, or the like. The general purpose processor may be a microprocessor or the processor may be any conventional processor or the like, the processor being the control center of the one type of iris recognition apparatus operation device, with various interfaces and lines connecting the various parts of the entire one type of iris recognition apparatus operable device.
The memory may be used to store the computer programs and/or modules, and the processor may implement the various functions of the iris recognition apparatus by operating or executing the computer programs and/or modules stored in the memory and calling data stored in the memory. The memory may mainly include a storage program area and a storage data area, wherein the storage program area may store an operating system, an application program required by at least one function (such as a sound playing function, an image playing function, etc.), and the like; the storage data area may store data (such as audio data, a phonebook, etc.) created according to the use of the cellular phone, and the like. In addition, the memory may include high speed random access memory, and may also include non-volatile memory, such as a hard disk, a memory, a plug-in hard disk, a Smart-Media-Card (SMC), a Secure-Digital (SD) Card, a Flash-memory Card (Flash-Card), at least one magnetic disk storage device, a Flash memory device, or other volatile solid state storage device.
While the present disclosure has been described in considerable detail and with particular reference to a few illustrative embodiments thereof, it is not intended to be limited to any such details or embodiments or any particular embodiments, but it is to be construed as effectively covering the intended scope of the disclosure by providing a broad, potential interpretation of such claims in view of the prior art with reference to the appended claims. Furthermore, the foregoing describes the disclosure in terms of embodiments foreseen by the inventor for which an enabling description was available, notwithstanding that insubstantial modifications of the disclosure, not presently foreseen, may nonetheless represent equivalent modifications thereto.
Claims (7)
1. An iris identification method, comprising:
acquiring a plurality of iris images, and normalizing the iris images into rectangular images with fixed sizes;
calculating the definition of the rectangular image, and removing the rectangular image with the definition lower than a threshold value;
dividing the rest rectangular images into a plurality of groups of sample sets, wherein each group of sample sets comprises rectangular images with equal number;
calculating an intra-group dispersion matrix of each group of sample sets, and selecting the sample set with the minimum intra-group dispersion matrix as a characteristic set;
and generating an iris feature vector according to the feature set so as to perform iris recognition.
2. An iris identification method according to claim 1, wherein said normalizing said iris image into a rectangular image of fixed size comprises:
and carrying out spatial transformation on the iris image by adopting a bilinear interpolation mode, and normalizing the iris image into a rectangular image with a fixed size.
3. The iris identification method according to claim 1, wherein said calculating the definition of said rectangular image and rejecting the rectangular image whose definition is lower than a threshold value comprises:
calculating the sharpness of the rectangular image by the following formula:
Si＝∑y∑x(|f(x+1,y)-f(x,y)|2+|f(x,y+1)-f(x,y)|2)
wherein f (x, y) represents the gray value of the pixel point with the coordinate (x, y) in the rectangular image, and f (x +1, y) represents the pixel point with the coordinate (x +1, y) in the rectangular imageThe brightness value of a pixel point in a red channel, f (x, y +1) represents the brightness value of the pixel point with coordinates of (x, y +1) in the rectangular image in a green channel, and SiThe definition of the rectangular image is obtained;
comparing the sharpness S of the rectangular imageiAnd a threshold value S0Size of (2), will definition SiBelow a threshold value S0And (4) eliminating the rectangular image.
4. The iris identification method according to claim 1, wherein the intra-group dispersion matrix of each group of sample sets is calculated, and the sample set with the minimum intra-group dispersion matrix is selected as a feature set, specifically:
let sample set Y ═ Y1，y2，...，yn×mY is an n × m matrix, n is the number of rectangular images in each set of samples, and m is the number of sets of samples;
calculating the mean vector of each group of rectangular images by the following formula:
wherein ci is the mean vector of the ith group of rectangular images, yijSamples with the number j in the ith group of sample sets;
calculating an intra-group dispersion matrix P of each group of rectangular images by the following formulai：
And comparing the sizes of the dispersion matrixes in the groups, and selecting the sample set with the minimum dispersion matrix in the groups as a characteristic set.
5. An iris identification method according to claim 1, wherein said generating iris feature vectors according to said feature set for iris identification comprises:
binarizing all rectangular images in the feature set to generate binarized images corresponding to the rectangular images;
taking the average value of all the binary images as an iris feature vector;
and carrying out characteristic matching on the iris characteristic vector and a preset iris characteristic template so as to carry out iris recognition.
6. An iris recognition apparatus, comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, the processor executing the computer program to operate in modules of:
the normalization module is used for acquiring a plurality of iris images and normalizing the iris images into rectangular images with fixed sizes;
the eliminating module is used for calculating the definition of the rectangular image and eliminating the rectangular image with the definition lower than a threshold value;
the dividing module is used for dividing the residual rectangular images into a plurality of groups of sample sets, and each group of sample sets comprises rectangular images with equal quantity;
the characteristic set selection module is used for calculating an in-group dispersion matrix of each group of sample sets and selecting the sample set with the minimum in-group dispersion matrix as a characteristic set;
and the identification module is used for generating an iris feature vector according to the feature set so as to carry out iris identification.
7. The iris identification apparatus of claim 6, wherein the normalization module is specifically configured to:
and carrying out spatial transformation on the iris image by adopting a bilinear interpolation mode, and normalizing the iris image into a rectangular image with a fixed size.
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