CN113936329A - Iris recognition method, iris recognition device, electronic equipment and computer readable medium - Google Patents
Iris recognition method, iris recognition device, electronic equipment and computer readable medium Download PDFInfo
- Publication number
- CN113936329A CN113936329A CN202111171102.6A CN202111171102A CN113936329A CN 113936329 A CN113936329 A CN 113936329A CN 202111171102 A CN202111171102 A CN 202111171102A CN 113936329 A CN113936329 A CN 113936329A
- Authority
- CN
- China
- Prior art keywords
- image
- feature point
- recognized
- iris
- information
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- 238000000034 method Methods 0.000 title claims abstract description 50
- 238000012545 processing Methods 0.000 claims description 22
- 238000004590 computer program Methods 0.000 claims description 9
- 238000012216 screening Methods 0.000 claims description 8
- 238000007781 pre-processing Methods 0.000 claims description 5
- 230000004044 response Effects 0.000 claims description 3
- 230000011218 segmentation Effects 0.000 claims description 3
- 210000000554 iris Anatomy 0.000 description 140
- 238000004422 calculation algorithm Methods 0.000 description 38
- 238000001514 detection method Methods 0.000 description 13
- 238000010586 diagram Methods 0.000 description 11
- 230000006870 function Effects 0.000 description 8
- 239000011159 matrix material Substances 0.000 description 7
- 238000004891 communication Methods 0.000 description 6
- 238000003706 image smoothing Methods 0.000 description 5
- 238000003708 edge detection Methods 0.000 description 4
- 230000003287 optical effect Effects 0.000 description 4
- 230000008569 process Effects 0.000 description 4
- 238000001914 filtration Methods 0.000 description 3
- 230000009467 reduction Effects 0.000 description 3
- 230000009466 transformation Effects 0.000 description 3
- 238000004364 calculation method Methods 0.000 description 2
- 238000005516 engineering process Methods 0.000 description 2
- 230000001815 facial effect Effects 0.000 description 2
- 238000009499 grossing Methods 0.000 description 2
- 238000009434 installation Methods 0.000 description 2
- 230000000644 propagated effect Effects 0.000 description 2
- 238000012935 Averaging Methods 0.000 description 1
- 238000003491 array Methods 0.000 description 1
- 230000003542 behavioural effect Effects 0.000 description 1
- 230000002146 bilateral effect Effects 0.000 description 1
- 230000008859 change Effects 0.000 description 1
- 238000006243 chemical reaction Methods 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 238000003707 image sharpening Methods 0.000 description 1
- 239000004973 liquid crystal related substance Substances 0.000 description 1
- 230000004807 localization Effects 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 239000013307 optical fiber Substances 0.000 description 1
- 238000003672 processing method Methods 0.000 description 1
- 230000002441 reversible effect Effects 0.000 description 1
- 239000004065 semiconductor Substances 0.000 description 1
- 230000002123 temporal effect Effects 0.000 description 1
- 238000012546 transfer Methods 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T3/00—Geometric image transformations in the plane of the image
- G06T3/60—Rotation of whole images or parts thereof
- G06T3/608—Rotation of whole images or parts thereof by skew deformation, e.g. two-pass or three-pass rotation
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/10—Segmentation; Edge detection
- G06T7/13—Edge detection
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/30—Subject of image; Context of image processing
- G06T2207/30004—Biomedical image processing
- G06T2207/30041—Eye; Retina; Ophthalmic
Landscapes
- Engineering & Computer Science (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Theoretical Computer Science (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Collating Specific Patterns (AREA)
- Measurement Of The Respiration, Hearing Ability, Form, And Blood Characteristics Of Living Organisms (AREA)
Abstract
Embodiments of the present disclosure disclose iris recognition methods, apparatuses, electronic devices, and computer readable media. One embodiment of the method comprises: determining an image corresponding to an area where eyes are located and included in an image to be recognized according to iris position information corresponding to the image to be recognized so as to generate an eye image; determining a first characteristic point information set according to the eye image; determining a characteristic point matching information set according to the first characteristic point information set and a second characteristic point information set corresponding to the registered iris image; according to the feature point matching information set, performing image rotation on an image to be recognized to generate a rotated image to be recognized; and carrying out iris recognition on the rotated image to be recognized. This embodiment improves the success rate of iris recognition.
Description
Technical Field
Embodiments of the present disclosure relate to the field of computer technologies, and in particular, to an iris identification method, apparatus, electronic device, and computer-readable medium.
Background
Biometric identification is a technique for identity authentication and identification using biologically stable, unique and reliable physiological or behavioral characteristics. Wherein, human irises are randomly generated in the embryonic period, which causes that the physiological structure of the iris of each person is different and hardly changes along with the change of time. Therefore, the iris-based biometric identification is an excellent identification method. At present, when iris recognition is performed, the method generally adopted is as follows: iris acquisition and identification are carried out through iris acquisition equipment which is fixedly installed.
However, when the above-described manner is adopted, there are often technical problems as follows:
when the eyes of the object to be detected and the iris acquisition device are not kept horizontal, the situation that the characteristic points corresponding to the acquired iris images are staggered with the characteristic points of the iris images registered in advance is often caused, and therefore the iris identification success rate is low.
Disclosure of Invention
This summary is provided to introduce a selection of concepts in a simplified form that are further described below in the detailed description. This summary is not intended to identify key features or essential features of the claimed subject matter, nor is it intended to be used to limit the scope of the claimed subject matter.
Some embodiments of the present disclosure propose iris recognition methods, apparatuses, electronic devices and computer readable media to solve one or more of the technical problems mentioned in the background section above.
In a first aspect, some embodiments of the present disclosure provide a method of iris recognition, the method including: determining an image corresponding to an area where eyes are located and included in an image to be recognized according to iris position information corresponding to the image to be recognized so as to generate an eye image; determining a first characteristic point information set according to the eye image; determining a characteristic point matching information set according to the first characteristic point information set and a second characteristic point information set corresponding to the registered iris image; according to the feature point matching information set, performing image rotation on the image to be recognized to generate a rotated image to be recognized; and carrying out iris recognition on the rotated image to be recognized.
In a second aspect, some embodiments of the present disclosure provide an iris recognition apparatus, including: the first determining unit is configured to determine an image corresponding to an area where eyes are located included in an image to be recognized according to iris position information corresponding to the image to be recognized so as to generate an eye image; a second determination unit configured to determine a first feature point information set from the eye image; a third determining unit configured to determine a feature point matching information set according to the first feature point information set and a second feature point information set corresponding to the registered iris image; an image rotation unit configured to perform image rotation on the image to be recognized according to the feature point matching information set to generate a rotated image to be recognized; and the iris recognition unit is configured to perform iris recognition on the rotated image to be recognized.
In a third aspect, some embodiments of the present disclosure provide an electronic device, comprising: one or more processors; a storage device having one or more programs stored thereon, which when executed by one or more processors, cause the one or more processors to implement the method described in any of the implementations of the first aspect.
In a fourth aspect, some embodiments of the present disclosure provide a computer readable medium on which a computer program is stored, wherein the program, when executed by a processor, implements the method described in any of the implementations of the first aspect.
The above embodiments of the present disclosure have the following advantages: by the iris identification method of some embodiments of the present disclosure, the success rate of iris identification is improved. Specifically, the reason why the success rate of iris recognition is low is that: when the eye of the object to be detected and the iris acquisition device are not kept horizontal, the situation that the characteristic points corresponding to the acquired iris image are staggered with the characteristic points of the iris image registered in advance is often caused. Based on this, in the iris identification method according to some embodiments of the present disclosure, first, an image corresponding to an area where an eye included in an image to be identified is located is determined according to iris position information corresponding to the image to be identified, so as to generate an eye image. In practical situations, due to the influence of factors such as the installation position of the iris acquisition device and the acquisition range of the iris acquisition device, the acquired image often contains other facial features except for the iris area. Therefore, the eye image is generated according to the iris position information, so that the image of the non-eye area is eliminated, and the subsequent image data processing amount is reduced. Next, a first feature point information set is determined from the eye image. In practical situations, the eye image is directly compared with the registered iris image, and the data processing amount is large. Therefore, the first feature point information set is determined, and the data processing amount can be further reduced on the premise of keeping the features of the eye image. And then, determining a characteristic point matching information set according to the first characteristic point information set and a second characteristic point information set corresponding to the registered iris image. Through feature point matching, the corresponding relation between the first feature point information in the first feature point information set and the second feature point information in the second feature point information set is determined. Further, according to the feature point matching information set, image rotation is performed on the image to be recognized, so that a rotated image to be recognized is generated. In practical situations, when the eyes included in the acquired image to be recognized are not tilted, a connecting line of two feature points corresponding to the feature point matching information in the feature point matching information set should be approximate to a horizontal line. Therefore, it is possible to determine whether or not the eye included in the image to be recognized is tilted and the angle of the tilt at the time of the tilt from the feature point matching information set. Further, according to the feature point matching information set, image rotation is performed on the image to be recognized, so that a rotated image to be recognized is generated. And finally, carrying out iris recognition on the rotated image to be recognized. In this way, even when the eye of the object to be detected and the iris collecting device are not kept horizontal, iris recognition can be successfully performed.
Drawings
The above and other features, advantages and aspects of various embodiments of the present disclosure will become more apparent by referring to the following detailed description when taken in conjunction with the accompanying drawings. Throughout the drawings, the same or similar reference numbers refer to the same or similar elements. It should be understood that the drawings are schematic and that elements and elements are not necessarily drawn to scale.
Fig. 1 is a schematic diagram of an application scenario of an iris recognition method of some embodiments of the present disclosure;
fig. 2 is a flow diagram of some embodiments of an iris recognition method according to the present disclosure;
FIG. 3 is a flow chart of further embodiments of iris recognition methods according to the present disclosure;
FIG. 4 is a schematic structural diagram of some embodiments of an iris recognition apparatus according to the present disclosure;
FIG. 5 is a schematic structural diagram of an electronic device suitable for use in implementing some embodiments of the present disclosure.
Detailed Description
Embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. While certain embodiments of the present disclosure are shown in the drawings, it is to be understood that the disclosure may be embodied in various forms and should not be construed as limited to the embodiments set forth herein. Rather, these embodiments are provided for a more thorough and complete understanding of the present disclosure. It should be understood that the drawings and embodiments of the disclosure are for illustration purposes only and are not intended to limit the scope of the disclosure.
It should be noted that, for convenience of description, only the portions related to the related invention are shown in the drawings. The embodiments and features of the embodiments in the present disclosure may be combined with each other without conflict.
It should be noted that the terms "first", "second", and the like in the present disclosure are only used for distinguishing different devices, modules or units, and are not used for limiting the order or interdependence relationship of the functions performed by the devices, modules or units.
It is noted that references to "a", "an", and "the" modifications in this disclosure are intended to be illustrative rather than limiting, and that those skilled in the art will recognize that "one or more" may be used unless the context clearly dictates otherwise.
The names of messages or information exchanged between devices in the embodiments of the present disclosure are for illustrative purposes only, and are not intended to limit the scope of the messages or information.
The present disclosure will be described in detail below with reference to the accompanying drawings in conjunction with embodiments.
Fig. 1 is a schematic diagram of an application scenario of an iris identification method according to some embodiments of the present disclosure.
In the application scenario of fig. 1, first, the computing device 101 may determine, according to the iris position information 103 corresponding to the image 102 to be recognized, an image corresponding to a region where an eye included in the image 102 to be recognized is located, so as to generate an eye image 104; secondly, the computing device 101 may determine a first feature point information set 105 from the eye image 104; then, the computing device 101 may determine a feature point matching information set 107 according to the first feature point information set 105 and a second feature point information set 106 corresponding to the registered iris image; further, the computing device 101 may perform image rotation on the image to be recognized 102 according to the feature point matching information set 107 to generate a rotated image to be recognized 108; finally, the computing device 101 may perform iris recognition on the rotated image to be recognized 108.
The computing device 101 may be hardware or software. When the computing device is hardware, it may be implemented as a distributed cluster composed of multiple servers or terminal devices, or may be implemented as a single server or a single terminal device. When the computing device is embodied as software, it may be installed in the hardware devices enumerated above. It may be implemented, for example, as multiple software or software modules to provide distributed services, or as a single software or software module. And is not particularly limited herein.
It should be understood that the number of computing devices in FIG. 1 is merely illustrative. There may be any number of computing devices, as implementation needs dictate.
With continued reference to fig. 2, a flow 200 of some embodiments of iris recognition methods according to the present disclosure is shown. The iris identification method comprises the following steps:
In some embodiments, an executing subject (e.g., the computing device 101 shown in fig. 1) of the iris recognition method may determine, according to the iris position information corresponding to the image to be recognized, an image corresponding to a region where an eye included in the image to be recognized is located, so as to generate the eye image. The image to be recognized may be an image of an object to be detected for iris recognition, which is acquired by an iris acquisition device. For example, the image to be recognized may be an image including a face of the object to be detected. The object to be detected can be a user needing iris recognition. The iris position information may represent a position of an iris in an eye included in the image to be recognized. The eye image may be an image corresponding to a region where the eye included in the image to be recognized is located. The execution body can extract the image in the area corresponding to the iris position information to generate the canthus image
Alternatively, the above-mentioned executed iris position information may be determined by: and carrying out iris positioning on the image to be recognized so as to determine the position of the iris included in the image to be recognized and obtain the iris position information. Wherein, the executing body can determine the iris position information through an iris positioning algorithm. For example, the iris localization algorithm may be, but is not limited to, any of the following: the method comprises a Daugman iris positioning algorithm, a Wildes iris circle positioning algorithm, an iris recognition algorithm based on combination of wavelet transformation and zero crossing point detection and an iris positioning method based on Hough transformation.
In some embodiments, the executing body may determine the first feature point information set according to the eye image. The first feature point information in the first feature point information set may be information corresponding to a feature point included in the corner of the eye image. The executing body may determine feature points included in the eye image by a feature point detection algorithm to generate the first feature point information set. For example, the above feature point detection algorithm may be, but is not limited to, any one of the following: harris feature point detection algorithm, Moravec feature point detection algorithm, and ORB (organized FAST and rotaed BRIEF) feature point detection algorithm.
And step 203, determining a characteristic point matching information set according to the first characteristic point information set and a second characteristic point information set corresponding to the registered iris image.
In some embodiments, the execution subject may determine the feature point matching information set according to the first feature point information set and a second feature point information set corresponding to the registered iris image. The registered iris image may be a previously acquired iris image of the user. The second feature point information in the second feature point information set may be information corresponding to a feature point included in the registered iris image. The feature point matching information in the feature point matching information set may include feature points corresponding to first feature point information and feature points corresponding to second feature point information that are matched with each other. The executing body may perform feature point matching on feature points corresponding to first feature point information in the first feature point information set and feature point information corresponding to second feature point information in the second feature point information set by using a feature point matching algorithm to generate the feature point matching information set. For example, the above feature point matching algorithm may be, but is not limited to, any of the following: SURF (Speeded Up Robust Features) Feature point matching algorithm and SIFT (Scale-Invariant Feature Transform) Feature point matching algorithm.
And 204, performing image rotation on the image to be recognized according to the feature point matching information set to generate a rotated image to be recognized.
In some embodiments, the executing entity may perform image rotation on the image to be recognized according to the feature point matching information set to generate the rotated image to be recognized.
As an example, first, the execution main body may determine an angle between a connection line of two feature points included in each feature point matching information in the feature point matching information set and a horizontal direction to generate an included angle value, so as to obtain an included angle value set. Then, the executing body may determine a mean value of each included angle value in the set of included angle values as a rotation angle value. Finally, the executing body may control the image to be recognized to rotate counterclockwise by the rotation angle value, so as to generate the rotated image to be recognized.
And step 205, performing iris recognition on the rotated image to be recognized.
In some embodiments, the executing body may perform iris recognition on the rotated image to be recognized.
As an example, first, the executing body may perform taylor expansion on the rotated image to be recognized, and extract a first derivative and a second derivative after taylor expansion, respectively, to generate a target feature matrix. Then, the executing body may compare the target feature matrix with a feature matrix corresponding to the registered iris image. And responding to the fact that the target characteristic matrix is consistent with the characteristic matrix corresponding to the registered iris image, and then the object to be detected passes through iris recognition. And responding to the fact that the target characteristic matrix is not consistent with the characteristic matrix corresponding to the registered iris image, and determining that the object to be detected does not pass iris recognition.
The above embodiments of the present disclosure have the following advantages: by the iris identification method of some embodiments of the present disclosure, the success rate of iris identification is improved. Specifically, the reason why the success rate of iris recognition is low is that: when the eye of the object to be detected and the iris acquisition device are not kept horizontal, the situation that the characteristic points corresponding to the acquired iris image are staggered with the characteristic points of the iris image registered in advance is often caused. Based on this, in the iris identification method according to some embodiments of the present disclosure, first, an image corresponding to an area where an eye included in an image to be identified is located is determined according to iris position information corresponding to the image to be identified, so as to generate an eye image. In practical situations, due to the influence of factors such as the installation position of the iris acquisition device and the acquisition range of the iris acquisition device, the acquired image often contains other facial features except for the iris area. Therefore, the eye image is generated according to the iris position information, so that the image of the non-eye area is eliminated, and the subsequent image data processing amount is reduced. Next, a first feature point information set is determined from the eye image. In practical situations, the eye image is directly compared with the registered iris image, and the data processing amount is large. Therefore, the first feature point information set is determined, and the data processing amount can be further reduced on the premise of keeping the features of the eye image. And then, determining a characteristic point matching information set according to the first characteristic point information set and a second characteristic point information set corresponding to the registered iris image. Through feature point matching, the corresponding relation between the first feature point information in the first feature point information set and the second feature point information in the second feature point information set is determined. Further, according to the feature point matching information set, image rotation is performed on the image to be recognized, so that a rotated image to be recognized is generated. In practical situations, when the eyes included in the acquired image to be recognized are not tilted, a connecting line of two feature points corresponding to the feature point matching information in the feature point matching information set should be approximate to a horizontal line. Therefore, it is possible to determine whether or not the eye included in the image to be recognized is tilted and the angle of the tilt at the time of the tilt from the feature point matching information set. Further, according to the feature point matching information set, image rotation is performed on the image to be recognized, so that a rotated image to be recognized is generated. And finally, carrying out iris recognition on the rotated image to be recognized. In this way, even when the eye of the object to be detected and the iris collecting device are not kept horizontal, iris recognition can be successfully performed.
With further reference to fig. 3, a flow 300 of further embodiments of iris recognition methods is illustrated. The process 300 of the iris identification method includes the following steps:
In some embodiments, the specific implementation of step 301 and the technical effect thereof may refer to step 201 in those embodiments corresponding to fig. 2, and are not described herein again.
In some embodiments, a subject performing the iris recognition method (e.g., computing device 101 shown in fig. 1) may perform image feature pre-processing on the eye image to generate the pre-processed eye image. The image feature preprocessing may be a process of smoothing detail features included in the corner of the eye image and retaining large-scale contour features. For example, the image feature pre-processing described above may be, but is not limited to, at least one of: image denoising processing, image enhancement processing and image smoothing processing.
As an example, the execution subject may implement image noise reduction processing on the eye corner image by a target noise reduction algorithm. The target noise reduction algorithm may be, but is not limited to, any of the following: the image denoising method comprises an image denoising algorithm based on Gaussian filtering, an image denoising algorithm based on median filtering and an image denoising algorithm based on bilateral filtering.
As still another example, the executing subject may implement image enhancement processing on the corner of the eye image by a target enhancement algorithm. The target enhancement algorithm may be, but is not limited to, any of the following: the image processing method comprises an image sharpening algorithm based on Laplace operators, an image equalization algorithm based on histograms, an image enhancement algorithm based on wavelet transformation and an image enhancement algorithm based on partial differential equations.
As still another example, the execution subject may implement image smoothing processing on the corner of the eye image through an image smoothing algorithm. The image smoothing algorithm may be, but is not limited to, any one of the following: an image smoothing algorithm based on the temporal domain averaging and a gaussian smoothing algorithm.
In some embodiments, the executing body may perform feature point detection on the pre-processed eye image through a feature point detection algorithm to determine first feature point information corresponding to the feature point, so as to obtain the first feature point information set. The feature point detection algorithm may be, but is not limited to, any one of the following: harris feature point detection algorithm, Moravec feature point detection algorithm, and ORB (organized FAST and rotaed BRIEF) feature point detection algorithm.
And 304, determining a feature point matching information set according to the first feature point information set and a second feature point information set corresponding to the registered iris image.
In some embodiments, the determining, by the executing entity, the feature point matching information set according to the first feature point information set and the second feature point information set corresponding to the registered iris image may include:
the first step is to determine the distance between the feature point corresponding to each second feature point information in the second feature point information set and the feature point corresponding to each first feature point information in the first feature point information set to generate a feature distance information set, so as to obtain a feature distance information set.
As an example, the execution subject may generate the feature distance information group by determining a euclidean distance between a feature point corresponding to the second feature point information and a feature point corresponding to each of the first feature point information in the first feature point information set.
And secondly, screening out the characteristic distance information meeting the characteristic point screening condition from the characteristic distance information groups as candidate characteristic distance information for each characteristic distance information group in the characteristic distance information group set.
The screening condition may be that the feature distance information is the feature distance information corresponding to the minimum distance value corresponding to the feature distance information group, and the distance value corresponding to the feature distance information is greater than a preset threshold. The preset threshold may be manually set.
And thirdly, generating feature point matching information according to each candidate feature distance information in the obtained at least one candidate feature distance information to obtain the feature point matching information set.
The execution subject may determine, as feature point matching information, first feature point information and second feature point information corresponding to two feature points corresponding to the candidate feature distance information.
And 305, performing image rotation on the image to be recognized according to the feature point matching information set to generate a rotated image to be recognized.
In some embodiments, the executing entity may perform image rotation on the image to be recognized according to the feature point matching information set to generate the rotated image to be recognized. Wherein, the executing body may execute the following processing steps according to the feature point matching information set:
the first step is that feature point matching information with a first target number is randomly selected from the feature point matching information set and is used as candidate feature point matching information, and a candidate feature point matching information sequence is obtained.
For example, the first target number may be 2.
And secondly, determining angle information corresponding to the matching information of the two candidate feature points in the candidate feature point matching information sequence to obtain an angle information group.
Wherein, the angle information in the angle information group comprises: a first angle value and a second angle value.
For example, the above-described two candidate feature point matching information may include candidate feature point matching information a and candidate feature point matching information B. Wherein, the feature point matching information A corresponds to the feature point P1And a feature point P2. The feature point matching information B corresponds to the feature point P3And a feature point P4. Specially for treating diabetesSign point P1And a feature point P3The feature point information may be a feature point corresponding to the first feature point information in the first feature point information set. Characteristic point P2And a feature point P4The feature point information may be a feature point corresponding to the second feature point information in the second feature point information set. Characteristic point P1May be (x)1，y1). Characteristic point P2May be (x)2，y2). Characteristic point P3May be (x)3，y3). Characteristic point P4May be (x)4，y4)。
The above-mentioned calculation formula of the first angle value may be as follows:
wherein σ1Representing the first angle value described above.
The above-mentioned calculation formula of the second angle value may be as follows:
wherein σ2Representing the second angle value.
And thirdly, taking the included angle value of the first angle value and the second angle value included by each angle information in the angle information group as a target angle value, and adding the target angle value to the target angle value sequence.
Wherein, the sequence of the target angle values is initially null. The execution body may determine an absolute value of an angle value between the first angle value and the second angle value included in the angle information as a target angle value.
And fourthly, responding to the fact that the number of the target included angle values in the target angle value sequence is smaller than or equal to the second target number, and executing the processing steps again.
The second target number may be a product of a number of target angle values obtained by performing the processing step once and a preset number of cycles.
And fifthly, determining the target angle value with the occurrence times meeting the angle value screening condition in the target angle value sequence as a rotation angle value in response to the fact that the number of the target included angle values in the target angle value sequence is larger than the second target number.
The angle value screening condition may be that the target angle value appears most frequently in the target angle value sequence.
And sixthly, performing image rotation on the image to be recognized according to the rotation angle value to generate the rotated image to be recognized.
For example, the executing body may control the image to be recognized to rotate clockwise by an angle corresponding to the rotation angle value, so as to generate the rotated image to be recognized.
And step 306, performing inner and outer circle segmentation on the rotated image to be recognized to generate an iris area image.
In some embodiments, the executing entity may perform inner and outer circle segmentation on the rotated image to be recognized through an edge detection algorithm to generate the iris region image. For example, the edge detection algorithm may be, but is not limited to, any of the following: an edge detection algorithm based on the Canny operator and an edge detection algorithm based on the Sobel operator.
In some embodiments, the executing entity may perform image expansion on the iris region image to generate the iris expanded image.
As an example, the executing body may perform polar coordinate conversion on the iris region image according to centers of inner and outer circles included in the iris region image to generate the iris expanded image.
And 308, performing iris recognition according to the iris expansion image and the registered iris image.
In some embodiments, the executing entity may perform iris recognition based on the iris expansion image and the registered iris image. The registered iris image may be a previously acquired iris image of the user.
As an example, the executing body may determine whether the iris recognition is passed by comparing a feature point included in the iris expansion image with a feature point included in the registered iris image to determine whether the feature point corresponds to the feature point.
As can be seen from fig. 3, compared with the description of some embodiments corresponding to fig. 2, the present disclosure considers that an error may occur when feature points are matched, i.e., a feature point that is not corresponding to the present disclosure is determined as a corresponding feature point. Under the condition, the included angle between the straight line formed by the connecting lines of each pair of characteristic points and the horizontal line is determined, and the mean value is calculated, so that the deviation between the finally obtained rotating angle and the actual rotating angle is caused. Further, the feature points cannot be aligned in iris recognition. Thus, iris recognition fails. Therefore, the target angle value sequence is generated, and the target angle value with the largest occurrence frequency is selected as the rotation angle value, so that the problem that the finally obtained rotation angle is deviated from the actual rotation angle due to the adoption of the mean value solving mode is solved. Therefore, the iris identification accuracy is greatly improved.
With further reference to fig. 4, as an implementation of the methods shown in the above figures, the present disclosure provides some embodiments of an iris recognition apparatus, which correspond to those shown in fig. 2, and which may be applied in various electronic devices in particular.
As shown in fig. 4, an iris recognition apparatus 400 of some embodiments includes: a first determination unit 401, a second determination unit 402, a third determination unit 403, an image rotation unit 404, and an iris recognition unit 405. The first determining unit 401 is configured to determine, according to iris position information corresponding to an image to be recognized, an image corresponding to a region where an eye included in the image to be recognized is located, so as to generate an eye image; a second determining unit 402 configured to determine a first feature point information set according to the eye image; a third determining unit 403 configured to determine a feature point matching information set according to the first feature point information set and a second feature point information set corresponding to the registered iris image; an image rotation unit 404 configured to perform image rotation on the image to be recognized according to the feature point matching information set to generate a rotated image to be recognized; and an iris recognition unit 405 configured to perform iris recognition on the rotated image to be recognized.
It will be understood that the elements described in the apparatus 400 correspond to various steps in the method described with reference to fig. 2. Thus, the operations, features and resulting advantages described above with respect to the method are also applicable to the apparatus 400 and the units included therein, and will not be described herein again.
Referring now to FIG. 5, a block diagram of an electronic device (such as computing device 101 shown in FIG. 1)500 suitable for use in implementing some embodiments of the present disclosure is shown. The electronic device shown in fig. 5 is only an example, and should not bring any limitation to the functions and the scope of use of the embodiments of the present disclosure.
As shown in fig. 5, electronic device 500 may include a processing means (e.g., central processing unit, graphics processor, etc.) 501 that may perform various appropriate actions and processes in accordance with a program stored in a Read Only Memory (ROM)502 or a program loaded from a storage means 508 into a Random Access Memory (RAM) 503. In the RAM 503, various programs and data necessary for the operation of the electronic apparatus 500 are also stored. The processing device 501, the ROM 502, and the RAM 503 are connected to each other through a bus 504. An input/output (I/O) interface 505 is also connected to bus 504.
Generally, the following devices may be connected to the I/O interface 505: input devices 506 including, for example, a touch screen, touch pad, keyboard, mouse, camera, microphone, accelerometer, gyroscope, etc.; output devices 507 including, for example, a Liquid Crystal Display (LCD), speakers, vibrators, and the like; storage devices 508 including, for example, magnetic tape, hard disk, etc.; and a communication device 509. The communication means 509 may allow the electronic device 500 to communicate with other devices wirelessly or by wire to exchange data. While fig. 5 illustrates an electronic device 500 having various means, it is to be understood that not all illustrated means are required to be implemented or provided. More or fewer devices may alternatively be implemented or provided. Each block shown in fig. 5 may represent one device or may represent multiple devices as desired.
In particular, according to some embodiments of the present disclosure, the processes described above with reference to the flow diagrams may be implemented as computer software programs. For example, some embodiments of the present disclosure include a computer program product comprising a computer program embodied on a computer readable medium, the computer program comprising program code for performing the method illustrated in the flow chart. In some such embodiments, the computer program may be downloaded and installed from a network via the communication means 509, or installed from the storage means 508, or installed from the ROM 502. The computer program, when executed by the processing device 501, performs the above-described functions defined in the methods of some embodiments of the present disclosure.
It should be noted that the computer readable medium described in some embodiments of the present disclosure may be a computer readable signal medium or a computer readable storage medium or any combination of the two. A computer readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the foregoing. More specific examples of the computer readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In some embodiments of the disclosure, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. In some embodiments of the present disclosure, however, a computer readable signal medium may include a propagated data signal with computer readable program code embodied therein, for example, in baseband or as part of a carrier wave. Such a propagated data signal may take many forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. A computer readable signal medium may also be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to: electrical wires, optical cables, RF (radio frequency), etc., or any suitable combination of the foregoing.
In some embodiments, the clients, servers may communicate using any currently known or future developed network Protocol, such as HTTP (HyperText Transfer Protocol), and may interconnect with any form or medium of digital data communication (e.g., a communications network). Examples of communication networks include a local area network ("LAN"), a wide area network ("WAN"), the Internet (e.g., the Internet), and peer-to-peer networks (e.g., ad hoc peer-to-peer networks), as well as any currently known or future developed network.
The computer readable medium may be embodied in the electronic device; or may exist separately without being assembled into the electronic device. The computer readable medium carries one or more programs which, when executed by the electronic device, cause the electronic device to: determining an image corresponding to an area where eyes are located and included in an image to be recognized according to iris position information corresponding to the image to be recognized so as to generate an eye image; determining a first characteristic point information set according to the eye image; determining a characteristic point matching information set according to the first characteristic point information set and a second characteristic point information set corresponding to the registered iris image; according to the feature point matching information set, performing image rotation on the image to be recognized to generate a rotated image to be recognized; and carrying out iris recognition on the rotated image to be recognized.
Computer program code for carrying out operations for embodiments of the present disclosure may be written in any combination of one or more programming languages, including an object oriented programming language such as Java, Smalltalk, C + +, and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or the connection may be made to an external computer (for example, through the Internet using an Internet service provider).
The flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present disclosure. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
The units described in some embodiments of the present disclosure may be implemented by software, and may also be implemented by hardware. The described units may also be provided in a processor, and may be described as: a processor includes a first determining unit, a second determining unit, a third determining unit, an image rotating unit, and an iris recognizing unit. The names of the units do not form a limitation on the units themselves in some cases, for example, the first determining unit may also be described as a unit that determines an image corresponding to a region where the eyes included in the image to be recognized are located according to iris position information corresponding to the image to be recognized to generate an eye image.
The functions described herein above may be performed, at least in part, by one or more hardware logic components. For example, without limitation, exemplary types of hardware logic components that may be used include: field Programmable Gate Arrays (FPGAs), Application Specific Integrated Circuits (ASICs), Application Specific Standard Products (ASSPs), systems on a chip (SOCs), Complex Programmable Logic Devices (CPLDs), and the like.
The foregoing description is only exemplary of the preferred embodiments of the disclosure and is illustrative of the principles of the technology employed. It will be appreciated by those skilled in the art that the scope of the invention in the embodiments of the present disclosure is not limited to the specific combination of the above-mentioned features, but also encompasses other embodiments in which any combination of the above-mentioned features or their equivalents is made without departing from the inventive concept as defined above. For example, the above features and (but not limited to) technical features with similar functions disclosed in the embodiments of the present disclosure are mutually replaced to form the technical solution.
Claims (11)
1. An iris recognition method comprising:
determining an image corresponding to an area where eyes are located and included in an image to be recognized according to iris position information corresponding to the image to be recognized so as to generate an eye image;
determining a first feature point information set according to the eye image;
determining a characteristic point matching information set according to the first characteristic point information set and a second characteristic point information set corresponding to the registered iris image;
according to the feature point matching information set, performing image rotation on the image to be recognized to generate a rotated image to be recognized;
and carrying out iris recognition on the rotated image to be recognized.
2. The method of claim 1, wherein said determining a first set of feature point information from the eye image comprises:
performing image feature preprocessing on the eye image to generate a preprocessed eye image;
and detecting the characteristic points of the preprocessed eye image to determine first characteristic point information corresponding to the characteristic points to obtain the first characteristic point information set.
3. The method of claim 1, wherein the iris position information is determined by:
and carrying out iris positioning on the image to be recognized so as to determine the position of the iris included in the image to be recognized and obtain the iris position information.
4. The method of claim 1, wherein determining a set of feature point matching information from the first set of feature point information and a second set of feature point information corresponding to registered iris images comprises:
determining a distance between a feature point corresponding to each piece of second feature point information in the second feature point information set and a feature point corresponding to each piece of first feature point information in the first feature point information set to generate a feature distance information group to obtain a feature distance information group set;
for each characteristic distance information group in the characteristic distance information group set, screening out characteristic distance information meeting characteristic point screening conditions from the characteristic distance information groups as candidate characteristic distance information;
and generating feature point matching information according to each candidate feature distance information in the obtained at least one candidate feature distance information to obtain the feature point matching information set.
5. The method of claim 1, wherein the iris recognition of the rotated image to be recognized comprises:
carrying out inner and outer circle segmentation on the rotated image to be recognized so as to generate an iris area image;
performing image expansion on the iris region image to generate an iris expanded image;
and performing iris recognition according to the iris expansion image and the registered iris image.
6. The method according to claim 1, wherein the performing image rotation on the image to be recognized according to the feature point matching information set to generate a rotated image to be recognized comprises:
according to the feature point matching information set, executing the following processing steps:
randomly selecting a first target number of feature point matching information from the feature point matching information set as candidate feature point matching information to obtain a candidate feature point matching information sequence;
determining angle information corresponding to two candidate feature point matching information in the candidate feature point matching information sequence to obtain an angle information group, wherein the angle information in the angle information group comprises: a first angle value and a second angle value;
adding an included angle value of a first angle value and a second angle value included by each angle information in the angle information group as a target angle value to a target angle value sequence, wherein the target angle value sequence is empty initially;
the processing steps are performed again in response to determining that the number of target pinch angle values in the sequence of target angle values is less than or equal to a second target number.
7. The method according to claim 6, wherein the image rotation is performed on the image to be recognized according to the feature point matching information set to generate a rotated image to be recognized, further comprising:
and in response to the fact that the number of the target included angle values in the target angle value sequence is larger than the second target number, determining the target angle values of which the occurrence times in the target angle value sequence meet the angle value screening condition as rotation angle values.
8. The method according to claim 7, wherein the image rotation is performed on the image to be recognized according to the feature point matching information set to generate a rotated image to be recognized, further comprising:
and according to the rotation angle value, performing image rotation on the image to be recognized to generate the rotated image to be recognized.
9. An iris recognition apparatus comprising:
the device comprises a first determining unit, a second determining unit and a processing unit, wherein the first determining unit is configured to determine an image corresponding to an area where eyes are located included in an image to be recognized according to iris position information corresponding to the image to be recognized so as to generate an eye image;
a second determination unit configured to determine a first feature point information set from the eye image;
a third determination unit configured to determine a feature point matching information set according to the first feature point information set and a second feature point information set corresponding to the registered iris image;
the image rotation unit is configured to perform image rotation on the image to be recognized according to the feature point matching information set so as to generate a rotated image to be recognized;
and the iris recognition unit is configured to perform iris recognition on the rotated image to be recognized.
10. An electronic device, comprising:
one or more processors;
a storage device having one or more programs stored thereon;
when executed by the one or more processors, cause the one or more processors to implement the method of any one of claims 1-8.
11. A computer-readable medium, on which a computer program is stored, wherein the program, when executed by a processor, implements the method of any one of claims 1 to 8.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202111171102.6A CN113936329A (en) | 2021-10-08 | 2021-10-08 | Iris recognition method, iris recognition device, electronic equipment and computer readable medium |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202111171102.6A CN113936329A (en) | 2021-10-08 | 2021-10-08 | Iris recognition method, iris recognition device, electronic equipment and computer readable medium |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN113936329A true CN113936329A (en) | 2022-01-14 |
Family
ID=79278210
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202111171102.6A Pending CN113936329A (en) | 2021-10-08 | 2021-10-08 | Iris recognition method, iris recognition device, electronic equipment and computer readable medium |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN113936329A (en) |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115083006A (en) * | 2022-08-11 | 2022-09-20 | 北京万里红科技有限公司 | Iris recognition model training method, iris recognition method and iris recognition device |
-
2021
- 2021-10-08 CN CN202111171102.6A patent/CN113936329A/en active Pending
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115083006A (en) * | 2022-08-11 | 2022-09-20 | 北京万里红科技有限公司 | Iris recognition model training method, iris recognition method and iris recognition device |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| US20220044009A1 (en) | Face verifying method and apparatus | |
| EP3382601B1 (en) | Face verifying method and apparatus | |
| CN108509915B (en) | Method and device for generating face recognition model | |
| KR102113911B1 (en) | Feature extraction and matching and template update for biometric authentication | |
| EP3525165B1 (en) | Method and apparatus with image fusion | |
| US11636705B2 (en) | Method and apparatus for preprocessing fingerprint image | |
| CN105681324B (en) | Internet financial transaction system and method | |
| CN111915480A (en) | Method, apparatus, device and computer readable medium for generating feature extraction network | |
| Ilankumaran et al. | Multi-biometric authentication system using finger vein and iris in cloud computing | |
| CN113936329A (en) | Iris recognition method, iris recognition device, electronic equipment and computer readable medium | |
| CN112949576B (en) | Attitude estimation method, apparatus, device and storage medium | |
| CN111783677B (en) | Face recognition method, device, server and computer readable medium | |
| CN114882576A (en) | Face recognition method, electronic device, computer-readable medium, and program product | |
| CN113780239A (en) | Iris recognition method, iris recognition device, electronic equipment and computer readable medium | |
| CN114120423A (en) | Face image detection method and device, electronic equipment and computer readable medium | |
| CN113869198A (en) | Iris image processing method, iris image processing device, electronic equipment and computer readable medium | |
| AU2018284102B2 (en) | System and method for generating a photographic police lineup | |
| CN112070022A (en) | Face image recognition method and device, electronic equipment and computer readable medium | |
| CN111126229A (en) | Data processing method and device | |
| CN114694238A (en) | Iris feature extraction method, iris feature extraction device, electronic equipment and computer readable medium | |
| CN114882308A (en) | Biological feature extraction model training method and image segmentation method | |
| CN111832533A (en) | Authentication method, device, system, electronic equipment and readable storage medium | |
| CN113780044A (en) | Face detection and recognition method and device | |
| CN114202805A (en) | Living body detection method, living body detection device, electronic apparatus, and storage medium | |
| CN117690193A (en) | Signature user authentication method, device, electronic equipment and storage medium |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination |