CN113095182A - Iris feature extraction method and system for human eye image - Google Patents
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- 210000001747 pupil Anatomy 0.000 claims abstract description 29
- 238000000034 method Methods 0.000 claims abstract description 27
- 230000011218 segmentation Effects 0.000 claims abstract description 8
- 230000001815 facial effect Effects 0.000 claims abstract description 4
- 230000005540 biological transmission Effects 0.000 claims description 7
- 238000004364 calculation method Methods 0.000 claims description 7
- 230000000638 stimulation Effects 0.000 claims description 7
- 238000007781 pre-processing Methods 0.000 claims description 6
- 238000004422 calculation algorithm Methods 0.000 claims description 3
- 238000003708 edge detection Methods 0.000 claims description 3
- 238000009499 grossing Methods 0.000 claims description 3
- 238000005286 illumination Methods 0.000 abstract description 5
- 230000004936 stimulating effect Effects 0.000 abstract description 4
- 230000007613 environmental effect Effects 0.000 abstract description 2
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- 238000004590 computer program Methods 0.000 description 2
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- 230000000694 effects Effects 0.000 description 2
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- 210000003786 sclera Anatomy 0.000 description 2
- 238000012795 verification Methods 0.000 description 2
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- 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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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/10—Segmentation; Edge detection
- G06T7/12—Edge-based segmentation
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- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
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Abstract
The invention relates to the technical field of image processing, in particular to a method and a system for extracting iris features of a human eye image, wherein the method comprises the following steps: when a human body is detected, starting a stimulating light source and a camera, and acquiring a facial image of the human body through the camera; extracting an eye image from the face image by adopting an edge segmentation method, and determining a pupil area in the eye image; determining the position range of the iris according to the pupil area; the iris features are extracted from the position range of the iris, and the method avoids the influence of environmental illumination and realizes objective and rapid extraction of the iris features.
Description
Technical Field
The invention relates to the technical field of image processing, in particular to a method and a system for extracting iris features of a human eye image.
Background
When the existing access control system adopts a face recognition scheme, due to the influence of ambient illumination, the camera easily collects the background light of the face image, so that the collected actual face image is very dark, the collected face image cannot be clearly recognized, and the accuracy and the efficiency of the face recognition are reduced.
The iris is used as the accurate expression of the unique identity characteristic of a human body, has natural advantages when being applied to an access control system, the identification and the characteristic detection of the iris are key links of access control, the detection result of the iris characteristic determines whether the identity verification of the access control passes, and the iris characteristic plays a crucial role in the identity verification of the access control, so that a solution is necessary to be provided, and the iris characteristic can be objectively and quickly extracted.
Disclosure of Invention
The invention aims to provide a method and a system for extracting iris features of a human eye image, which are used for solving one or more technical problems in the prior art and at least providing a beneficial selection or creation condition.
In order to achieve the purpose, the invention provides the following technical scheme:
a method for extracting iris features of human eye images comprises the following steps:
step S100, when a human body is detected, starting a stimulation light source and a camera, and acquiring a facial image of the human body through the camera;
s200, extracting an eye image from the face image by adopting an edge segmentation method, and determining a pupil area in the eye image;
s300, determining the position range of the iris according to the pupil area;
and S400, extracting iris features from the position range of the iris.
Further, in step S200, the determining the pupil area in the eye image includes:
preprocessing the eye image to obtain a gray level image, wherein the preprocessing comprises smoothing denoising processing and graying processing;
integrally sharpening the gray level image to obtain an image to be processed;
carrying out binarization processing on the image to be processed according to a set threshold value to obtain a binarized image;
detecting a light transmission area from the binary image;
and determining a pupil area in the eye image according to the edge gray value distribution of the light transmission area.
Further, the integrally sharpening the grayscale image includes:
updating the gray value of each pixel point in the gray image, wherein the calculation formula of the updated gray value is as follows:
wherein (x)i,yi) Expressing the pixel point with number i in the gray image, and (m, n) expressing the pixel point (x)i,yi) Neighborhood point of, yi-1≤m≤yi+1，xi-3≤n≤xi+3，k∈[0.1,1]G (·) represents the gray value of the corresponding pixel point, w (x)i,yiM, n) are update coefficients; k is an adjusting coefficient of the gray value; by adjusting the updated gray value, the whole image is prevented from being too bright, and the subsequent threshold segmentation is facilitated;
the calculation formula of the update coefficient is as follows:
wherein σ is a standard deviation of the grayscale image.
Further, the step S300 includes:
step S310, determining the minimum external rectangle of the pupil area, and acquiring the length and the width of the minimum external rectangle;
step S320, respectively extending the left side and the right side of the minimum external rectangle to the upper end and the lower end of the eye image to obtain a critical line of the left side and the right side;
step S330, determining the size of a sliding window, and arranging the sliding window at the outer boundary of any critical line;
step S340, carrying out longitudinal sliding search through the sliding window, and extracting a plurality of edge lines from the image to be processed by adopting an edge detection algorithm;
step S350, determining whether the edge lines are connected, if not, executing step S360; if yes, go to step S370;
step S360, after the sliding window is transversely slid for a set distance, step S340 is executed, wherein the set distance is the width of the sliding window;
step S370, connecting the edge lines into an arc line, fitting based on the arc line to obtain a circular area, and taking the circular area as the position range of the iris.
Further, the length of the sliding window is equal to 0.8 to 3 times the length of the minimum outside rectangle, and the width of the sliding window is 1/5 to 1/3 of the width of the minimum outside rectangle.
A computer-readable storage medium, on which an iris feature extraction program of a human eye image is stored, the iris feature extraction program of the human eye image, when being executed by a processor, implementing the steps of the iris feature extraction method of the human eye image as described in any one of the above.
An iris feature extraction system of a human eye image, the system comprising:
at least one processor;
at least one memory for storing at least one program;
when the at least one program is executed by the at least one processor, the at least one processor may implement any one of the above methods for extracting iris features of a human eye image.
The invention has the beneficial effects that: the invention discloses an iris feature extraction method of a human eye image, which avoids the influence of uneven illumination by carrying out image processing and iris feature extraction in a light stimulation environment, and the result of image processing is more objective; because the light spot formed by the stimulating light source is positioned in the pupil, the pupil area can be quickly positioned; because the iris to be positioned is positioned between the pupil and the sclera, the position of the iris can be quickly positioned in the subsequent process according to the roughly determined position range of the pupil, so that the time waste caused by blind search is avoided, and the iris features can be quickly extracted. The invention comprehensively utilizes the photoelectric technology and the image processing technology, can avoid the influence of environmental illumination and realizes the objective and rapid extraction of the iris characteristics.
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 a method for extracting iris features of a human eye image according to an embodiment of the present invention;
FIG. 2 is a schematic diagram of a structure of an image of a human eye according to an embodiment of the invention;
fig. 3 is a schematic structural diagram of an iris feature extraction system of a human eye image in an embodiment of the invention.
Detailed Description
The conception, the specific structure and the technical effects of the present invention will be clearly and completely described in conjunction with the embodiments and the accompanying drawings to fully understand the objects, the schemes and the effects of the present invention. It should be noted that the embodiments and features of the embodiments may be combined with each other without conflict.
Referring to fig. 1 and 2, the present invention provides a method for extracting iris features of a human eye image, the method comprising the steps of:
step S100, when a human body is detected, starting a stimulation light source and a camera, and acquiring a facial image of the human body through the camera;
in the embodiment provided by the invention, a human body is detected through a human body proximity sensor, a controller is respectively connected with the human body proximity sensor, a stimulus light source and a camera, and the controller drives the stimulus light source and the camera to start working when receiving a signal that the human body proximity sensor detects the human body; the LEDs in the stimulating light source are distributed according to a circle, so that uniform scattering of light is realized.
S200, extracting an eye image from the face image by adopting an edge segmentation method, and determining a pupil area in the eye image;
in some embodiments, the eye image is extracted by determining the edge of the eye from the face image by adopting an edge segmentation method; because the light spot formed by the stimulating light source is positioned in the pupil, the pupil area can be quickly positioned.
S300, determining the position range of the iris according to the pupil area;
and S400, extracting iris features from the position range of the iris.
The invention comprehensively utilizes the photoelectric technology and the image processing technology, and discloses an objective and accurate image processing method, which avoids the influence of uneven illumination by carrying out image processing and iris characteristic extraction in a light stimulation environment; after the eye image is determined, then determining a pupil region in the eye image; because the iris to be positioned is positioned between the pupil and the sclera, the position of the iris can be quickly positioned in the subsequent process according to the roughly determined position range of the pupil, so that the time waste caused by blind search is avoided, and the iris features can be quickly extracted.
As a further improvement of the above embodiment, the step S300 includes:
step S310, determining the minimum external rectangle of the pupil area, and acquiring the length and the width of the minimum external rectangle;
step S320, respectively extending the left side and the right side of the minimum external rectangle to the upper end and the lower end of the eye image to obtain a critical line of the left side and the right side; namely a left critical line and a right critical line;
step S330, determining the size of a sliding window, and arranging the sliding window at the outer boundary of any critical line; namely any critical line of the left critical line and the right critical line;
step S340, carrying out longitudinal sliding search through the sliding window, and extracting a plurality of edge lines from the image to be processed by adopting an edge detection algorithm;
step S350, determining whether the edge lines are connected, if not, executing step S360; if yes, go to step S370;
step S360, after the sliding window is transversely slid for a set distance, step S340 is executed, wherein the set distance is the width of the sliding window;
step S370, connecting the edge lines into an arc line, fitting based on the arc line to obtain a circular area, and taking the circular area as the position range of the iris.
Because the iris images are mostly circular, the circular area is obtained based on the arc line fitting, the iris area can be basically included, and the extracted iris features are enough for subsequent feature recognition.
As a further refinement of the above embodiment, the length of the sliding window is equal to 0.8 to 3 times the length of the minimum outside rectangle, and the width of the sliding window is 1/5 to 1/3 times the width of the minimum outside rectangle.
In the embodiment, the pupil area is mostly circular, the obtained minimum external rectangle is close to a square, and the length of the sliding window is as long as possible, so that the probability that the iris edge line can be extracted by longitudinally sliding search once is improved while the calculation amount is prevented from being wasted; the width of the sliding window is as short as possible, and the accuracy of extracting the edge line can be improved.
As a further improvement of the above-described embodiment, the determining the pupil region in the eye image includes:
preprocessing the eye image to obtain a gray level image, wherein the preprocessing comprises smoothing denoising processing and graying processing;
integrally sharpening the gray level image to obtain an image to be processed;
carrying out binarization processing on the image to be processed according to a set threshold value to obtain a binarized image;
detecting a light transmission area from the binary image;
and determining a pupil area in the eye image according to the edge gray value distribution of the light transmission area.
The light-transmitting area refers to an area excluding the light-transmitting area from a circle with the center of the light-transmitting area as the center and a set value as the radius. In the eye image, the pupil, which is a passage through which light enters the eye, generates a light-transmitting region of a certain size, and the gray value of the light-transmitting region in the pupil is larger than that of the iris. Therefore, binarization processing is performed on the eye image according to the set gray level threshold value, and the part higher than the gray level threshold value is used as a light transmission area.
As a further improvement of the foregoing embodiment, the integrally sharpening the grayscale image includes:
updating the gray value of each pixel point in the gray image, wherein the calculation formula of the updated gray value is as follows:
wherein (x)i,yi) Expressing the pixel point with number i in the gray image, and (m, n) expressing the pixel point (x)i,yi) Neighborhood point of, yi-1≤m≤yi+1，xi-3≤n≤xi+3，k∈[0.1,1]G (·) represents the gray value of the corresponding pixel point, w (x)i,yiM, n) are update coefficients; k is an adjusting coefficient of the gray value; by adjusting the updated gray value, the whole image is prevented from being too bright, and the subsequent threshold segmentation is facilitated;
the calculation formula of the update coefficient is as follows:
wherein σ is a standard deviation of the grayscale image.
Corresponding to the method of fig. 1, an embodiment of the present invention further provides a computer-readable storage medium, where an iris feature extraction program of a human eye image is stored, and when the iris feature extraction program of the human eye image is executed by a processor, the steps of the iris feature extraction method of the human eye image according to any one of the above embodiments are implemented.
Referring to fig. 3, corresponding to the method of fig. 1, an embodiment of the present invention further provides an iris feature extraction system for a human eye image, where the system includes: the controller, the controller is connected with human proximity sensor, stimulus light source and camera respectively, the controller includes:
at least one processor;
at least one memory for storing at least one program;
when the at least one program is executed by the at least one processor, the at least one processor may implement the method for extracting iris features of a human eye image according to any of the above embodiments.
Wherein the stimulation light source comprises a plurality of LEDs, and each LED in the stimulation light source is distributed according to a circle; uniform scattering of light is achieved.
The contents in the above method embodiments are all applicable to the present system embodiment, the functions specifically implemented by the present system embodiment are the same as those in the above method embodiment, and the beneficial effects achieved by the present system embodiment are also the same as those achieved by the above method embodiment.
The Processor may be a Central-Processing Unit (CPU), other general-purpose Processor, a Digital Signal Processor (DSP), an Application-Specific-Integrated-Circuit (ASIC), a Field-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 is a control center of the iris feature extraction system of the human eye image, and various interfaces and lines are utilized to connect various parts of the iris feature extraction system operable device of the whole human eye image.
The memory may be used to store the computer program and/or module, and the processor may implement various functions of the iris feature extraction system of the human eye image by operating or executing the computer program and/or module 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 invention 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 rather it is to be construed that the invention effectively covers the intended scope of the invention by virtue of the prior art providing a broad interpretation of such claims in view of the appended claims. Furthermore, the foregoing describes the invention in terms of embodiments foreseen by the inventor for which an enabling description was available, notwithstanding that insubstantial modifications of the invention, not presently foreseen, may nonetheless represent equivalent modifications thereto.
Claims (7)
1. An iris feature extraction method of a human eye image is characterized by comprising the following steps:
step S100, when a human body is detected, starting a stimulation light source and a camera, and acquiring a facial image of the human body through the camera;
s200, extracting an eye image from the face image by adopting an edge segmentation method, and determining a pupil area in the eye image;
s300, determining the position range of the iris according to the pupil area;
and S400, extracting iris features from the position range of the iris.
2. The method for extracting iris features of human eye images as claimed in claim 1, wherein the step S200 of determining the pupil region in the eye image comprises:
preprocessing the eye image to obtain a gray level image, wherein the preprocessing comprises smoothing denoising processing and graying processing;
integrally sharpening the gray level image to obtain an image to be processed;
carrying out binarization processing on the image to be processed according to a set threshold value to obtain a binarized image;
detecting a light transmission area from the binary image;
and determining a pupil area in the eye image according to the edge gray value distribution of the light transmission area.
3. The method for extracting iris features of human eye images as claimed in claim 2, wherein said integrally sharpening said gray-scale image comprises:
updating the gray value of each pixel point in the gray image, wherein the calculation formula of the updated gray value is as follows:
wherein (x)i,yi) Expressing the pixel point with number i in the gray image, and (m, n) expressing the pixel point (x)i,yi) Neighborhood point of, yi-1≤m≤yi+1，xi-3≤n≤xi+3，k∈[0.1,1]G (·) represents the gray value of the corresponding pixel point, w (x)i,yiM, n) are update coefficients; k is an adjusting coefficient of the gray value; by adjusting the updated gray value, the whole image is prevented from being too bright, and the subsequent threshold segmentation is facilitated;
the calculation formula of the update coefficient is as follows:
wherein σ is a standard deviation of the grayscale image.
4. The method of claim 2, wherein the step S300 comprises:
step S310, determining the minimum external rectangle of the pupil area, and acquiring the length and the width of the minimum external rectangle;
step S320, respectively extending the left side and the right side of the minimum external rectangle to the upper end and the lower end of the eye image to obtain a critical line of the left side and the right side;
step S330, determining the size of a sliding window, and arranging the sliding window at the outer boundary of any critical line;
step S340, carrying out longitudinal sliding search through the sliding window, and extracting a plurality of edge lines from the image to be processed by adopting an edge detection algorithm;
step S350, determining whether the edge lines are connected, if not, executing step S360; if yes, go to step S370;
step S360, after the sliding window is transversely slid for a set distance, step S340 is executed; wherein the set distance is the width of the sliding window;
step S370, connecting the edge lines into an arc line, fitting based on the arc line to obtain a circular area, and taking the circular area as the position range of the iris.
5. An iris feature extraction method of a human eye image as claimed in claim 4, wherein the length of the sliding window is equal to 0.8 to 3 times the length of the minimum external rectangle, and the width of the sliding window is 1/5 to 1/3 times the width of the minimum external rectangle.
6. A computer-readable storage medium, on which an iris feature extraction program of a human eye image is stored, the iris feature extraction program of the human eye image implementing the steps of the iris feature extraction method of the human eye image as claimed in any one of claims 1 to 5 when executed by a processor.
7. An iris feature extraction system of a human eye image, the system comprising:
at least one processor;
at least one memory for storing at least one program;
when executed by the at least one processor, cause the at least one processor to implement the method for extracting iris features of an image of a human eye according to any one of claims 1 to 5.
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| CN110929570A (en) * | 2019-10-17 | 2020-03-27 | 珠海虹迈智能科技有限公司 | Iris rapid positioning device and positioning method thereof |
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| CN101807110A (en) * | 2009-02-17 | 2010-08-18 | 由田新技股份有限公司 | Pupil positioning method and system |
| CN105550631A (en) * | 2015-08-25 | 2016-05-04 | 宇龙计算机通信科技(深圳)有限公司 | Iris image acquisition method and apparatus |
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