CN111539258B - Iris image recognition method, iris image recognition device, and storage medium - Google Patents
Iris image recognition method, iris image recognition device, and storage medium Download PDFInfo
- Publication number
- CN111539258B CN111539258B CN202010244416.3A CN202010244416A CN111539258B CN 111539258 B CN111539258 B CN 111539258B CN 202010244416 A CN202010244416 A CN 202010244416A CN 111539258 B CN111539258 B CN 111539258B
- Authority
- CN
- China
- Prior art keywords
- iris image
- processed
- empirical mode
- mode decomposition
- image recognition
- 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.)
- Active
Links
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V40/00—Recognition of biometric, human-related or animal-related patterns in image or video data
- G06V40/10—Human or animal bodies, e.g. vehicle occupants or pedestrians; Body parts, e.g. hands
- G06V40/18—Eye characteristics, e.g. of the iris
- G06V40/193—Preprocessing; Feature extraction
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/24—Classification techniques
- G06F18/243—Classification techniques relating to the number of classes
- G06F18/24323—Tree-organised classifiers
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/25—Fusion techniques
- G06F18/253—Fusion techniques of extracted features
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V40/00—Recognition of biometric, human-related or animal-related patterns in image or video data
- G06V40/10—Human or animal bodies, e.g. vehicle occupants or pedestrians; Body parts, e.g. hands
- G06V40/18—Eye characteristics, e.g. of the iris
- G06V40/197—Matching; Classification
Landscapes
- Engineering & Computer Science (AREA)
- Theoretical Computer Science (AREA)
- Data Mining & Analysis (AREA)
- General Physics & Mathematics (AREA)
- Physics & Mathematics (AREA)
- Computer Vision & Pattern Recognition (AREA)
- General Engineering & Computer Science (AREA)
- Health & Medical Sciences (AREA)
- Evolutionary Computation (AREA)
- Bioinformatics & Computational Biology (AREA)
- Bioinformatics & Cheminformatics (AREA)
- Artificial Intelligence (AREA)
- Life Sciences & Earth Sciences (AREA)
- Evolutionary Biology (AREA)
- General Health & Medical Sciences (AREA)
- Ophthalmology & Optometry (AREA)
- Human Computer Interaction (AREA)
- Multimedia (AREA)
- Collating Specific Patterns (AREA)
- Measurement Of The Respiration, Hearing Ability, Form, And Blood Characteristics Of Living Organisms (AREA)
Abstract
The present disclosure relates to an iris image recognition method, an iris image recognition apparatus, and a storage medium, the iris image recognition method including: acquiring an iris image to be processed; decomposing the iris image to be processed based on the improved two-dimensional empirical mode decomposition to obtain a plurality of two-dimensional empirical mode decomposition components; selecting a preset number of two-dimensional empirical mode decomposition components from the plurality of two-dimensional empirical mode decomposition components for reconstruction, and obtaining a reconstructed iris image to be processed; extracting and fusing the distribution characteristics of the reconstructed iris image to be processed; and identifying the distribution characteristics based on the light gradient lifting frame to obtain an identification result. According to the embodiment of the disclosure, the iris image to be processed is decomposed through the improved BEMD, the iris image is reconstructed by extracting part of decomposition components, the characteristics of the iris image after reconstruction are extracted and fused, and the iris image is identified by means of a lightgbm algorithm, so that noise in the iris image and interference of background factors are effectively eliminated, and the accuracy and efficiency of iris identification are improved.
Description
Technical Field
The present disclosure relates to the field of image processing technologies, and in particular, to an iris image recognition method, an iris image recognition device, and a storage medium.
Background
Biometric identification is a technology for verifying and identifying identity through physiological features or behavioral features inherent to human bodies, wherein iris identification has the advantages of high reliability, good stability and the like, and meanwhile, the iris identification is widely focused by academia, industry, government and army, and is a focus of attention in the field of biometric identification.
When iris recognition is utilized, the quality of an iris image is related to the recognition speed and accuracy, and in the current iris recognition using process, the quality of the collected iris image is poor, the difficulty of iris image recognition is increased, the recognition efficiency is affected, the recognition accuracy is reduced, even the iris recognition is failed, and inconvenience is brought to the use of people.
Disclosure of Invention
To overcome the problems in the related art, the present disclosure provides an iris image recognition method, an iris image recognition apparatus, a human eye recognition method, a human eye recognition apparatus, and a storage medium.
According to an aspect of the embodiments of the present disclosure, there is provided an iris image recognition method including: acquiring an iris image to be processed; decomposing the iris image to be processed based on the improved two-dimensional empirical mode decomposition to obtain a plurality of two-dimensional empirical mode decomposition components; selecting a preset number of two-dimensional empirical mode decomposition components from the plurality of two-dimensional empirical mode decomposition components for reconstruction, and obtaining a reconstructed iris image to be processed; extracting and fusing the distribution characteristics of the reconstructed iris image to be processed; and identifying the distribution characteristics based on the light gradient lifting frame to obtain an identification result.
In one embodiment, decomposing the iris image to be processed based on the improved two-dimensional empirical mode decomposition comprises: in the improved two-dimensional empirical mode decomposition, an envelope surface mean value of the improved two-dimensional empirical mode decomposition is calculated by using a piecewise cubic Hermite polynomial interpolation function.
In one embodiment, decomposing the iris image to be processed based on the improved two-dimensional empirical mode decomposition comprises: and carrying out boundary symmetrical continuation on the iris image to be processed.
In an embodiment, extracting and fusing the distribution characteristics of the reconstructed iris image to be processed includes: and extracting the distribution characteristics of the reconstructed iris image to be processed by using a log Gabor odd function.
In one embodiment, identifying distribution features based on a lightweight gradient lifting framework includes: determining the similarity of the iris images to be processed by utilizing the Hamming distance; and the hamming distance is identified based on the light gradient lifting frame.
In an embodiment, the iris image recognition method further includes: preprocessing an iris image to be processed, wherein the preprocessing comprises; normalizing the iris image to be processed; and blocking the normalized iris image to be processed.
In one embodiment, the reconstructed iris image to be processed includes: and enhancing the reconstructed iris image to be processed based on self-adaptive histogram equalization.
According to an aspect of the embodiments of the present disclosure, there is provided an iris image recognition apparatus including: the acquisition module is used for acquiring an iris image to be processed; the decomposition module is used for decomposing the iris image to be processed based on improved two-dimensional empirical mode decomposition to obtain a plurality of two-dimensional empirical mode decomposition components; the reconstruction module is used for selecting a preset number of two-dimensional empirical mode decomposition components from the plurality of two-dimensional empirical mode decomposition components to reconstruct so as to obtain a reconstructed iris image to be processed; the extraction module is used for extracting and fusing the distribution characteristics of the reconstructed iris image to be processed; and the identification module is used for identifying the distribution characteristics based on the light gradient lifting frame to obtain an identification result.
In an embodiment, when decomposing the iris image to be processed based on the improved two-dimensional empirical mode decomposition, the decomposition module is configured to: in the improved two-dimensional empirical mode decomposition, an envelope surface mean value of the improved two-dimensional empirical mode decomposition is calculated by using a piecewise cubic Hermite polynomial interpolation function.
In an embodiment, when decomposing the iris image to be processed based on the improved two-dimensional empirical mode decomposition, the decomposition module is configured to: and carrying out boundary symmetrical continuation on the iris image to be processed.
In an embodiment, when extracting and fusing the distribution characteristics of the reconstructed iris image to be processed, the extracting module is used for: and extracting the distribution characteristics of the reconstructed iris image to be processed by using a log Gabor odd function.
In an embodiment, when the distribution characteristics are identified based on the light gradient lifting frame, the identification module is used for: determining the similarity of the iris images to be processed by utilizing the Hamming distance; and the hamming distance is identified based on the light gradient lifting frame.
In an embodiment, the iris image recognition apparatus further includes: the preprocessing module is used for preprocessing the iris image to be processed, and the preprocessing comprises the following steps: normalizing the iris image to be processed; and blocking the normalized iris image to be processed.
In an embodiment, when obtaining the reconstructed iris image to be processed, the reconstruction module is configured to: and enhancing the reconstructed iris image to be processed based on self-adaptive histogram equalization.
According to still another aspect of the embodiments of the present disclosure, there is provided an iris image recognition apparatus including: a processor; a memory for storing processor-executable instructions; wherein the processor is configured to: the iris image recognition method of any one of the foregoing is performed.
According to yet another aspect of embodiments of the present disclosure, there is provided a non-transitory computer-readable storage medium, which when executed by a processor of a mobile terminal, enables the mobile terminal to perform the iris image recognition method of one of the above.
The technical scheme provided by the embodiment of the disclosure can comprise the following beneficial effects: the iris image to be processed is decomposed based on the improved BEMD, partial decomposition component is extracted to reconstruct the iris image, the characteristics of the iris image after reconstruction are extracted and fused, and recognition is carried out by means of a lightgbm algorithm, so that noise in the iris image and interference of background factors are effectively eliminated, and the accuracy and efficiency of iris recognition are improved.
It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the disclosure.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the invention and together with the description, serve to explain the principles of the invention.
Fig. 1 is a flowchart illustrating an iris image recognition method according to an exemplary embodiment of the present disclosure.
Fig. 2 is a flowchart illustrating an iris image recognition method according to still another exemplary embodiment of the present disclosure.
Fig. 3 is a block diagram illustrating an iris image recognition apparatus according to an exemplary embodiment of the present disclosure.
Fig. 4 is a block diagram illustrating an iris image recognition apparatus according to an exemplary embodiment of the present disclosure.
Fig. 5 is a block diagram of an apparatus according to an example embodiment.
Detailed Description
Reference will now be made in detail to exemplary embodiments, examples of which are illustrated in the accompanying drawings. When the following description refers to the accompanying drawings, the same numbers in different drawings refer to the same or similar elements, unless otherwise indicated. The implementations described in the following exemplary examples do not represent all implementations consistent with the invention. Rather, they are merely examples of apparatus and methods consistent with aspects of the invention as detailed in the accompanying claims.
Fig. 1 is a flowchart illustrating an iris image recognition method according to an exemplary embodiment of the present disclosure, and as shown in fig. 1, the iris image recognition method includes the following steps.
In step S101, an iris image to be processed is acquired.
The iris image to be processed can be acquired by the image acquisition equipment, or can be acquired by electronic equipment with shooting function such as a mobile phone, a PC, a notebook and the like. The number of iris images to be processed may be 1 or more.
In step S102, the iris image to be processed is decomposed based on the improved two-dimensional empirical mode decomposition, resulting in a plurality of two-dimensional empirical mode decomposition components.
In the field of image processing, an image is used as a visual carrier, and noise pollution is generated due to the influence of external or internal factors in the acquisition and transmission processes, so that the quality of the image is reduced by the noise, important information in the image is lost, and great difficulty is brought to the understanding of the image.
In the process of shooting an iris image, due to the influence of human beings and environment, the obtained image often has noise and uneven illumination, wherein the noise belongs to a high-frequency component, and the illumination background belongs to a low-frequency component. The improved two-dimensional empirical mode decomposition (bidimensional empirical mode decomposition, BEMD) decomposes the iris image to be processed to obtain a plurality of two-dimensional empirical mode decomposition components so as to separate the noise modal components and the illumination background modal components from the iris image to be processed, remove interference factors and further improve the recognition precision and efficiency of the iris image to be processed.
The iris image to be processed is segmented, so that local features of the iris image to be processed can be conveniently extracted, the local features of the iris image to be processed and the whole features of the iris image to be processed are fused, and accuracy and robustness of iris recognition are improved.
For example, dividing I into 4 rows and 8 columns, i.e. 32 sub-blocks, i.e. the iris image I to be processed is expressed as:
decomposing the iris image I to be processed by using an improved BEMD algorithm to obtain an intrinsic mode function IMF i I=1, 2,.. n . Wherein the first modal decomposition component IMF 1 Corresponding to the noise modal component.
In step S103, a preset number of two-dimensional empirical mode decomposition components are selected from the plurality of two-dimensional empirical mode decomposition components for reconstruction, so as to obtain a reconstructed iris image to be processed.
In order to solve the influence of noise, illumination background and other interference factors existing in the shooting process of the iris image to be processed, the improved BEMD is adopted to decompose the iris image I to be processed, so as to obtain an intrinsic mode function IMF i I=1, 2,.. n . Taking n=5, i.e. 4 cycles, to extract the second, third and fourth corresponding fractions in view of the final effect and calculation amountReconstructing iris images from solution components, i.e.
Noise and background illumination effects are filtered out.
In step S104, the distribution characteristics of the iris image to be processed after reconstruction are extracted and fused.
And extracting and fusing the step-by-step characteristics of the reconstructed iris image to effectively classify and identify the image.
In step S105, the distribution characteristics are identified based on the light gradient lifting frame, and an identification result is obtained.
The lightweight gradient lifting framework (Light Gradient Boosting Machine, lightGBM) is a fast, distributed, high-performance gradient lifting framework based on decision tree algorithm, which is an improved version of Gradient Boosting algorithm, the main idea being to use weak classifiers (decision trees) for iterative training to obtain an optimal model. The LightGBM model has the advantages of good training effect, difficulty in fitting and the like, and is used for tasks such as feature selection, searching and classification. And the distribution characteristics are identified based on the lightgbm, so that the identification effect of the iris image to be processed is improved.
According to the iris image recognition method, the iris image to be processed is decomposed based on the improved BEMD, the iris image is reconstructed by extracting part of decomposition components, the iris image characteristics after reconstruction are extracted and fused, recognition is carried out by means of a lightgbm algorithm, noise in the iris image and interference of background factors are effectively eliminated, and therefore accuracy and efficiency of iris recognition are improved.
In one embodiment, in the improved two-dimensional empirical mode decomposition, an envelope surface mean of the improved two-dimensional empirical mode decomposition is calculated using a piecewise cubic hermite polynomial interpolation function.
The iris image to be processed is R 1，1 (x，y)＝I(x，y)，x∈[0，M]，y∈[0，N]Wherein M and N are the number of rows and columns of the iris image to be processed.
In the improved BEMD, a cubic spline interpolation method cannot be used for well fitting a curved surface, and a piecewise cubic Hermite polynomial interpolation function is used for reducing errors of curve fitting.
Note i=1, r i，j (x, y) =i (x, y), find R i，j (x, y) all maxima and maxima points, in the improved BEMD, the maxima and minima, i.e. the upper and lower envelopes Max of maxima and minima, are fitted respectively by a piecewise cubic Hermite polynomial interpolation function i，j (x, y) and Min i，j (x, y) calculating the mean value of the envelope
Calculating an iris image R to be processed i，j (x, y) and envelope mean M i，j Difference R of (x, y) i，j+1 (x，y)，R i，j+1 The calculation method of (x, y) is as follows:
R i，j+1 (x，y)＝R i，j (x，y)-M i，j (x，y)
determination of standard deviation SD j Whether or not it is smaller than the set value, if the standard deviation SD j Less than the set value, record R i，j (x, y) is IMF i (x，y)。
If standard deviation SD j Not smaller than the set value, taking the iris image to be processed as R i，j+1 (x, y), repeating the steps of fitting the maximum value and the minimum value respectively by using the piecewise three-time Hermite polynomial interpolation function, and judging the importance degree of the influence of the feature on the final classification result by means of chi-square test until the following conditions are satisfied:
the input signal is denoted as R i+1，j (x，y)＝R i，j (x，y)-IMF i (x, y), j=1, and repeating the fitting of the maxima and minima, respectively, by the piecewise cubic hermite polynomial interpolation function until the residual component R remains n (x, y) is a monotonic function.
In one embodiment, the iris image to be processed is subjected to boundary symmetry continuation.
In the process of decomposing the iris image to be processed by the improved BEMD algorithm, boundary effects exist, and the influence of the boundary effects is reduced by adopting a boundary symmetrical continuation method.
For example, a boundary symmetry extension method may be adopted to extend the upper and lower directions of the iris image to be processed by 16 pixels respectively and extend the left and right directions by 64 pixels respectively. It can be appreciated that the continuation of the iris image to be processed in the up-down and left-right directions is symmetrical, further reducing the influence of the boundary effect on the iris image recognition.
In one embodiment, the distribution characteristics of the iris image to be processed after reconstruction are extracted by using a log Gabor odd function.
The log Gabor function can be split into real part even symmetry and imaginary part odd symmetry, and compared with the log Gabor function, the log Gabor odd function has higher recognition accuracy and reduces the calculated amount.
The log Gabor function can be expressed as:
wherein f 0 For the center frequency, θ 0 For angular direction, sigma, of the filter f For determining radial bandwidth, bandwidth formula B f ＝2(2/ln2) 1/2 |ln(σ f /f 0 )|，σ θ Bandwidth formula B for determining directional bandwidth θ ＝2σ θ (2ln2) 1/2 。
For example, it may be a log Gabor odd function Im (G ij ) Wherein i, j=1, 2,3,4, and respectively extracting the distribution characteristics of the iris image to be processed.
For two iris images I to be processed 1 、I 2 Using 4 directions, 4 size log Gabor odd functions, and phase encoding, 4 x 4 feature templates are formed for each iris image to be processed.
In one embodiment, the hamming distance is used to determine the similarity of the iris image to be processed and the hamming distance is identified based on a lightweight gradient lifting framework.
For two iris images I to be processed 1 、I 2 Recognition is carried out to determine an iris image I to be processed 1 、I 2 Whether the iris images belong to the same human eye or not can utilize log Gabor odd functions of 4 directions and 4 sizes and phase coding to enable each iris image to be processed to form 4 multiplied by 4 characteristic templates.
Calculating two iris images I to be processed under the same function 1 、I 2 Hamming distance between corresponding feature templates of (I) two iris images I to be processed 1 、I 2 Is the overall distance H of w ，H w ＝[H w1 ，H w2 ，...，H w16 ]。
For two iris images I to be processed 1 、I 2 Corresponding sub-blocks respectively act on I by using a log Gabor odd function with 4 directions and 4 sizes and a phase coding method 1 ，I 2 Each image forming 4 x 8 feature sub-templates, calculating hamming distance H between corresponding feature sub-templates of two images under the action of the same function s ，H s ＝[H s1 ，H s2 ，…，H s512 ]。
Fusing two iris images I to be processed 1 、I 2 Overall hamming distance H of (a) w And two iris images I to be processed 1 、I 2 Local hamming distance H between corresponding sub-blocks s I.e. the metric vector is used to measure the distance h= [ H ] between the iris images to be processed w ，H s ]. Will overall hamming distance H w And a local hamming distance H s And the vector is aggregated into a one-dimensional vector, so that the calculation is simple and convenient, and the recognition speed is improved.
And identifying the distribution characteristics based on the LightGBM to obtain an identification result.
Training data by means of a lightgbm classification algorithm and combining a univariate feature selection method to reduce dimensions, and comprehensively selecting the most important 100 feature degreesThe amount, i.e. H f ＝[H f1 ，H f2 ，...，H f100 ]The accuracy can be effectively improved when the identification is performed.
The data are trained by means of a lightgbm classification algorithm and the dimension is reduced by combining a univariate feature selection method, and 100 most important features are determined.
Iris image I to be processed 1 、I 2 And (5) performing identification to obtain an identification result. I.e. to the vector H f Taken as the variable x, the predicted result is y. When two iris images I are to be processed 1 、I 2 In the case of images belonging to the same human eye, y=1. When two iris images I are to be processed 1 、I 2 And not an image belonging to the same human eye, y=0.
Fig. 2 is a flowchart illustrating an iris image recognition method according to still another exemplary embodiment of the present disclosure, and as shown in fig. 2, the iris image recognition method includes the following steps.
In step S201, an iris image to be processed is acquired.
In step S202, normalization processing is performed on the iris image to be processed, and the normalized iris image to be processed is segmented.
The iris image to be processed is normalized, wherein normalization means that a series of standard processing transformation is performed on the iris image to be processed, so that the iris image to be processed is transformed into a fixed standard form.
The normalization processing can be to normalize the ring-shaped iris image to be processed into a rectangular image, so that unnecessary pixels outside the iris can be removed, the size of the iris image to be processed is compressed, and subsequent feature extraction and recognition operations are facilitated. The iris image to be processed after normalization processing is a normalized image of the iris image to be processed. And carrying out normalization processing on the iris image to be processed to obtain the iris image to be processed after normalization processing, so that the iris recognition precision can be improved, and the recognition result is more accurate.
The normalized iris image to be processed is segmented, so that local features of the iris image to be processed can be conveniently extracted, the local features of the iris image to be processed and the overall features of the iris image to be processed are fused, and accuracy and robustness of iris recognition are improved.
In step S203, the iris image to be processed is decomposed based on the improved two-dimensional empirical mode decomposition, resulting in a plurality of two-dimensional empirical mode decomposition components.
In step S204, a preset number of two-dimensional empirical mode decomposition components are selected from the plurality of two-dimensional empirical mode decomposition components for reconstruction, so as to obtain a reconstructed iris image to be processed.
In step S205, the distribution characteristics of the iris image to be processed after reconstruction are extracted and fused.
In step S206, the distribution characteristics are identified based on the light gradient lifting frame, and an identification result is obtained.
In an embodiment, the reconstructed iris image to be processed is enhanced based on adaptive histogram equalization.
And (3) enhancing the contrast of the iris image to be processed by using self-adaptive histogram equalization, and enhancing the reconstructed iris image to be processed to obtain the iris image template to be processed.
Fig. 3 is a block diagram of an iris image recognition apparatus according to an exemplary embodiment of the present disclosure, and as shown in fig. 3, an iris image recognition apparatus 300 includes: the system comprises an acquisition module 310, a decomposition module 320, a reconstruction module 330, an extraction module 340 and an identification module 350.
An acquisition module 310 is configured to acquire an iris image to be processed.
The decomposition module 320 is configured to decompose the iris image to be processed based on the improved two-dimensional empirical mode decomposition, so as to obtain a plurality of two-dimensional empirical mode decomposition components.
The reconstruction module 330 is configured to select a preset number of two-dimensional empirical mode decomposition components from the plurality of two-dimensional empirical mode decomposition components for reconstruction, so as to obtain a reconstructed iris image to be processed.
The extracting module 340 is configured to extract and fuse the distribution characteristics of the reconstructed iris image to be processed.
The identification module 350 is configured to identify the distribution feature based on the light gradient lifting frame, so as to obtain an identification result.
In one embodiment, when decomposing the iris image to be processed based on the improved two-dimensional empirical mode decomposition, the decomposition module 320 is configured to: in the improved two-dimensional empirical mode decomposition, an envelope surface mean value of the improved two-dimensional empirical mode decomposition is calculated by using a piecewise cubic Hermite polynomial interpolation function.
In one embodiment, when decomposing the iris image to be processed based on the improved two-dimensional empirical mode decomposition, the decomposition module 320 is configured to: and carrying out boundary symmetrical continuation on the iris image to be processed.
In an embodiment, when extracting and fusing the distribution characteristics of the reconstructed iris image to be processed, the extracting module 340 is configured to: and extracting the distribution characteristics of the reconstructed iris image to be processed by using a log Gabor odd function.
In one embodiment, when identifying the distribution feature based on the lightweight gradient lifting framework, the identification module 350 is configured to: determining the similarity of the iris images to be processed by utilizing the Hamming distance; and the hamming distance is identified based on the light gradient lifting frame.
Fig. 4 is a block diagram of an iris image recognition apparatus according to an exemplary embodiment of the present disclosure, and as shown in fig. 4, the iris image recognition apparatus 300 further includes: the preprocessing module 360.
The preprocessing module 360 is configured to preprocess an iris image to be processed, where the preprocessing includes: normalizing the iris image to be processed; and blocking the normalized iris image to be processed.
In an embodiment, when obtaining the reconstructed iris image to be processed, the reconstruction module 330 is configured to: and enhancing the reconstructed iris image to be processed based on self-adaptive histogram equalization.
The specific manner in which the various modules perform the operations in the apparatus of the above embodiments have been described in detail in connection with the embodiments of the method, and will not be described in detail herein.
Fig. 5 is an electronic device 30 shown in accordance with an exemplary embodiment. The electronic device 30 includes, among other things, a memory 310, a processor 320, and an Input/Output (I/O) interface 330. Wherein the memory 310 is used for storing instructions. And a processor 320 for calling the instructions stored in the memory 310 to execute the iris image recognition method according to the embodiment of the invention. Wherein the processor 320 is coupled to the memory 310, the I/O interface 330, respectively, such as via a bus system and/or other form of connection mechanism (not shown). The memory 310 may be used to store programs and data, including programs of the iris image recognition method according to the embodiment of the present invention, and the processor 320 performs various functional applications of the electronic device 30 and data processing by running the programs stored in the memory 310.
The processor 320 in embodiments of the present invention may be implemented in at least one hardware form of a digital signal processor (Digital Signal Processing, DSP), field-programmable gate array (Field-Programmable Gate Array, FPGA), programmable logic array (Programmable Logic Array, PLA), the processor 320 may be one or a combination of several of a central processing unit (Central Processing Unit, CPU) or other form of processing unit having data processing and/or instruction execution capabilities.
In the embodiment of the present invention, the I/O interface 330 may be used to receive input instructions (e.g., numeric or character information, and generate key signal inputs related to user settings and function control of the electronic device 30, etc.), and may also output various information (e.g., images or sounds, etc.) to the outside. The I/O interface 330 in embodiments of the present invention may include one or more of a physical keyboard, function keys (e.g., volume control keys, switch keys, etc.), a mouse, a joystick, a trackball, a microphone, a speaker, a touch panel, etc.
In some embodiments, the present invention provides a computer-readable storage medium storing computer-executable instructions that, when executed by a processor, perform any of the methods described above.
Although operations are depicted in the drawings in a particular order, this should not be understood as requiring that such operations be performed in the particular order shown or in sequential order, or that all illustrated operations be performed, to achieve desirable results. In certain circumstances, multitasking and parallel processing may be advantageous.
The methods and apparatus of the present invention can be implemented using standard programming techniques with various method steps being performed using rule-based logic or other logic. It should also be noted that the words "apparatus" and "module" as used herein and in the claims are intended to include implementations using one or more lines of software code and/or hardware implementations and/or equipment for receiving inputs.
Any of the steps, operations, or procedures described herein may be performed or implemented using one or more hardware or software modules alone or in combination with other devices. In one embodiment, the software modules are implemented using a computer program product comprising a computer readable medium containing computer program code capable of being executed by a computer processor for performing any or all of the described steps, operations, or programs.
The foregoing description of the implementations of the invention has been presented for purposes of illustration and description. It is not intended to be exhaustive or to limit the invention to the precise form disclosed, and modifications and variations are possible in light of the above teachings or may be acquired from practice of the invention. The embodiments were chosen and described in order to explain the principles of the invention and its practical application to enable one skilled in the art to utilize the invention in various embodiments and with various modifications as are suited to the particular use contemplated.
Claims (16)
1. An iris image recognition method, the method comprising:
acquiring an iris image to be processed;
decomposing the iris image to be processed based on improved two-dimensional empirical mode decomposition to obtain a plurality of two-dimensional empirical mode decomposition components;
selecting a preset number of two-dimensional empirical mode decomposition components from the plurality of two-dimensional empirical mode decomposition components for reconstruction, and obtaining the reconstructed iris image to be processed;
extracting and fusing the distribution characteristics of the iris image to be processed after reconstruction;
and identifying the distribution characteristics based on a light gradient lifting frame to obtain an identification result.
2. The iris image recognition method according to claim 1, wherein decomposing the iris image to be processed based on the improved two-dimensional empirical mode decomposition comprises:
in the improved two-dimensional empirical mode decomposition, an envelope surface mean value of the improved two-dimensional empirical mode decomposition is calculated by using a piecewise cubic hermite polynomial interpolation function.
3. The iris image recognition method according to claim 1 or 2, wherein decomposing the iris image to be processed based on improved two-dimensional empirical mode decomposition comprises:
and carrying out boundary symmetry continuation on the iris image to be processed.
4. The iris image recognition method of claim 3, wherein the extracting and fusing the reconstructed distribution characteristics of the iris image to be processed comprises:
and extracting the distribution characteristics of the iris image to be processed after reconstruction by using a log Gabor odd function.
5. The iris image recognition method of claim 3, wherein the recognition of the distribution feature based on a lightweight gradient lifting framework comprises:
determining the similarity of the iris image to be processed by utilizing the Hamming distance;
and identifying the hamming distance based on a lightweight gradient lifting framework.
6. The iris image recognition method according to claim 4 or 5, wherein the method further comprises:
preprocessing the iris image to be processed, wherein the preprocessing comprises;
normalizing the iris image to be processed; and
and blocking the normalized iris image to be processed.
7. The iris image recognition method according to claim 6, wherein obtaining the reconstructed iris image to be processed comprises:
and enhancing the iris image to be processed after reconstruction based on self-adaptive histogram equalization.
8. An iris image recognition apparatus, the apparatus comprising:
the acquisition module is used for acquiring an iris image to be processed;
the decomposition module is used for decomposing the iris image to be processed based on improved two-dimensional empirical mode decomposition to obtain a plurality of two-dimensional empirical mode decomposition components;
the reconstruction module is used for selecting a preset number of the two-dimensional empirical mode decomposition components from the plurality of two-dimensional empirical mode decomposition components to reconstruct so as to obtain the reconstructed iris image to be processed;
the extraction module is used for extracting and fusing the distribution characteristics of the iris image to be processed after reconstruction;
and the identification module is used for identifying the distribution characteristics based on the light gradient lifting frame to obtain an identification result.
9. The iris image recognition apparatus as claimed in claim 8, wherein the decomposition module is for, when decomposing the iris image to be processed based on improved two-dimensional empirical mode decomposition:
in the improved two-dimensional empirical mode decomposition, an envelope surface mean value of the improved two-dimensional empirical mode decomposition is calculated by using a piecewise cubic hermite polynomial interpolation function.
10. The iris image recognition apparatus according to claim 8 or 9, wherein when decomposing the iris image to be processed based on the improved two-dimensional empirical mode decomposition, the decomposition module is configured to:
and carrying out boundary symmetry continuation on the iris image to be processed.
11. The iris image recognition device according to claim 10, wherein when extracting and fusing the reconstructed distribution characteristics of the iris image to be processed, the extracting module is configured to:
and extracting the distribution characteristics of the iris image to be processed after reconstruction by using a log Gabor odd function.
12. The iris image recognition device according to claim 10, wherein the recognition module is configured to, when recognizing the distribution feature based on a lightweight gradient lifting framework:
determining the similarity of the iris image to be processed by utilizing the Hamming distance;
and identifying the hamming distance based on a lightweight gradient lifting framework.
13. The iris image recognition apparatus as claimed in claim 11 or 12, wherein the apparatus further comprises:
the preprocessing module is used for preprocessing the iris image to be processed, and the preprocessing comprises the following steps:
normalizing the iris image to be processed; and
and blocking the normalized iris image to be processed.
14. The iris image recognition device according to claim 13, wherein when the iris image to be processed after the reconstruction is obtained, the reconstruction module is configured to:
and enhancing the iris image to be processed after reconstruction based on self-adaptive histogram equalization.
15. An iris image recognition apparatus, comprising:
a processor;
a memory for storing processor-executable instructions;
wherein the processor is configured to: an iris image recognition method as claimed in any one of claims 1 to 7 is performed.
16. A non-transitory computer readable storage medium, which when executed by a processor of a mobile terminal, causes the mobile terminal to perform the iris image recognition method of any one of claims 1 to 7.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010244416.3A CN111539258B (en) | 2020-03-31 | 2020-03-31 | Iris image recognition method, iris image recognition device, and storage medium |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010244416.3A CN111539258B (en) | 2020-03-31 | 2020-03-31 | Iris image recognition method, iris image recognition device, and storage medium |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN111539258A CN111539258A (en) | 2020-08-14 |
| CN111539258B true CN111539258B (en) | 2023-05-12 |
Family
ID=71973307
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202010244416.3A Active CN111539258B (en) | 2020-03-31 | 2020-03-31 | Iris image recognition method, iris image recognition device, and storage medium |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN111539258B (en) |
Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2015119520A1 (en) * | 2014-02-05 | 2015-08-13 | Walus Michał | The method and decision system of personal characteristics acquisition especially in biometrical authorisation systems |
| CN107578028A (en) * | 2017-09-20 | 2018-01-12 | 广东工业大学 | A kind of face identification method, device, equipment and computer-readable recording medium |
| CN109902780A (en) * | 2019-02-14 | 2019-06-18 | 广州番禺职业技术学院 | Testimony of a witness unification verification terminal and system and method based on multi-modal recognition of face |
Family Cites Families (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US11645835B2 (en) * | 2017-08-30 | 2023-05-09 | Board Of Regents, The University Of Texas System | Hypercomplex deep learning methods, architectures, and apparatus for multimodal small, medium, and large-scale data representation, analysis, and applications |
-
2020
- 2020-03-31 CN CN202010244416.3A patent/CN111539258B/en active Active
Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2015119520A1 (en) * | 2014-02-05 | 2015-08-13 | Walus Michał | The method and decision system of personal characteristics acquisition especially in biometrical authorisation systems |
| CN107578028A (en) * | 2017-09-20 | 2018-01-12 | 广东工业大学 | A kind of face identification method, device, equipment and computer-readable recording medium |
| CN109902780A (en) * | 2019-02-14 | 2019-06-18 | 广州番禺职业技术学院 | Testimony of a witness unification verification terminal and system and method based on multi-modal recognition of face |
Also Published As
| Publication number | Publication date |
|---|---|
| CN111539258A (en) | 2020-08-14 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Peralta et al. | On the use of convolutional neural networks for robust classification of multiple fingerprint captures | |
| Chen et al. | Median filtering forensics based on convolutional neural networks | |
| Qi et al. | Robust feature learning by stacked autoencoder with maximum correntropy criterion | |
| Naikal et al. | Informative feature selection for object recognition via sparse PCA | |
| CN110363091B (en) | Face recognition method, device and equipment under side face condition and storage medium | |
| CN111079639A (en) | Method, device and equipment for constructing garbage image classification model and storage medium | |
| CN106650568B (en) | Face recognition method and device | |
| CN111626371A (en) | Image classification method, device and equipment and readable storage medium | |
| CN110245621B (en) | Face recognition device, image processing method, feature extraction model, and storage medium | |
| CN111553215A (en) | Personnel association method and device, and graph convolution network training method and device | |
| CN111353514A (en) | Model training method, image recognition method, device and terminal equipment | |
| CN111681182A (en) | Picture restoration method and device, terminal equipment and storage medium | |
| CN110659631A (en) | License plate recognition method and terminal equipment | |
| CN111539258B (en) | Iris image recognition method, iris image recognition device, and storage medium | |
| Neethu et al. | Enhancement of Fingerprint using FFT×| FFT| n Filter | |
| Krupiński et al. | Binarization of degraded document images with generalized Gaussian distribution | |
| CN111488811A (en) | Face recognition method and device, terminal equipment and computer readable medium | |
| CN113159211B (en) | Method, computing device and computer storage medium for similar image retrieval | |
| TWI775038B (en) | Method and device for recognizing character and storage medium | |
| CN108629264B (en) | Method and apparatus for image processing | |
| Wang et al. | A wavelet energy decomposition signature for robust non-rigid shape matching | |
| Rachapalli et al. | Texture driven hierarchical fusion for multi-biometric system | |
| CN113312946A (en) | Fingerprint image feature extraction method and device and computer readable storage medium | |
| Hsia et al. | Redundancy features detection and removal for simplification of convolutional neural networks | |
| Giełczyk et al. | Intelligent human-centred mobile authentication system based on palmprints |
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 | ||
| CB02 | Change of applicant information |
Address after: 100081 room 701, floor 7, Fuhai international port, Haidian District, Beijing Applicant after: Beijing wanlihong Technology Co.,Ltd. Address before: 100081 1504, floor 15, Fuhai international port, Daliushu Road, Haidian District, Beijing Applicant before: BEIJING SUPERRED TECHNOLOGY Co.,Ltd. |
|
| CB02 | Change of applicant information | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |