US20020150281A1 - Method of recognizing human iris using daubechies wavelet transform - Google Patents

Method of recognizing human iris using daubechies wavelet transform Download PDF

Info

Publication number
US20020150281A1
US20020150281A1 US09946714 US94671401A US2002150281A1 US 20020150281 A1 US20020150281 A1 US 20020150281A1 US 09946714 US09946714 US 09946714 US 94671401 A US94671401 A US 94671401A US 2002150281 A1 US2002150281 A1 US 2002150281A1
Authority
US
Grant status
Application
Patent type
Prior art keywords
iris
characteristic
image
method
characteristic vector
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.)
Abandoned
Application number
US09946714
Inventor
Seong-Won Cho
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
EVERMEDIA Co Ltd
Original Assignee
EVERMEDIA Co Ltd
Priority date (The priority date 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 date listed.)
Filing date
Publication date

Links

Images

Classifications

    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06KRECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
    • G06K9/00Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
    • G06K9/00597Acquiring or recognising eyes, e.g. iris verification

Abstract

The present invention relates to a method of recognizing the human iris using the Daubechies wavelet transform. The dimensions of characteristic vectors are initially reduced by extracting iris features from the inputted iris image signals through the Daubechies wavelet transform. Then, the binary characteristic vectors are generated by applying quantization functions to the extracted characteristic values so that the utility of human iris recognition can be improved as the storage capacity and processing time thereof can be reduced by generating low capacity characteristic vectors. By measuring the similarity between the generated characteristic vectors and the previously registered characteristic vectors, characteristic vectors indicative of the iris patterns can be realized.

Description

    CLAIM OF PRIORITY
  • This application makes reference to, incorporates the same herein, and claims all benefits accruing under 35 U.S.C. Section 119 from an application for “Method of Recognizing Human Iris Using Daubechies Wavelet Transform,” filed earlier in the Korean Industrial Property Office on Mar. 6, 2001, and there duly assigned Serial No. 2001-11440. [0001]
  • BACKGROUND OF THE INVENTION
  • 1. Field of Invention [0002]
  • The present invention relates to a method of recognizing the human iris and, more particularly, to a method of recognizing the human iris using the Daubechies wavelet transform to reduce the dimensions of characteristic vectors to improve the processing time. [0003]
  • 2. Description of the Related Art [0004]
  • An iris recognition system is used for performing the identification of an individual based on the information obtained from the analysis of the iris patterns, which are different for each individual. The iris recognition system has superior identification accuracy and thus provides excellent security when compared to other biometric methods that use voice and fingerprints for identification. [0005]
  • A wavelet transform is typically used to extract the characteristics of the iris images and involves analyzing signals in a multi-resolution mode. The wavelet transform is a mathematical theory used for formulating a model for systems, signals, and a series of processes using selected signals based on the Fourier transform. These signals are referred to as little waves or wavelets. Recently, the wavelet transform is widely employed in the field of signal and image processing as it has a faster rate when compared with the traditional signal processing algorithm, and it can efficiently achieve signal localization in time and frequency domains. The images are obtained by extracting the iris patterns from an iris image that are acquired by an image acquisition device, then patterns normalized in the 450×60 size are used to extract the characteristic values using the wavelet transform. [0006]
  • There are other types of wavelet transmform known in the art. For example, the Harr wavelet transform has been widely used also in the conventional iris recognition systems, image processing, and the like. However, the Harr wavelet transform has disadvantages in that the characteristic values change irregularly and rapidly. In addition, a high resolution of the images cannot be obtained if the images are decompressed again after they have been compressed. In contrast, the Daubechies wavelet transform is a continuous function, thus the disadvantages associated with the Harr wavelet functions can be avoided in certain instances for extracting more accurate and delicate characteristic values. If the images are decompressed again after they have been compressed using the Daubechies wavelet transform, the images can be restored with a high resolution quality back to the original images if the Harr wavelet transform is used. However, as the Daubechies wavelet functions are generally more complicated than the Harr wavelet functions, there is a disadvantage in that a larger arithmetic quantity may be needed. A main advantage of the Daubechies wavelet transform is that it provides fine characteristic values when performing the wavelet transform to extract the characteristic values. That is, if the Daubechies wavelet transform is used, the identification of the iris features can be made with a lower number of data, and the extraction of the iris features can be made accurately. [0007]
  • Another method of extracting the characteristic values indicative of the iris patterns and forming the characteristic vectors uses the Gabor transform. However, the characteristic vectors generated by this method require 256 or more dimensions and at least 256 bytes, where one byte is assigned to one dimension. Thus, there is a problem in that practicability and efficiency are undermined when the Gabor transform is used in the field if low capacity information is required. [0008]
  • The Hamming distance (HD) is used to verify the two characteristic vectors generated in the form of binary vectors. The method of measuring a distance, such as the Hamming distance (HD) between two characteristic vectors (i.e., characteristic vectors relevant to the input pattern and the stored reference characteristic vectors) for the pattern classification is disclosed in U.S. Pat. No. 5,291,560, the teachings of which are incorporated herein by reference. The bit values assigned according to the respective dimension are compared with each other. If they are identical to each other, 0 is given; and if they are different from each other, 1 is given. Then, the value divided by the total number of dimensions is obtained as a final result. Hence, this method is simple and useful in discriminating the degree of similarity between the characteristic vectors consisting of binary codes. The comparison result of all the bits becomes 0 if identical data are compared with each other. Thus, the result approaching 0 implies that the data belong to the persons themselves. If the data do indeed belong to the person, the probability of the degree of similarity will be 0.5. Accordingly, a proper limit set between 0 and 0.5 will be a boundary for differentiating between people. The Hamming distance (HD) is also excellent for application with the extracted iris features by subdividing the data, but it is not suitable when low capacity data is to be used. If the total number of the bits of the characteristic vectors with 256-byte information is 2048, considerably high acceptance rates are realized even though the Hamming distance is applied. In addition, there are disadvantages in that the formation of the reference characteristic vectors through generalizing the pattern information cannot be easily made, and one can not rely upon the information characteristics of each dimension of the characteristic vectors. [0009]
  • Accordingly, if the low capacity characteristic vectors are used, the accuracy of differentiating characteristic vectors is poor due to an increase in lost information. Thus, a method of preventing information loss while maintaining the minimum capacity of the characteristic vectors is needed in generating the characteristic vectors. Accordingly, there is a need for a method of forming the low capacity characteristic vectors, so that the processing, storage, transfer, search, and the like of the pattern information can be achieved efficiently. [0010]
  • SUMMARY OF INVENTION
  • The present invention is directed to a method of forming low capacity characteristic vectors, so that the false acceptance rate (FAR) and the false rejection rate (FRR) can be remarkably reduced as compared to the conventional Harr wavelet transform. To this end, the iris features from inputted iris image signals are extracted using the Daubechies wavelet transform. [0011]
  • One aspect of the present invention provides a method for measuring the similarity between the characteristic vectors, wherein the low capacity characteristic vectors can be properly used for the similarity measurement while the loss of information can be minimized. [0012]
  • Another aspect of the present invention provides a method for recognizing the human iris using the Daubechies wavelet transform, wherein the iris image from an eye using an image acquisition device with a halogen lamp illuminator is provided. The method includes the steps of: (a) repeatedly performing the Daubechies wavelet transform of the iris image at predetermined times to multi-divide the iris image, and extracting an image including the high frequency components from the multi-divided image to extract iris features; (b) extracting the characteristic values of a characteristic vector from the extracted image with the high frequency components, and generating a binary characteristic vector by quantizing the relevant characteristic values; and, (c) determining the user as an enrollee based on the similarity between the generated characteristic vector and a previously registered characteristic vector. [0013]
  • According to another aspect of the present invention, the iris image is acquired through an image acquisition device utilizing a halogen lamp as an illuminator. By repeatedly performing the Daubechies wavelet transform of the inputted iris image, the iris image is multi-divided, and the iris features with optimized sizes are extracted. The characteristic vector, which is effective in displaying and processing the image, is then formed by quantizing the extracted characteristic values. Furthermore, the dimension of the characteristic vector is reduced by quantizing the extracted characteristic values into binary values—that is, when a low capacity characteristic vector is formed, the method of measuring the similarity between the weight registered and the inputted characteristic vectors is used to prevent the reduction of acceptance resulting from the formation of the low capacity characteristic vector. The user authenticity is, therefore, determined by the foregoing method.[0014]
  • BRIEF DESCRIPTION OF THE DRAWINGS
  • FIG. 1 is a view illustrating the constitution of the image acquisition equipment used for performing an iris recognition method according to the present invention. [0015]
  • FIG. 2 is a flowchart illustrating the process of verifying an iris image according to the present invention. [0016]
  • FIG. 3 is a flowchart illustrating the process of multi-dividing the iris image using the Daubechies wavelet transform according to the present invention. [0017]
  • FIG. 4 shows an example of multi-dividing the iris image using the Daubechies wavelet transform. [0018]
  • FIG. 5 is a flowchart illustrating the process of forming the characteristic vector of an iris image based on the data acquired from the multi-dividing operation according to the present invention. [0019]
  • FIG. 6[0020] a shows a distribution example of the characteristic values of the extracted iris image.
  • FIG. 6[0021] b shows the quantization function for generating a binary characteristic vector from the distribution example of FIG. 6a.
  • FIG. 7 is a flowchart showing the procedures for determining user authenticity through a similarity test between the characteristic vectors.[0022]
  • DETAILED DESCRIPTION FOR PREFERRED EMBODIMENT
  • Hereinafter, a method of recognizing a human iris using the Daubechies wavelet transform according to the present invention will be explained in detail with reference to the accompanying drawings. [0023]
  • FIG. 1 shows the exemplary embodiment of the image acquisition equipment for use in recognizing a human iris according to the present invention. The image acquisition equipment includes a halogen lamp [0024] 11 for illuminating the iris in order to acquire clear iris patterns, a CCD camera 13 for photographing the eye 10 of a user through a lens 12, a frame grabber 14 connected to the CCD camera 12 for acquiring the iris image, and a monitor 15 for showing the image to the user so that the acquisition of correct images and the position of the user can be obtained as the images are acquired.
  • In the embodiment, the CCD camera [0025] 13 is used to acquire the eye image, and the iris recognition is made through the pattern analysis of iridial folds. However, where the iris image is acquired indoors using an ordinary illuminator, it is difficult to extract the desired pattern information as the iris image is generally gloomy. Additional illuminators should therefore be used so that the information on the iris image cannot be lost and a clear iris pattern can be obtained. In the present invention, the halogen lamp 11 with strong floodlighting effects is preferably used as a main illuminator so that the iris pattern can be clearly shown. However, it should be noted that other light sources known to those skilled in this art can be successfully used. Furthermore, as shown in FIG. 1, the loss of the iris image information and eye fatigue of the user can be avoided by placing the halogen lamp illuminators on the left and right sides of the eye in order to cause the reflective light from the lamp to be formed on the outer portions of the iris region.
  • FIG. 2 is a flowchart showing the operation steps for verifying the iris image for identification purposes according to the present invention. Referring to FIG. 2, the eye image is acquired through the image acquisition equipment shown in FIG. 1 in step [0026] 200. In step 210, the images of the iris regions are extracted from the acquired eye image through pre-processing and transformed into a polar coordinate system, then the transformed iris pattern is inputted to a module for extracting the features. Acquiring the iris image and transforming the image into a polar coordinate system are well known in the art that can be performed in a variety of ways. In step 220, the Daubechies wavelet transform of the inputted iris pattern transformed into the polar coordinate system is performed, and the features of the iris regions are then extracted. The extracted features would have real numbers. In step 230, a binary characteristic vector is generated by applying a K-level quantization function to the extracted features. In step 240, the similarity between the generated characteristic vector and the previously registered data of the user is measured. Through the similarity measurement, user authenticity is determined and then the verification results are obtained.
  • In a case where the features of the iris regions are extracted by performing the Daubechies wavelet transform as described above, the Daubechies wavelet function with eight, sixteen, or more coefficients can extract more delicate characteristic values than the Daubechies wavelet function with four coefficients, even though the former method is more complicated than the latter. Although the Daubechies wavelet function with eight or more coefficients has been used and tested in the present invention, greater performance improvement was not obtained and the arithmetic quantity and processing time are increased, as compared with a case where the Daubechies wavelet function with four coefficients is tested. Hence, the Daubechies wavelet function with four coefficients may be used for extracting the characteristic values indicative of the iris patterns. [0027]
  • FIG. 3 is a flowchart showing the process of multi-dividing the iris image by performing the Daubechies wavelet transform according to the present invention. FIG. 4 shows an image divided using the Daubechies wavelet transform. As shown in FIG. 4, when “L” and “H” are respectively used to indicated low frequency and high frequency components, the term “LL” indicates the component that has passed through a low-pass filter (LPF) in all x and y directions, whereas the term “HH” indicates the component that has passed through a high-pass filter (HPF) in the x and y directions. The subscript numerals signify image-dividing stages. For example, “LH[0028] 2” means that the image has passed through the low-pass filter in the x direction and through the high-pass filter in the y direction during the 2-stage wavelet division.
  • Referring back to FIG. 3, in step [0029] 310, the inputted iris image is multi-divided using the Daubechies wavelet transform. As the iris image is considered a two-dimensional signal in which one-dimensional signals are arrayed in the x and y directions, quarterly divided components of one image should be extracted by passing through the LPF and HPF in all x and y directions in order to analyze the iris image. That is, one two-dimensional image signal is wavelet-transformed in vertical and horizontal directions, and the image is divided into four regions: LL, LH, HL, and HH after the wavelet transform has been performed once. At this time, using the Daubechies wavelet transform, the signal is divided into a differential component thereof that has passed through the high-pass filter and an average component that has passed through the low-pass filter.
  • The performance of the iris recognition system is evaluated in view of two factors; a false acceptance rate (FAR) and a false rejection rate (FRR). Here, the FAR means the probability that the entrance of unregistered persons (imposters) may be accepted due to the false recognition of unregistered persons as registered persons, and the FRR means the probability that entrance of registered persons (enrollees) is rejected due to false recognition of the registered persons as unregistered ones. In simulation, when the method of recognizing the human iris using the Daubechies wavelet transform according to the present invention was employed, the FAR has been reduced from 5.5% to 3.07% and the FRR has also been reduced from 5.0% to 2.25%, as compared with the method of recognizing the human iris using the conventional Harr wavelet transform. [0030]
  • In step [0031] 320, a region HH including only the high frequency components in the x and y directions are extracted from the divided iris image.
  • In step [0032] 330, after increasing the iterative number of times of dividing the iris image, the processing step is completed when the iterative number is greater than a predetermined number. Alternatively, if the iterative number is lower than the predetermined number, the information on the region HH is stored for use in extracting the iris features in step 340.
  • In step [0033] 350, the region LL comprising only low frequency components in the x and y directions is extracted from the multi-divided iris image. As the extracted region LL (corresponding to the image reduced in a fourth size as compared with the previous image) includes major information on the iris image, it is provided as an image to be newly processed so that the wavelet transform can be applied again to the relevant region. Thereafter, the Daubechies wavelet transform is repeated again from step 310.
  • In a case where the iris image is transformed from the Cartesian coordinate system to the polar coordinate system, in order to avoid changes in the iris features according to variations in the size of the pupil, the region between the inner and outer boundaries of the iris is divided into 60 segments in the r direction and 450 segments in the θ direction by varying the angles by 0.8 degrees. Finally, the information on the iris image is acquired and normalized as 450×60 (θ×r) data. Then, if the acquired iris image is once again wavelet-transformed, the characteristics of the 225×30 region HH[0034] 1 of which size is reduced by half are obtained, namely, the 225×30 information is used as a characteristic vector. This information may be used as it is, but the process of dividing the signals is repeatedly performed in order to reduce the information size. Since the region LL includes major information on the iris image, the characteristic values of further reduced regions, such as HH2, HH3, and HH4, are obtained by successively applying the wavelet transform to the respective relevant regions.
  • The iterative number, which is provided as a discriminating criterion for repeatedly performing the wavelet transform, should be set as an optimal value in consideration of the loss of the information and the size of the characteristic vector. Therefore, in the present invention, the region HH[0035] 4 obtained by performing the wavelet transform four times becomes a major characteristic region, and the values thereof are selected as the components of the characteristic vector. At this time, the region HH4 contains the information having 84 (=28×3) data.
  • FIG. 5 is a flowchart showing the process of forming the characteristic vector of the iris image using the data acquired from the multi-divided iris image according to the present invention. Referring to FIG. 5, the information on the n characteristic vector extracted from the above process, i.e., the information on the regions HH[0036] 1, HH2, HH3, and HH4 is inputted in step 510. In step 520, in order to acquire the characteristic information on the regions HH1, HH2, and HH3 excluding the information on the region HH4 obtained through the last wavelet transform among the n characteristic vector, each average value of the regions HH1, HH2, and HH3 is calculated and assigned one dimension. In step 530, all values of the final obtained region HH4 are extracted as the characteristic values thereof. After extraction of the characteristics of the iris image signals has been completed, the characteristic vector is generated based on these characteristics. A module for generating the characteristic vector mainly performs the processes of extracting the characteristic values in the form of real numbers and then transforming them to binary codes consisting of 0 and 1.
  • However, in step [0037] 540, the N−1 characteristic values extracted from step 520 and the M (the size of the final obtained region HH) characteristic values extracted from step 530 are combined and (M+N−1)-dimensional characteristic vector is generated. That is, the total 87 data, which the 84 data of the region HH4 and the 3 average data of the regions HH1, HH2, and HH3 are combined, are used as a characteristic vector in the present invention.
  • In step [0038] 550, the values of the previously obtained characteristic vector, i.e., the respective component values of the characteristic vector expressed in the form of the real numbers, are quantized into binary values 0 or 1. In step 560, the resultant (M+N−1)-bit characteristic vector is generated by the quantized values. That is, according to the present invention, the resultant 87-bit characteristic vector is generated.
  • FIG. 6[0039] a shows a distribution example of the characteristic values of the extracted iris image. When the values of the 87-dimensional characteristic vector are distributed according to the respective dimensions, the distribution roughly takes the shape of FIG. 6a. The binary vector including all the dimensions is generated by the following Equation 1.
  • f n=0iff(n)<0
  • f n=1 if f(n)>0  (1),
  • where f(n) is a characteristic value of the n-th dimension, and f[0040] n is the value of the n-th characteristic vector
  • When the 87-bit characteristic vector that is obtained by assigning one bit to the total 87 dimensions are generated in order to use a low capacity characteristic vector, the improvement of the recognition rate is limited to some extent as loss of the information on the iris image is increased. Therefore, when generating the characteristic vector, it is necessary to prevent information loss while maintaining the minimum capacity of the characteristic vector. [0041]
  • FIG. 6[0042] b shows a quantization function for generating a binary characteristic vector from the distribution example of the characteristic values shown in FIG. 6a. The extracted (M+N−1)-dimensional characteristic vector shown in FIG. 6a is evenly distributed mostly between 1 and −1 in view of its magnitude. Then, the binary vector is generated by applying the K-level quantization function shown in FIG. 6a to the characteristic vector. Since only signs of the characteristic values are obtained through the process of Equation 1, it is understood that information on the magnitude has been discarded. Thus, in order to accept the magnitude of the characteristic vector, a 4-level quantization process was utilized in the present invention.
  • As described above, in order to efficiently compare the characteristic vector generated through the 4-level quantization with the registered characteristic vector, the quantization levels have the weights expressed in the following Equation 2.[0043]
  • f n=4 if f(n)≧0.5 (level 4)
  • f n=1 if 0.5>f(n)≧0 (level 3)
  • f n=−1 if 0>f(n)>−0.5 (level 2)
  • f n=−4 if f(n)≦−0.5 (level 1)  (2),
  • where f[0044] n represents the n-th dimension of the previously registered characteristic vector fR of the user or the characteristic vector fT of the user generated from the iris image of the eye image of the user. An explanation of how to use the weights expressed in Equation 2, is as follows.
  • In a case where the n-th dimensional characteristic value f(n) is equal or more than 0.5 (level 4), the value of the i-th dimension f[0045] Ri or fTi is converted and assigned “4” if the value is “11.” In a case where the n-th dimensional characteristic value f(n) is more than 0 and less than 0.5 (level 3), the value of the i-th dimension fRi or fTi is converted and assigned “1” if the value is “10.” In a case where the n-th dimensional characteristic value f(n) is more than −0.5 and less than 0 (level 2), the value of the i-th dimension fRi or fTi is converted and assigned −1 if the value is “01.” In a case where the n-th dimensional characteristic value f(n) is equal to or less than −0.5 (level 1), the value of the i-th dimension fRi or fTi is converted and assigned −4 if the value is “00.” This is due to the weights being applied to the respective values as expressed in Equation 2 as it is suitable for the following verification method of the present invention.
  • FIG. 7 is a flowchart showing the procedures for discriminating user authenticity through the similarity measurement test between the characteristic vectors. Referring to FIG. 7, in step [0046] 710, the characteristic vector fT of the user is generated from the iris image of the eye image of the user. Step 720, searches the previously registered characteristic vector fR of the user. In step 730, in order to measure the similarity between the two characteristic vectors, the weights are assigned to the characteristic vectors fR and fT depending on the value of the binary characteristic vector based on Equation 2.
  • In step [0047] 740, an inner product or scalar product S of the two characteristic vectors is calculated and the similarity is finally measured. Among the measures generally used for determining the correlation between the registered characteristic vector fR and the characteristic vector fT of the user, it is the inner product S of the two characteristic vectors that indicate the most direct association. That is, after the weights have been assigned to the respective data of the characteristic vector in step 730, the inner product S of the two characteristic vectors is used to measure the similarity between the two vectors.
  • The following Equation 3 is used for calculating the inner product of the two characteristic vectors. [0048] S = i = 1 n f Ri f Ti = ( f R1 f T1 + f R2 f T2 + + f Rn f Tn ) , ( 3 )
    Figure US20020150281A1-20021017-M00001
  • where f[0049] R is the characteristic vector of the user that has been already registered, and fT is the characteristic vector of the user that is generated from the iris image of the eye of the user.
  • According to the above processes, one effect, which can be obtained by the quantization according to the sign of the characteristic vector values as in the method in which the binary vector, is generated with respect to the values of the characteristic vector extracted from the iris image according to the respective dimensions. That is, like the Hamming distance, the difference between 0 and 1 can be expressed. In a case where the two characteristic vectors have the same-signed values with respect to each dimension, positive values are added to the inner product S of the two characteristic vectors. Otherwise, negative values are added to the inner product S of the two vectors. Consequently, the inner product S of the two characteristic vectors increases if the two data belong to an identical person, while the inner product S of the two characteristic vectors decreases if the two data do not belong to an identical person. [0050]
  • In step [0051] 750, the user authenticity is determined according to the measured similarity obtained from the inner product S of the two characteristic vectors. At this time, the determination of the user authenticity based on the measured similarity depends on the following Equation 4.
  • If S>C, then TRUE or else FALSE  (4),
  • where C is a reference value for verifying the similarity between the two characteristic vectors. [0052]
  • That is, if the inner product S of the two characteristic vectors is equal to or more than the verification reference value C, the user is determined as an enrollee. Otherwise, the user is determined as an imposter. [0053]
  • As described above, the method of recognizing the human iris using the Daubechies wavelet transform according to the present invention has an advantage in that FAR and FRR can be remarkably reduced as compared with the method using the conventional Harr wavelet transform, as the iris features are extracted from the inputted iris image signals through the Daubechies wavelet transform. [0054]
  • Furthermore, in order to verify the similarity between the registered and extracted characteristic vectors f[0055] R and fT, the inner product S of the two characteristic vectors is calculated, and the user authenticity is determined based on the measured similarity obtained by the calculated inner product S of the two vectors. Therefore, there is provided a method of measuring the similarity between the characteristic vectors wherein the loss of the information, which may be produced by forming the low capacity characteristic vectors, can be minimized.
  • The foregoing is a mere embodiment for embodying the method of recognizing the human iris using the Daubechies wavelet transform according to the present invention. However, the present invention is not limited to the embodiment described above. A person skilled in the art can make various modifications and changes to the present invention without departing from the technical spirit and the scope of the present invention defined by the appended claims. [0056]

Claims (8)

    What is claimed is:
  1. 1. A method of recognizing a human iris using the Daubechies wavelet transform, the method comprising the steps of:
    (a) obtaining an iris image from a user's eye using an image acquisition device;
    (b) repeatedly performing said Daubechies wavelet transform on said iris image so as to multi-divide said iris image for a predetermined number of times;
    (c) extracting image with high frequency components from said multi-divided image so as to extract iris features;
    (d) extracting characteristic values of a characteristic vector from said extracted image with said high frequency components;
    (e) generating a binary characteristic vector by quantizing said extracted characteristic values; and,
    (f) determining whether said user as an enrollee by measuring a similarity between said generated characteristic vector and a previously registered characteristic vector.
  2. 2. The method of claim 1, further comprising the step of illuminating said user's eye.
  3. 3. The method of claim 2, wherein the step of illuminating said user's eye comprises the step of placing a halogen lamp at both ends of said user's eye.
  4. 4. The method of claim 1, wherein said step (b) comprises the steps of: extracting a region HH from said multi-divided image having said high frequency components in both x and y directions; storing information of said region HH for use in extracting iris features; performing multi-division of a region LL from said multi-divided image having low frequency components in both x and y directions.
  5. 5. The method of claim 2, wherein said predetermined number of times is set at four.
  6. 6. The method of claim 1, wherein said step (c) comprises the steps of: receiving multi-divided images of a plurality of high frequency regions HHi formed by said multi-division in said step (b); calculating the average values of regions HH1 to HHn−1 excluding the last region HHN; assigning said calculated average values to the components of said characteristic vector, respectively; assigning said calculated value M of said last region HHN to the components of said binary characteristic vector; combining said N−1 average values and said M values so as to generate a (M+N−1)-dimensional characteristic vector; and, quantizing all values of said generated characteristic vector into binary values so as to generate a final (M+N−1)-dimensional characteristic vector.
  7. 7. The method of claim 1, wherein said step (f) comprises the steps of: applying predetermined weights to the i-th dimensions of said generated characteristic vector generated from said step (c) and said previously registered characteristic vector; calculating the inner product S of said two weighted characteristic vectors; and determining said user as an enrollee if said inner product S is more than a verification reference value C.
  8. 8. The method of claim 1, wherein said image acquisition device comprises a halogen lamp.
US09946714 2001-03-06 2001-09-05 Method of recognizing human iris using daubechies wavelet transform Abandoned US20020150281A1 (en)

Priority Applications (2)

Application Number Priority Date Filing Date Title
KR20010011440A KR100374707B1 (en) 2001-03-06 2001-03-06 Method of recognizing human iris using daubechies wavelet transform
KR2001-11440 2001-03-06

Publications (1)

Publication Number Publication Date
US20020150281A1 true true US20020150281A1 (en) 2002-10-17

Family

ID=19706518

Family Applications (3)

Application Number Title Priority Date Filing Date
US09946714 Abandoned US20020150281A1 (en) 2001-03-06 2001-09-05 Method of recognizing human iris using daubechies wavelet transform
US10656885 Expired - Fee Related US7302087B2 (en) 2001-03-06 2003-09-05 Daubechies wavelet transform of iris image data for use with iris recognition system
US11941019 Abandoned US20100290676A1 (en) 2001-03-06 2007-11-15 Daubechies wavelet transform of iris image data for use with iris recognition system

Family Applications After (2)

Application Number Title Priority Date Filing Date
US10656885 Expired - Fee Related US7302087B2 (en) 2001-03-06 2003-09-05 Daubechies wavelet transform of iris image data for use with iris recognition system
US11941019 Abandoned US20100290676A1 (en) 2001-03-06 2007-11-15 Daubechies wavelet transform of iris image data for use with iris recognition system

Country Status (6)

Country Link
US (3) US20020150281A1 (en)
EP (1) EP1374145A4 (en)
JP (2) JP2004527832A (en)
KR (1) KR100374707B1 (en)
CN (1) CN1258733C (en)
WO (1) WO2002071317A1 (en)

Cited By (28)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20040071300A1 (en) * 2002-10-10 2004-04-15 Texas Instruments Incorporated Sharing wavelet domain components among encoded signals
WO2004084726A1 (en) * 2003-03-25 2004-10-07 Bausch & Lomb Incorporated Positive patient identification
ES2224838A1 (en) * 2003-02-21 2005-03-01 Universidad Politecnica De Madrid System for biometric identification of people, by analyzing iris, for use in access control in e.g. buildings, has determination unit determining signature of iris, and comparison unit comparing captured image with stored image
US20060222212A1 (en) * 2005-04-05 2006-10-05 Yingzi Du One-dimensional iris signature generation system and method
US20080159600A1 (en) * 2001-03-06 2008-07-03 Senga Advisors, Llc. Iris image data processing for use with iris recognition system
US20090324064A1 (en) * 2006-08-02 2009-12-31 Japan Science And Technology Agency Image feature extraction method and image compression method
US20100002913A1 (en) * 2005-01-26 2010-01-07 Honeywell International Inc. distance iris recognition
US7761453B2 (en) 2005-01-26 2010-07-20 Honeywell International Inc. Method and system for indexing and searching an iris image database
US20100260390A1 (en) * 2005-11-30 2010-10-14 The Research Foundation Of State University Of New York System and method for reduction of false positives during computer aided polyp detection
US20100290676A1 (en) * 2001-03-06 2010-11-18 Senga Advisors, Llc Daubechies wavelet transform of iris image data for use with iris recognition system
US7933507B2 (en) 2006-03-03 2011-04-26 Honeywell International Inc. Single lens splitter camera
US8045764B2 (en) 2005-01-26 2011-10-25 Honeywell International Inc. Expedient encoding system
US8050463B2 (en) 2005-01-26 2011-11-01 Honeywell International Inc. Iris recognition system having image quality metrics
US8049812B2 (en) 2006-03-03 2011-11-01 Honeywell International Inc. Camera with auto focus capability
US8063889B2 (en) 2007-04-25 2011-11-22 Honeywell International Inc. Biometric data collection system
US8064647B2 (en) 2006-03-03 2011-11-22 Honeywell International Inc. System for iris detection tracking and recognition at a distance
US8085993B2 (en) 2006-03-03 2011-12-27 Honeywell International Inc. Modular biometrics collection system architecture
US8090246B2 (en) 2008-08-08 2012-01-03 Honeywell International Inc. Image acquisition system
US8090157B2 (en) 2005-01-26 2012-01-03 Honeywell International Inc. Approaches and apparatus for eye detection in a digital image
US8098901B2 (en) 2005-01-26 2012-01-17 Honeywell International Inc. Standoff iris recognition system
US8213782B2 (en) 2008-08-07 2012-07-03 Honeywell International Inc. Predictive autofocusing system
US8280119B2 (en) 2008-12-05 2012-10-02 Honeywell International Inc. Iris recognition system using quality metrics
US8436907B2 (en) 2008-05-09 2013-05-07 Honeywell International Inc. Heterogeneous video capturing system
US8442276B2 (en) 2006-03-03 2013-05-14 Honeywell International Inc. Invariant radial iris segmentation
US8472681B2 (en) 2009-06-15 2013-06-25 Honeywell International Inc. Iris and ocular recognition system using trace transforms
US8630464B2 (en) 2009-06-15 2014-01-14 Honeywell International Inc. Adaptive iris matching using database indexing
US8705808B2 (en) 2003-09-05 2014-04-22 Honeywell International Inc. Combined face and iris recognition system
US8742887B2 (en) 2010-09-03 2014-06-03 Honeywell International Inc. Biometric visitor check system

Families Citing this family (20)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US7336806B2 (en) * 2004-03-22 2008-02-26 Microsoft Corporation Iris-based biometric identification
GB0412175D0 (en) * 2004-06-01 2004-06-30 Smart Sensors Ltd Identification of image characteristics
GB0427737D0 (en) * 2004-12-17 2005-01-19 Univ Cambridge Tech Method of identifying features within a dataset
JP4664147B2 (en) * 2005-07-29 2011-04-06 国立大学法人東北大学 Iris authentication device
KR100734857B1 (en) 2005-12-07 2007-07-03 한국전자통신연구원 Method for verifying iris using CPAChange Point Analysis based on cumulative sum and apparatus thereof
CN101093538B (en) 2006-06-19 2011-03-30 电子科技大学 Method for identifying iris based on zero crossing indication of wavelet transforms
JP2008090483A (en) * 2006-09-29 2008-04-17 Oki Electric Ind Co Ltd Personal identification system and personal identification method
US9846739B2 (en) 2006-10-23 2017-12-19 Fotonation Limited Fast database matching
US7809747B2 (en) * 2006-10-23 2010-10-05 Donald Martin Monro Fuzzy database matching
WO2009041963A1 (en) * 2007-09-24 2009-04-02 University Of Notre Dame Du Lac Iris recognition using consistency information
JP2009080522A (en) * 2007-09-25 2009-04-16 Mitsubishi Electric Corp Object image recognition device
US20100278394A1 (en) * 2008-10-29 2010-11-04 Raguin Daniel H Apparatus for Iris Capture
US8317325B2 (en) 2008-10-31 2012-11-27 Cross Match Technologies, Inc. Apparatus and method for two eye imaging for iris identification
US8577094B2 (en) 2010-04-09 2013-11-05 Donald Martin Monro Image template masking
CN102314731A (en) * 2010-07-06 2012-01-11 中国银联股份有限公司 Mobile payment method and equipment for implementing same
US9412022B2 (en) * 2012-09-06 2016-08-09 Leonard Flom Iris identification system and method
CN102902967B (en) * 2012-10-16 2015-03-11 第三眼(天津)生物识别科技有限公司 Method for positioning iris and pupil based on eye structure classification
KR20150003573A (en) * 2013-07-01 2015-01-09 한국전자통신연구원 Method and apparatus for extracting pattern of image
US20150142837A1 (en) * 2013-11-20 2015-05-21 Qliktech International Ab Methods And Systems For Wavelet Based Representation
KR101476173B1 (en) * 2013-11-28 2014-12-24 서강대학교산학협력단 User authentication method and system using iris characteristic

Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US5291560A (en) * 1991-07-15 1994-03-01 Iri Scan Incorporated Biometric personal identification system based on iris analysis
US6090051A (en) * 1999-03-03 2000-07-18 Marshall; Sandra P. Method and apparatus for eye tracking and monitoring pupil dilation to evaluate cognitive activity
US6247813B1 (en) * 1999-04-09 2001-06-19 Iritech, Inc. Iris identification system and method of identifying a person through iris recognition
US6424727B1 (en) * 1998-11-25 2002-07-23 Iridian Technologies, Inc. System and method of animal identification and animal transaction authorization using iris patterns
US6643406B1 (en) * 1999-07-28 2003-11-04 Polaroid Corporation Method and apparatus for performing linear filtering in wavelet based domain

Family Cites Families (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US5572596A (en) * 1994-09-02 1996-11-05 David Sarnoff Research Center, Inc. Automated, non-invasive iris recognition system and method
KR100374707B1 (en) * 2001-03-06 2003-03-04 에버미디어 주식회사 Method of recognizing human iris using daubechies wavelet transform
KR100453943B1 (en) * 2001-12-03 2004-10-20 주식회사 세넥스테크놀로지 Iris image processing recognizing method and system for personal identification
US8023699B2 (en) * 2007-03-09 2011-09-20 Jiris Co., Ltd. Iris recognition system, a method thereof, and an encryption system using the same

Patent Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US5291560A (en) * 1991-07-15 1994-03-01 Iri Scan Incorporated Biometric personal identification system based on iris analysis
US6424727B1 (en) * 1998-11-25 2002-07-23 Iridian Technologies, Inc. System and method of animal identification and animal transaction authorization using iris patterns
US6090051A (en) * 1999-03-03 2000-07-18 Marshall; Sandra P. Method and apparatus for eye tracking and monitoring pupil dilation to evaluate cognitive activity
US6247813B1 (en) * 1999-04-09 2001-06-19 Iritech, Inc. Iris identification system and method of identifying a person through iris recognition
US6643406B1 (en) * 1999-07-28 2003-11-04 Polaroid Corporation Method and apparatus for performing linear filtering in wavelet based domain

Cited By (35)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20100290676A1 (en) * 2001-03-06 2010-11-18 Senga Advisors, Llc Daubechies wavelet transform of iris image data for use with iris recognition system
US20080159600A1 (en) * 2001-03-06 2008-07-03 Senga Advisors, Llc. Iris image data processing for use with iris recognition system
US20040071300A1 (en) * 2002-10-10 2004-04-15 Texas Instruments Incorporated Sharing wavelet domain components among encoded signals
US7890335B2 (en) * 2002-10-10 2011-02-15 Texas Instruments Incorporated Sharing wavelet domain components among encoded signals
ES2224838A1 (en) * 2003-02-21 2005-03-01 Universidad Politecnica De Madrid System for biometric identification of people, by analyzing iris, for use in access control in e.g. buildings, has determination unit determining signature of iris, and comparison unit comparing captured image with stored image
WO2004084726A1 (en) * 2003-03-25 2004-10-07 Bausch & Lomb Incorporated Positive patient identification
US7436986B2 (en) 2003-03-25 2008-10-14 Bausch & Lomb Incorporated Positive patient identification
KR101126017B1 (en) * 2003-03-25 2012-03-19 보오슈 앤드 롬 인코포레이팃드 Positive patient identification
US8705808B2 (en) 2003-09-05 2014-04-22 Honeywell International Inc. Combined face and iris recognition system
US8488846B2 (en) 2005-01-26 2013-07-16 Honeywell International Inc. Expedient encoding system
US8098901B2 (en) 2005-01-26 2012-01-17 Honeywell International Inc. Standoff iris recognition system
US20100002913A1 (en) * 2005-01-26 2010-01-07 Honeywell International Inc. distance iris recognition
US8090157B2 (en) 2005-01-26 2012-01-03 Honeywell International Inc. Approaches and apparatus for eye detection in a digital image
US8045764B2 (en) 2005-01-26 2011-10-25 Honeywell International Inc. Expedient encoding system
US8050463B2 (en) 2005-01-26 2011-11-01 Honeywell International Inc. Iris recognition system having image quality metrics
US7761453B2 (en) 2005-01-26 2010-07-20 Honeywell International Inc. Method and system for indexing and searching an iris image database
US8285005B2 (en) * 2005-01-26 2012-10-09 Honeywell International Inc. Distance iris recognition
US20060222212A1 (en) * 2005-04-05 2006-10-05 Yingzi Du One-dimensional iris signature generation system and method
US20100260390A1 (en) * 2005-11-30 2010-10-14 The Research Foundation Of State University Of New York System and method for reduction of false positives during computer aided polyp detection
US8085993B2 (en) 2006-03-03 2011-12-27 Honeywell International Inc. Modular biometrics collection system architecture
US8442276B2 (en) 2006-03-03 2013-05-14 Honeywell International Inc. Invariant radial iris segmentation
US7933507B2 (en) 2006-03-03 2011-04-26 Honeywell International Inc. Single lens splitter camera
US8049812B2 (en) 2006-03-03 2011-11-01 Honeywell International Inc. Camera with auto focus capability
US8064647B2 (en) 2006-03-03 2011-11-22 Honeywell International Inc. System for iris detection tracking and recognition at a distance
US8761458B2 (en) 2006-03-03 2014-06-24 Honeywell International Inc. System for iris detection, tracking and recognition at a distance
US8160368B2 (en) * 2006-08-02 2012-04-17 Japan Science And Technology Agency Image feature extraction method and image compression method
US20090324064A1 (en) * 2006-08-02 2009-12-31 Japan Science And Technology Agency Image feature extraction method and image compression method
US8063889B2 (en) 2007-04-25 2011-11-22 Honeywell International Inc. Biometric data collection system
US8436907B2 (en) 2008-05-09 2013-05-07 Honeywell International Inc. Heterogeneous video capturing system
US8213782B2 (en) 2008-08-07 2012-07-03 Honeywell International Inc. Predictive autofocusing system
US8090246B2 (en) 2008-08-08 2012-01-03 Honeywell International Inc. Image acquisition system
US8280119B2 (en) 2008-12-05 2012-10-02 Honeywell International Inc. Iris recognition system using quality metrics
US8472681B2 (en) 2009-06-15 2013-06-25 Honeywell International Inc. Iris and ocular recognition system using trace transforms
US8630464B2 (en) 2009-06-15 2014-01-14 Honeywell International Inc. Adaptive iris matching using database indexing
US8742887B2 (en) 2010-09-03 2014-06-03 Honeywell International Inc. Biometric visitor check system

Also Published As

Publication number Publication date Type
KR100374707B1 (en) 2003-03-04 grant
EP1374145A1 (en) 2004-01-02 application
US7302087B2 (en) 2007-11-27 grant
WO2002071317A1 (en) 2002-09-12 application
JP2002269564A (en) 2002-09-20 application
US20100290676A1 (en) 2010-11-18 application
CN1493056A (en) 2004-04-28 application
JP2004527832A (en) 2004-09-09 application
KR20020071329A (en) 2002-09-12 application
US20040114781A1 (en) 2004-06-17 application
CN1258733C (en) 2006-06-07 grant
EP1374145A4 (en) 2006-10-18 application

Similar Documents

Publication Publication Date Title
Jing et al. A face and palmprint recognition approach based on discriminant DCT feature extraction
Almansa et al. Fingerprint enhancement by shape adaptation of scale-space operators with automatic scale selection
Tuyls et al. Practical biometric authentication with template protection
US6418238B1 (en) Image detection apparatus and image detection method capable of detecting roundish shape
US7110581B2 (en) Wavelet-enhanced automated fingerprint identification system
US5659626A (en) Fingerprint identification system
Monro et al. DCT-based iris recognition
Rattani et al. Feature level fusion of face and fingerprint biometrics
US5291560A (en) Biometric personal identification system based on iris analysis
US7039221B1 (en) Facial image verification utilizing smart-card with integrated video camera
US5974163A (en) Fingerprint classification system
US6963659B2 (en) Fingerprint verification system utilizing a facial image-based heuristic search method
US6067369A (en) Image feature extractor and an image feature analyzer
US6591002B2 (en) Association of finger pores and macrofeatures for identification of individuals
US6128398A (en) System, method and application for the recognition, verification and similarity ranking of facial or other object patterns
US20060171571A1 (en) Systems and methods for quality-based fusion of multiple biometrics for authentication
US20060008124A1 (en) Iris image-based recognition system
Sanchez-Avila et al. Iris-based biometric recognition using dyadic wavelet transform
US6920231B1 (en) Method and system of transitive matching for object recognition, in particular for biometric searches
US20070160267A1 (en) Method for localizing irises in images using gradients and textures
US7142699B2 (en) Fingerprint matching using ridge feature maps
de Martin-Roche et al. Iris recognition for biometric identification using dyadic wavelet transform zero-crossing
Noh et al. Palmprint identification algorithm using Hu invariant moments and Otsu binarization
US20060104504A1 (en) Face recognition method and apparatus
US20070160308A1 (en) Difference of sum filters for texture classification

Legal Events

Date Code Title Description
AS Assignment

Owner name: EVERMEDIA CO., LTD., KOREA, REPUBLIC OF

Free format text: ASSIGNMENT OF ASSIGNORS INTEREST;ASSIGNOR:CHO, SEONG-WON;REEL/FRAME:012258/0232

Effective date: 20010810