WO2002071317A1 - Method of recognizing human iris using daubechies wavelet transform - Google Patents
Method of recognizing human iris using daubechies wavelet transform Download PDFInfo
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- WO2002071317A1 WO2002071317A1 PCT/KR2001/001303 KR0101303W WO02071317A1 WO 2002071317 A1 WO2002071317 A1 WO 2002071317A1 KR 0101303 W KR0101303 W KR 0101303W WO 02071317 A1 WO02071317 A1 WO 02071317A1
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- 238000000034 method Methods 0.000 title claims abstract description 59
- 239000013598 vector Substances 0.000 claims abstract description 121
- 229910052736 halogen Inorganic materials 0.000 claims description 6
- 150000002367 halogens Chemical class 0.000 claims description 6
- 238000012795 verification Methods 0.000 claims description 4
- 238000007781 pre-processing Methods 0.000 claims description 3
- 230000008569 process Effects 0.000 abstract description 12
- 238000012545 processing Methods 0.000 abstract description 9
- 238000005259 measurement Methods 0.000 abstract description 6
- 238000003860 storage Methods 0.000 abstract description 3
- 208000016339 iris pattern Diseases 0.000 description 9
- 238000013139 quantization Methods 0.000 description 8
- 230000008901 benefit Effects 0.000 description 3
- 238000000605 extraction Methods 0.000 description 3
- 230000006872 improvement Effects 0.000 description 3
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- BORBLDJNKYHVJP-FXBDTBDDSA-N dolichodial Chemical compound C[C@H]1CC[C@H](C(=C)C=O)[C@@H]1C=O BORBLDJNKYHVJP-FXBDTBDDSA-N 0.000 description 1
- 230000004807 localization Effects 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 230000009467 reduction Effects 0.000 description 1
- 210000003786 sclera Anatomy 0.000 description 1
- 238000012546 transfer Methods 0.000 description 1
- 230000001131 transforming effect Effects 0.000 description 1
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- 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
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- 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
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V10/00—Arrangements for image or video recognition or understanding
- G06V10/40—Extraction of image or video features
- G06V10/46—Descriptors for shape, contour or point-related descriptors, e.g. scale invariant feature transform [SIFT] or bags of words [BoW]; Salient regional features
- G06V10/478—Contour-based spectral representations or scale-space representations, e.g. by Fourier analysis, wavelet analysis or curvature scale-space [CSS]
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V10/00—Arrangements for image or video recognition or understanding
- G06V10/70—Arrangements for image or video recognition or understanding using pattern recognition or machine learning
- G06V10/74—Image or video pattern matching; Proximity measures in feature spaces
- G06V10/75—Organisation of the matching processes, e.g. simultaneous or sequential comparisons of image or video features; Coarse-fine approaches, e.g. multi-scale approaches; using context analysis; Selection of dictionaries
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- 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
Definitions
- the inputted iris image is multi-divided by using the Daubechies wavelet transform. Since the iris image is considered as 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, through 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.
- f R is the characteristic vector of the user that has been already registered
- fr is the characteristic vector of the user that is generated from the iris , image of the eye of the user.
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Abstract
The present invention relates to a method of recognizing the human iris corresponding to a field of a biometric technology, and more particularly to a method of recognizing human iris using Daubechies wavelet transform, wherein the dimensions of characteristic vectors are reduced by extracting iris features from inputted iris image signals through the Daubechies wavelet transform, binary characteristic vectors are generated by applying quantiztion functions to the extracted characteristic values so that utility of human iris recognition can be improved since storage capacity and processing time thereof can be reduced by generating low capacity characteristic vectors, and a measurement process suitable for the low capacity characteristic vectors is employed when measuring the similarity between the generated characteristic vectors and previously registered characteristic vectors.
Description
METHOD OF RECOGNIZING HUMAN IRIS USING DAUBECHIES WAVELET TRANSFORM
Field of Invention
The present invention relates to a method of recognizing the human iris using Daubechies wavelet transform. More particularly, the present invention is directed to a method of recognizing human iris using Daubechies wavelet transform, wherein the dimensions of characteristic vectors are reduced by extracting iris features from inputted iris image signals through the Daubechies wavelet transform, binary characteristic vectors are generated by applying quantiztion functions to the extracted characteristic values so that utility of human iris recognition can be improved since storage capacity and processing time thereof can be reduced by generating low capacity characteristic vectors, and a measurement process suitable for the low capacity characteristic vectors is employed when measuring the similarity between the generated characteristic vectors and previously registered characteristic vectors.
Background of Invention
An iris recognition system is an apparatus for performing identification of each individual by differentiating iris patterns of the pupil of an eye, which are unique for each individual. It has superior identification accuracy and excellent security as compared with other biometric method using voice and fingerprints from each individual. A human iris is the region between a pupil and a white sclera of an eye, and iris recognition is a technique for performing identification of each individual based on information that is obtained from an analysis of the iris patterns which are different in each individual.
In general, it is a core technology to efficiently acquire unique characteristic information from input images in the field of an applied technology for performing identification of each individual by utilizing the characteristic information of the human body. A wavelet transform is used to extract characteristics of the iris images, and it is a kind of technique of analyzing signals in multiresolution mode. The wavelet transform is a mathematical theory of formulating a model for signals, systems and a series of processes by using specifically selected signals. 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 since it has a fast rate as compared with a conventional signal processing algorithm based on the Fourier transform, and it can efficiently accomplish signal localization in time and frequency domains.
On the other hand, the images, which are obtained by extracting only iris patterns from the iris images ' acquired by image acquisition equipment and normalizing the patterns at a 450 x 60 size, are used to extract characteristic values through the wavelet transform. Further, a Harr wavelet transform has been widely used in conventional iris recognition, image processing and the like. However, Harr wavelet functions have disadvantages in that the characteristic values are discontinuously and rapidly changed and that high resolution of the images cannot be obtained in a case where the images are again decompressed after they have been compressed. On the contrary, since Daubechies wavelet functions are continuous functions, the disadvantages of the Harr wavelet functions that the values thereof are discontinuously and rapidly changed can be avoided^ and the characteristic values can be extracted more accurately and delicately. Therefore, in a case where the images are to be again decompressed after they have been compressed by using the Daubechies wavelet transform, the images can be restored in high resolution nearer to the original images than when ' the Harr wavelet transform is used. Since the Daubechies wavelet functions are more complicated than the Harr wavelet functions, there is a disadvantage in that more arithmetic quantity may be needed. However, it can be easily overcome by the recent advent of ultrahigh speed microprocessors.
There is also an advantage in that the Daubechies wavelet transform can obtain fine characteristic values in the process of performing the wavelet transform for extracting the characteristic values. That is, if the Daubechies wavelet transform is used, expression of the iris features can be made in a low capacity of data and extraction of the features can be made accurately.
Methods of extracting the characteristic values and forming the characteristic vectors by using Gabor transform been mainly used in the conventional iris recognition field. However, the characteristic vectors generated by these methods are formed to have 256 or more dimensions, and they have at least 256 bytes even
though it is assumed that one byte is assigned to one dimension. Thus, there is a problem in that practicability and efficiency can be reduced when it is used in the field where low capacity information is needed. Accordingly, it is necessary to develop a method of forming the low capacity characteristic vectors wherein processing, storage, transfer, search, and the like of the pattern information can be efficiently made. In addition, since a simple method of measuring a distance such as a Hamming distance (HD) between two characteristic vectors (characteristic vectors relevant to the input pattern and stored reference characteristic vectors) is used for pattern classification in a prior art such as U.S. Patent No. 5,291,560, there are disadvantages in that formation of the reference characteristic vectors through generalization of the pattern information cannot be easily made and infonriation characteristics of each dimension of the characteristic vectors cannot be properly reflected.
That is, in the method of using the Hamming distance in order to verify the two characteristic vectors generated in the form of binary vectors, bit values assigned according to respective dimensions 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, a value divided by the total number of the dimensions is obtained as a final result. The method is simple and useful in discriminating a degree of similarity between the characteristic vectors consisted of binary codes. When the Hamming distance is used, the comparison result of all the bits becomes 0 if identical data are compared with each other. Thus, the result approaching to 0 implies that the data belong to the persons themselves. If the data do indeed belong to the person, the probability of a degree of similarity will be 0.5. Thus, upon comparison with the other person's data, it is understood that the values converge around 0.5. Accordingly, a proper limit set between 0 and 0.5 will be a boundary for differentiating the data of the persons themselves from the other person's data. The Hamming distance (HD) is excellent in performance thereof in a case where the information is obtained from the extracted iris features by subdividing the data, but it is not suitable when low capacity data is to be used. In other words, in a case where total number of the bits of the characteristic vectors having 256-byte information is 2048, considerably high acceptance rates can be achieved even though the Hamming distance is applied. However, in a case where low capacity characteristic vectors in
which the number of characteristic vectors is reduced are used as in the present invention, high acceptance rates cannot be obtained.
On the other hand, in a case where the low capacity characteristic vectors are used, improvement of the acceptance rate is limited to a certain extent since lost information is increased. Thus, a method of preventing loss of the information while maintaining minimum capacity of the characteristic vectors should be considered in the process of generating the characteristic vectors.
Summary of Invention
Therefore, the present invention is contemplated to solve these problems mentioned above. An object of the present invention is to provide a method of forming low capacity characteristic vectors wherein a false acceptance rate (FAR) and a false rejection rate (FRR) can be remarkably reduced, as compared with a conventional Harr wavelet transform, by extracting iris features from inputted iris image signals through a Daubechies wavelet transform.
Another object of the present invention is to provide a method of measuring the similarity between characteristic vectors, wherein loss of information produced when low capacity characteristic vectors are formed can be minimized and the low capacity characteristic vectors can be properly used for the similarity measurement.
In order to achieve the above objects of the present invention, there is provided a method of recognizing the human iris using the Daubechies wavelet transform, wherein preprocessing for extracting only an iris image from an eye image of a user acquired by image acquisition equipment using a halogen lamp illuminator is performed and identification of the user is determined by the preprocessed iris image comprising (a) repeatedly performing the Daubechies wavelet transform of the preprocessed iris image at predetermined times so as to multi-divide the iris image, and extracting an image including high frequency components from the multi-divided image so as to extract iris features; (b) extracting characteristic values of a characteristic vector from the extracted image including the high frequency components, and generating a binary characteristic vector by quantizing relevant characteristic values; and (c) determining the user as an enrollee by measuring similarity between the generated characteristic vector and a previously registered
characteristic vector.
The present invention will be briefly described below. The iris image is acquired through the image acquisition equipment in which the halogen lamp illuminator is used. By repeatedly performing the Daubechies wavelet transform of the inputted iris image, the iris image is multi-divided and iris features having optimized sizes thereof are extracted. The characteristic vector, which is effective in displaying and processing the image, is then formed by quantizing the extracted characteristic values. Since the Daubechies wavelet transform is used as a wavelet transform, more accurate characteristic values can be extracted while maintaining maximum advantage of the wavelet. Furthermore, in a case where the dimension of the characteristiς vector is reduced by quantizing the extracted characteristic values into binary values, that is, when a low capacity characteristic vector is formed, a method of measuring similarity between the weighted registered and inputted characteristic vectors is properly used to prevent reduction of acceptance resulting from the formation of the low capacity characteristic vector. The user authenticity is therefore determined by the foregoing method.
Brief Description of the Drawings
FIG. 1 is a view showing the constitution of image acquisition equipment used for performing an iris recognition method according to the present invention.
FIG. 2 is a flowchart showing procedures for verifying an iris image according to the present invention.
FIG. 3 is a flowchart showing procedures for multi-dividing the iris image through Daubechies wavelet transform according to the present invention. FIG. 4 shows an example of multi-dividing the iris image through the
Daubechies wavelet transform. '
FIG. 5 is a flowchart showing procedures for forming a characteristic vector of the iris image based on data acquired from the procedures of multi-dividing the iris image according to the present invention. FIG. 6a shows a distribution example of characteristic values of the extracted iris image.
FIG. 6b shows a quantization function for generating binary characteristic vector from the distribution example of FIG. 6a.
FIG. 7 is a flowchart showing procedures for determining user authenticity
through a similarity measurement between the characteristic vectors.
Detailed Description for Preferred Embodiment Hereinafter, a method of recognizing a human iris using a Daubechies wavelet transform according to the present invention will be explained in detail with reference to the accompanying drawings.
FIG. 1 shows the constitution of image acquisition equipment for use in a method of recognizing a human iris according to the present invention. Referring to FIG. 1, the constitution of the iris image acquisition equipment will be explained. The image acquisition equipment for use in the method of recognizing the human iris according to the present invention comprises a halogen lamp 11 for illuminating the iris in order to acquire clear iris patterns, a CCD camera 13 for photographing an eye 10 of a user through a lens 12, a frame grabber 14 connected to the CCD camera 12 for acquiring an iris image, and a monitor 15 for showing the image, which are currently inputted to the camera, to the user so that acquisition of correct images and positioning convenience of the user can be obtained when images are acquired.
According to the constitution of the image acquisition equipment, the CCD camera is used to acquire the image, and iris recognition is made through a pattern analysis of iridial folds. However, in a case where the iris image is acquired indoors by using an ordinary illuminator, it is difficult to extract desired pattern information since the iris image is generally gloomy. Additional illuminators should therefore be used so that the information on the iris image cannot be lost. In such a case, loss of the iris pattern information and deterioration of recognition capability due to reflective light should be prevented, and proper illuminators should be utilized so that a clear iris pattern can be obtained, hi the present invention, the halogen lamp 11 having strong floodlighting effects is used as a main illuminator so that the iris pattern can be clearly shown. Further, 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 outer portions of the iris region.
FIG. 2 is a flowchart showing procedures for verifying the iris image according to the present invention. Referring to FIG. 2, an eye image is acquired
through the image acquisition equipment as mentioned above in step 200. In step 210, images of the iris regions are extracted from the acquired eye image through preprocessing and transformed into a polar coordinate system, and the transformed iris pattern is inputted to a module for extracting the features. 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 have real numbers. In step 230, a binary characteristic vector is generated by applying K-level quantization function to the extracted features. In step 240, similarity between the generated characteristic vector and previously registered data of the user is measured. Through the similarity measurement, user authenticity is determined and then verification results are shown.
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 having eight, sixteen or more coefficients can extract more delicate characteristic values than the Daubechies wavelet function having four coefficients, even though the former is more complicate than the latter. Although the Daubechies wavelet function having eight or more coefficients has been used and tested in the present invention, greater performance improvement was not obtained from the present invention and arithmetic quantity and processing time are increased, as compared with a case where the Daubechies wavelet function having four coefficients has been used and tested. Thus, the Daubechies wavelet function having four coefficients has been used for extracting the characteristic values..
FIG. 3 is a flowchart showing procedures for multi-dividing the iris image by performing the Daubechies wavelet transform according to the present invention, and FIG. 4 shows an image divided through the Daubechies wavelet transform. Referring to FIGS. 3 and 4, in the present invention, the Daubechies wavelets among various mother wavelets are used to perform extraction of the iris image characteristics. As shown in FIG. 4, when "L" and "H" are respectively used to indicated low frequency and high frequency components, the term "LL" means a component that has passed through a low-pass filter (LPF) in all x and y directions whereas a term "HH" means a component that has passed through a high-pass filter (HPF) in the x and y directions. Furtliennore, subscript numerals signify image-
dividing stages. ' For example, "LH2" 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 2-stage wavelet division.
In step 310, the inputted iris image is multi-divided by using the Daubechies wavelet transform. Since the iris image is considered as 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, through 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.
Alternatively, 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 a probability that entrance of unregistered persons (imposters) may be accepted due to false recognition of unregistered persons as registered ones, and the FRR means a probability that entrance of registered persons (enrollees) is rejected due to false recognition of the registered persons as unregistered ones. For reference, when the method of recognizing the human iris using the Daubechies wavelet transfoπn according to the present invention is 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. In step 320, a region HH including only high frequency components in the x and y directions is extracted from the divided iris image.
In step 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 as information for extracting the iris features in step 340.
Further, in step 350, a region LL comprising only low frequency components
in the x and y directions is extracted from the multi-divided iris image. Since 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 again applied to the relevant region. Thereafter, the Daubechies wavelet transform is repeatedly performed from step 310.
On the other hand, in a case where the iris image is transformed from the
«
Cartesian coordinate system to 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 x 60 (θ x r) data. Then, if the acquired iris image is once again wavelet-transformed, the characteristics of the 225 x 30 region HHi of which size is reduced by half are obtained, namely, the 225 x 30 information is used as a characteristic vector. This information may be used as it is, but a 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 HSU are obtained by successively applying the wavelet transform to respective relevant regions.
The iterative number, which is provided as a discriminating criterion for repeatedly performing the wavelet transform, should be set as a proper value in consideration of loss of the information and size of the characteristic vector. Therefore, in the present invention, the region HH4 obtained by performing the wavelet transform four times becomes a major characteristic region, and values thereof are selected as components of the characteristic vector. At this time, the region HE ÷ contains the information having 84 (= 28 x 3) data.
FIG. 5 is a flowchart showing procedures for forming the characteristic vector of the iris image by using the data acquired from the process of multi-dividing the 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 HHi, HH2, HH3, and HH* is inputted in step 510. In step 520, in order to
acquire the characteristic information on the regions HHi, HH2 and HH3 excluding the information on the region HFI4 obtained through the last wavelet transform among the n characteristic vector, each average value of the regions HHi, HH2 and HH3 is calculated and assigned one dimension. In step 530, all the values of the final obtained region HHφ 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 540, the N-l characteristic values extracted from step 520 and the M (the sjze of the final obtained region HH) characteristic values extracted from step 530 are combined and (M-HN-l)-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 HHls HH2 and HH3 are combined, are used as a characteristic vector in the present invention.
In step 550, the values of the previously obtained characteristic vector, i.e., respective component values of the characteristic vector expressed in the form of the real numbers are quantized into binary values 0 or 1. h step 560, the resultant (M+N-l)-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. 6a 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 respective dimensions, the distribution roughly takes a shape of FIG. 6a. The binary vector . including all the dimensions is generated by the following Equation 1. [Equation 1] fn = 0 iff(n) < 0 fn = l iff(n) > 0 where f(n) is a characteristic value of the n-th dimension and fn is a 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, improvement of the recognition rate is limited to some extent since 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.
FIG. 6b 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-l)-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.
[Equation 2] fn = 4 if f(n) > 0.5 (level 4) fn = l if0.5 > f(n) ≥ 0 (level 3) fn = -1 if 0 > f(n) > -0.5 (level 2) f„ = -4 iff(n) < -0.5 (level 1) where fn means an n-th dimension of the previously registered characteristic vector fR of the user or the characteristic vector fr of the user generated from the iris image of the eye image of the user. Explanation of how to use the weights expressed in Equation 2, is as follows.
In a case where the h-th dimensional characteristic value f(n) is equal or more than 0.5 (level 4), the value of the i-th dimension fR; or n 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 f*Ri or n 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 f* Rj or is converted and assigned -1 if the value is "01". In a case where the n-th dimensional characteristic value f(n) is equal, or less than -0.5 (level 1), the value of the i-th dimension Rj or n is converted and assigned -4 if the value is "00". This is due to the weights being applied to 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 procedures for discriminating the user authenticity through similarity measurement between the characteristic vectors. Referring to FIG. 7, in step 710, the characteristic vector f*τ of the user is generated from the iris image of the eye image of the user. In step 720, the previously registered characteristic vector f of the user is searched. In step 730, in order to measure the similarity between the two characteristic vectors, the weights are assigned to the characteristic vectors fR and fr depending on the value of the binary characteristic vector based on Equation 2. In step 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 correlation between the registered characteristic vector f and the characteristic vector fr of the user, it is the inner product S of the two characteristic vectors which 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. [Equation 3]
n
S = _ __, fm fn = ( ΪR \ f τ & f R I fτ ι + — + J ' rm Tn ) i = \
where fR is the characteristic vector of the user that has been already registered, and fr 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 respective dimensions can be maintained. 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 the 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 does not belong to an identical person.
In step 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.
[Equation 4]
If S > C. then TRUE or else FALSE where C is a reference value for verifying the similarity between the two characteristic vectors. That is, if the inner product S of the two characteristic vectors is equal or more than the verification reference value C, the user is determined as an enrollee. Otherwise, the user is determined as an imposter.
As described above, the method of recognizing the human iris using the
Daubechies wavelet transform according to the present invention has an advantage that FAR and FRR can be remarkably reduced as compared with the method using the conventional Harr wavelet .transform, since the iris features are extracted from the inputted iris image signals through the Daubechies wavelet transform.
Furthermore, in order to verify the similarity between the registered and extracted characteristic vectors fR and fr, 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 loss of the information, which may be produced by forming the low capacity characteristic vectors, can be miiiimized.
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. 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 scope of the present invention defined by the appended claims.
Claims
1. A method of recognizing a human iris using the Daubechies wavelet transform, wherein a preprocessing for extracting only an iris image from an eye image of a user acquired by image acquisition equipment using a halogen lamp illuminator is performed and identification of the user is determined by the preprocessed iris image, comprising:
(a) repeatedly performing the Daubechies wavelet transform of said preprocessed iris image at predetermined times so as to multi-divide said iris image, and extracting image including high frequency components from said multi-divided image so as to extract iris features;
(b) extracting characteristic values of a characteristic vector from said extracted image including said high frequency components, and generating a binary characteristic vector by quantizing relevant characteristic values; and
(c) determining said user as an enrollee by measuring a similarity between said generated characteristic vector and a previously registered characteristic vector.
2. The method of recognizing the human iris using the Daubechies wavelet transform as claimed in claim 1, wherein said step (a) comprises the steps of extracting a region HH of said image including said high frequency components in both x and y directions at every stage of multi-dividing said image, storing information on said region HH as information for extracting said iris features, perfonning multi-division of a region LL of said image including low frequency components in both x and y directions if an iterative number of times is less than a predetermined number of times, and completing said multi-division of said image when said iterative number reaches said predetermined number.
3. The method of recognizing the human iris using the Daubechies wavelet transform as claimed in claim 2, wherein said predetermined number is set at four.
4. The method of recognizing the human iris using the Daubechies wavelet transform as claimed in claim 1, wherein said step (b) comprises the steps of receiving multi-divided images of a plurality of high frequency regions HHj formed by said multi-division of said step (a); calculating average values of regions HHt to HHN-I excluding the final obtained region HHN among all the regions corresponding to said received image and assigning said N-1 calculated average values to components of said characteristic vector, respectively; assigning all M values existed in said lastly obtained region HHN to the other components of said characteristic vector; combining said N-1 average values and said M values so as to generate a (M+N-l)-dimensional characteristic vector; and quantizing said respective component values of said generated characteristic vector into binary values so as to generate a final (M+N-l)- dimensional characteristic vector.
5. The method of recognizing the human iris using the Daubechies wavelet transform as claimed in claim 1, wherein said step (c) comprises the steps of applying predetermined weights to i-th dimensions of said generated characteristic vector generated from said step (b) and said previously registered characteristic vector; calculating inner product S of said two weighted characteristic vectors; and determining said user as an enrollee when said inner product S is more than a verification reference value C.
Priority Applications (4)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| EP01953361A EP1374145A4 (en) | 2001-03-06 | 2001-07-31 | Method of recognizing human iris using daubechies wavelet transform |
| JP2002570166A JP2004527832A (en) | 2001-03-06 | 2001-07-31 | Iris Recognition Method Using Dobesy Wavelet Transform |
| US10/656,885 US7302087B2 (en) | 2001-03-06 | 2003-09-05 | Daubechies wavelet transform of iris image data for use with iris recognition system |
| US11/941,019 US20100290676A1 (en) | 2001-03-06 | 2007-11-15 | Daubechies wavelet transform of iris image data for use with iris recognition system |
Applications Claiming Priority (2)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| KR2001/11440 | 2001-03-06 | ||
| KR10-2001-0011440A KR100374707B1 (en) | 2001-03-06 | 2001-03-06 | Method of recognizing human iris using daubechies wavelet transform |
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| Application Number | Title | Priority Date | Filing Date |
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| US10/656,885 Continuation US7302087B2 (en) | 2001-03-06 | 2003-09-05 | Daubechies wavelet transform of iris image data for use with iris recognition system |
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| WO2002071317A1 true WO2002071317A1 (en) | 2002-09-12 |
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| Application Number | Title | Priority Date | Filing Date |
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| PCT/KR2001/001303 WO2002071317A1 (en) | 2001-03-06 | 2001-07-31 | Method of recognizing human iris using daubechies wavelet transform |
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| 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) |
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| CN109034206A (en) * | 2018-06-29 | 2018-12-18 | 泰康保险集团股份有限公司 | Image classification and recognition method, device, electronic device, and computer-readable medium |
Also Published As
| Publication number | Publication date |
|---|---|
| US20040114781A1 (en) | 2004-06-17 |
| JP2004527832A (en) | 2004-09-09 |
| CN1258733C (en) | 2006-06-07 |
| KR100374707B1 (en) | 2003-03-04 |
| US7302087B2 (en) | 2007-11-27 |
| KR20020071329A (en) | 2002-09-12 |
| EP1374145A4 (en) | 2006-10-18 |
| US20100290676A1 (en) | 2010-11-18 |
| JP2002269564A (en) | 2002-09-20 |
| CN1493056A (en) | 2004-04-28 |
| US20020150281A1 (en) | 2002-10-17 |
| EP1374145A1 (en) | 2004-01-02 |
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