WO2014017697A1 - Procédé et dispositif d'extraction de motifs de veines de doigt à l'aide d'un filtre de gabor guidé - Google Patents

Procédé et dispositif d'extraction de motifs de veines de doigt à l'aide d'un filtre de gabor guidé Download PDF

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WO2014017697A1
WO2014017697A1 PCT/KR2012/008410 KR2012008410W WO2014017697A1 WO 2014017697 A1 WO2014017697 A1 WO 2014017697A1 KR 2012008410 W KR2012008410 W KR 2012008410W WO 2014017697 A1 WO2014017697 A1 WO 2014017697A1
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image
guided
vein pattern
finger vein
filter
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PCT/KR2012/008410
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English (en)
Korean (ko)
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해산연
박동선
윤숙
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전북대학교산학협력단
목포대학교산학협력단
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Publication of WO2014017697A1 publication Critical patent/WO2014017697A1/fr

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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
    • G06V10/88—Image or video recognition using optical means, e.g. reference filters, holographic masks, frequency domain filters or spatial domain filters
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/0059—Measuring for diagnostic purposes; Identification of persons using light, e.g. diagnosis by transillumination, diascopy, fluorescence
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/117—Identification of persons
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/48—Other medical applications
    • A61B5/4887—Locating particular structures in or on the body
    • A61B5/489—Blood vessels
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V10/00—Arrangements for image or video recognition or understanding
    • G06V10/10—Image acquisition
    • 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
    • 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
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B2576/00—Medical imaging apparatus involving image processing or analysis
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/0033—Features or image-related aspects of imaging apparatus classified in A61B5/00, e.g. for MRI, optical tomography or impedance tomography apparatus; arrangements of imaging apparatus in a room
    • A61B5/0037—Performing a preliminary scan, e.g. a prescan for identifying a region of interest
    • 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
    • G06V40/14—Vascular patterns
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H30/00—ICT specially adapted for the handling or processing of medical images
    • G16H30/40—ICT specially adapted for the handling or processing of medical images for processing medical images, e.g. editing

Definitions

  • the present invention relates to a finger vein pattern extraction method and apparatus, and more particularly to a finger vein pattern extraction method and apparatus using a guided Gabor filter.
  • the user authentication method using biometrics among user authentication methods is a user authentication method that has recently been in the spotlight as a method of providing reliable security since the user's body information is used as a security medium for user authentication.
  • biometrics authentication methods include fingerprint recognition, hand shape, face contours (eyes, nose, eyebrows, mouth, cheeks, etc.), ear shapes, voiceprints, Authentication methods using various biometrics such as the eye retina, iris, back of hand, vein pattern of finger, sign, DNA, etc. have been devised and are expanding the scope of application.
  • Fingerprint recognition has been in the spotlight as the most widely used biometrics technology for user authentication.
  • various counterfeit methods such as counterfeit fingerprints have recently emerged.
  • fingerprint recognition has a problem that it is difficult to recognize when the finger is wound or when the finger is dry.
  • Finger vein pattern identification is emerging as a new user authentication method that can replace fingerprint recognition because finger vein is more difficult to forge than fingerprint and has unique pattern for each person like fingerprint.
  • the finger vein unlike the fingerprint in the human body, is less affected by the human condition, and the chance of injury is very low. In addition, since the pattern does not change from birth to death, it is suitable for user authentication.
  • Finger vein pattern identification is to determine the pattern of the finger vein using the characteristic that the hemoglobin in the vein absorbs infrared light and appears darker than other areas. Vein pattern identification captures vein patterns using harmless infrared light or near-infrared light with a wavelength between 0.7 ⁇ m and 1 ⁇ m. By absorbing it is represented by the shadow area.
  • Conventional finger vein pattern identification methods include a curvature-based method, a line tracking method, and a gabor filter method.
  • the finger vein pattern identification method has difficulty in extracting the finger vein due to uneven lighting or local illumination changes caused by various tissues and bones inside the finger. It is difficult to determine the threshold for dividing a, and there is a problem in that performance decreases rapidly according to the image quality of the image.
  • An object of the present invention is to provide a finger vein pattern extraction method using a guided Gabor filter to enable clear finger vein pattern extraction without segmentation of the image.
  • Another object of the present invention to provide a finger vein pattern extraction apparatus using the finger vein pattern extraction method.
  • Finger vein pattern extraction apparatus for achieving the above object to obtain a capture image including the vein pattern of the user by using infrared light, and from the capture image to the region of interest including the finger vein
  • a preprocessor to set A guided filter unit configured to perform guided filtering on the captured image acquired by the image acquisition unit using a preset guide image to output a guided image having an improved image quality than the captured image
  • a Gabor filter unit for performing Gabor filtering on the guided image to extract the shape of the ridges from the guided image and outputting the filtered image. It includes.
  • the finger vein pattern extracting apparatus may further include a pre-processing unit receiving the captured image from the image obtaining unit and performing a pre-processing operation on the received capture vein image to transmit the pre-processed captured image to the guided filter unit. Characterized in that.
  • the pre-processing unit extracts at least one boundary line from the captured image to generate an edge image consisting of a plurality of edge points set;
  • An image rotation unit which detects the at least one boundary line direction from the edge image and rotates the captured image in a predetermined direction according to the direction of the at least one boundary line;
  • a region of interest setting unit which receives the captured image from the image rotating unit, sets a specific region of the captured image as a region of interest according to a preset method, and outputs the captured image as the preprocessed captured image to the guided filter. It is characterized by.
  • the edge image extractor may generate the edge image by filtering the captured image using one of a top-hat filter and a Sobel filter.
  • the image rotating unit detects the direction of the boundary line by performing a Hough transform on the edge image, and rotates the captured image according to whether the detected boundary line direction satisfies a predetermined condition.
  • the image rotating unit is a mathematical equation for a set of edge points ⁇ (x 1 , y 1 ), (x 2 , y 2 ) ... (x k , y k ) ⁇ included in the edge image.
  • the captured image is rotated at an angle of ( ⁇ 1 + ⁇ 2 ) / 2.
  • the ROI setting unit sets an ROI with a predetermined size based on a point C on the captured image output from the image rotating unit, and the coordinate of the point is
  • the guided filter unit has a relation between the guide image and the guided image
  • G u is a guided image filtered by a guided filter
  • ⁇ k is a window centered on pixel k
  • a k and b k are linear coefficients.
  • ⁇ is a normalization parameter for preventing the linear coefficient a k from increasing, and an edge preservation coefficient (edge) indicating the degree of edge preservation on the guided image (G u ) preserving coefficient).
  • ⁇ k and ⁇ k 2 are the mean and variance values of the guide image I in the window ⁇ k , respectively.
  • the guided filter unit uses the guided image (G u ) as a guide image (I), and an equation for the input image (p) of the guide filter.
  • W ij (I, ⁇ , ⁇ ) is the kernel weight.
  • the kernel weight is calculated by
  • the guided filter unit may perform the guided filtering using the captured image as the guide image.
  • ⁇ is a standard deviation (or scale) of an elliptic Gaussian envelope.
  • the Gabor filter unit sets the filtering direction ( ⁇ k ) of the Gabor filter in eight directions, It is characterized by that.
  • the finger vein pattern extracting apparatus may include: a quality estimating unit configured to receive the filtering image from the Gabor filter unit and to determine whether the quality of the vein pattern included in the filtering image is greater than or equal to a predetermined reference quality; And a feature extractor configured to receive the filtered image including the vein pattern having the reference quality or higher and extract a feature of the vein pattern included in the filtered image.
  • the feature extractor divides the filtered image into a plurality of small blocks, and extracts an average absolute deviation of each of the divided plurality of small blocks F mn as a feature of the captured image corresponding to the filtered image. It is characterized by.
  • the feature extracting unit is the average absolute deviation ADD ( ⁇ k mn ) for each of the plurality of small blocks F mn .
  • Finger vein pattern extraction method for achieving the above another object is a finger vein pattern extraction apparatus including an image acquisition unit, a guided filter unit and a Gabor filter unit for obtaining a captured image using infrared light
  • a method for extracting finger vein pattern comprising: obtaining, by the image obtaining unit, the captured image including a vein pattern of a user; Outputting a guided image having improved image quality than the captured image by performing the guided filtering on the captured image by the guided filter unit; And outputting the filtered image by performing Gabor filtering on the guided image to extract the shape of the ridges from the guided image by the Gabor filter unit. It includes.
  • the method and apparatus for extracting finger vein pattern using the guided Gabor filter of the present invention protects the vein pattern by using the guided filter, and is affected by background effects such as blur, lighting, ambient conditions and changes in blood flow.
  • the ridgeline of the vein pattern can be easily extracted to make clear the distinction between the vein pattern and the background.
  • the fast operation speed enables not only to extract vein patterns in real time, but also to perform accurate image matching during user authentication.
  • FIG. 1 illustrates a finger vein pattern extraction method using a guided Gabor filter according to an embodiment of the present invention.
  • FIG. 2 illustrates a step of obtaining a finger vein image of FIG. 1.
  • FIG. 3 illustrates an embodiment of the finger vein image acquisition step of FIG. 2.
  • FIG. 4 illustrates another embodiment of the finger vein image acquisition step of FIG. 2.
  • FIG 5 shows an example of image quality improvement performance of the guided filter according to the guide image and the window size and the edge retention coefficient.
  • FIG. 6 shows the finger vein pattern filtered in eight Gabor filter directions.
  • FIG. 7 shows the performance of a Gabor filter in accordance with the present invention.
  • FIG 8 shows the finger vein pattern extraction performance according to the finger vein pattern extraction method of the present invention.
  • FIG. 10 shows a finger vein pattern extraction apparatus according to an embodiment of the present invention.
  • FIG. 11 shows an example of a user authentication system according to the present invention.
  • FIG. 1 illustrates a finger vein pattern extraction method using a guided Gabor filter according to an embodiment of the present invention.
  • the finger vein pattern extraction method using a guided Gabor filter first obtains the finger vein image (S100).
  • the finger vein image may be obtained by using an image capture device or an image scan device that captures a finger vein image by scanning a user's finger. Can also be obtained. If necessary, a preprocessing operation may be performed on the acquired finger vein image. Detailed description of the pretreatment will be described later.
  • the guided filter improves the image quality of the finger vein image according to a preset guidance image.
  • the guide image serves as a director to protect vein patterns and to reduce the effects of background effects such as haze, light variations, physical ambient conditions and changes in blood flow.
  • the Gabor filter allows the vein pattern to be easily extracted from the vein image by the guided filter so that the vein and the background can be easily distinguished.
  • the finger vein pattern extraction method applies a Gabor filter together with a guided filter to the acquired finger vein image so that the vein pattern in the finger vein image is clearly distinguished from the background. To facilitate the recognition of finger vein patterns.
  • FIG. 2 shows a step of acquiring the finger vein image of FIG. 1
  • FIG. 3 shows one embodiment of the step of acquiring the finger vein image of FIG. 2.
  • the obtaining of the finger vein image is performed.
  • the step of obtaining the finger vein image acquires an input image as shown in FIG. 3A (S110).
  • the input image a is an image including a finger vein pattern for at least one finger.
  • the input image a may be obtained using an image capture device such as an infrared camera so that the finger vein pattern may be displayed.
  • the preprocessing is preferably performed to facilitate the extraction of the finger vein pattern.
  • the finger vein image extraction method using the guided Gabor filter according to the present invention although the pretreatment is not performed, the finger vein pattern can be extracted more clearly than the conventional technique, but in order to extract the finger vein pattern more clearly, the pretreatment should be performed. . Whether or not to perform the preprocessing may be set in advance in the finger vein pattern extraction apparatus.
  • a finger profile is extracted from the input image a (S130). Finger contour extraction may be performed by applying a top-hat filter or a Sobel filter to the input image.
  • the top hat filter is a filter that converts each point of an image to a binary value of 0 or 1 based on a predetermined boundary value.
  • the Sobel filter is a nonlinear filter that finds the difference between sums of pixels at both ends in an image and then averages the horizontal and vertical directions to emphasize the boundary. Both the top-hat filter and the Sobel filter are filters commonly used for image processing to extract a boundary line from an image, and thus a detailed description thereof will be omitted.
  • the finger outline is extracted in the form of an edge image as shown in (b) by the boundary line extracted from the input image (a).
  • the edge image b may be regarded as being composed of a set of a plurality of edge points.
  • the finger outline is localized (S140). Localization of the finger outline is performed using a Hough transform.
  • the finger contour can be estimated as a line of small curvature, and the Hough transform is used to detect the position and angle of the finger contour.
  • Hough transform is generally a function used to extract a straight line from an image, but a function that can also extract a circle or a curve is mainly used in image processing, and thus a detailed description thereof will be omitted.
  • Equation 1 The set of edge points ⁇ (x 1 , y 1 ), (x 2 , y 2 ) ... (x k , y k ) ⁇ extracted from the edge image (b) is equation 1, which is an equation of the Hough transform. It can be converted into a sine curve on the polar plane ( ⁇ , ⁇ ) (where ⁇ ⁇ 0, 0 ⁇ ⁇ ⁇ ⁇ ).
  • the set of edge points ⁇ (x 1 , y 1 ), (x 2 , y 2 ) ... (x k , y k ) ⁇ is transformed into a polar coordinate system by Hough transform, accumulates, and If there is at least one peak in the set of accumulated edge points, this is evidence that there is at least one straight line corresponding to the edge image, depending on the nature of the Hough transform.
  • (c) represents two peaks (( ⁇ 1 , ⁇ 1 ), ( ⁇ 2 , ⁇ 2 ) extracted from a set of cumulative edge points from which the edge image (b) is Hough transformed, and the two peaks (( ⁇ ) 1 , ⁇ 1 ), ( ⁇ 2 , ⁇ 2 )) respectively correspond to the finger contour line.
  • (d) indicated two straight lines corresponding to two peaks (( ⁇ 1 , ⁇ 1 ), ( ⁇ 2 , ⁇ 2 )) on the input image a in blue.
  • Equation 2 If the extracted two peaks (( ⁇ 1 , ⁇ 1 ), ( ⁇ 2 , ⁇ 2 ) do not satisfy Equation 2, the input image is not rotated, but the extracted two peaks (( ⁇ 1 , When ⁇ 1 ), ⁇ 2 , ⁇ 2 ) satisfy Equation 2, the input image a is rotated at an angle of ( ⁇ 1 + ⁇ 2 ) / 2 (S160).
  • FIG. 4 illustrates another embodiment of the finger vein image acquisition step of FIG. 2.
  • the input image a does not have a horizontal direction. Since two peaks (( ⁇ 1 , ⁇ 1 ), ( ⁇ 2 , ⁇ 2 ) extracted by the Hough transform shown in (b) satisfy the condition of Equation 2, as shown in (c) Likewise, it is rotated at an angle of ( ⁇ 1 + ⁇ 2 ) / 2 and converted into a rotationally corrected image.
  • a region of interest is set in the rotation correction image c (S170).
  • the region of interest is an area set for extracting the finger vein pattern in the finger outline, as shown in (d).
  • the size of the ROI is set to, for example, [256, 96].
  • FIG. 4 illustrates a region of interest obtained by separating the set region of interest from the rotational correction image.
  • the guided filter is that the guide image (I) and the filter output (Gu) consist of a local linear model relationship. Therefore, in the present invention, it is assumed that the guided image G u filtered by the guided filter is a linear transformation of the guide image I in the window w k centered on the pixel k, as shown in Equation 3 below. I can express it.
  • I is a guide image
  • G u is a guided image filtered by the guided filter
  • w k is a window centered on the pixel k.
  • a k and b k are linear coefficients.
  • Equation 4 a cost function that minimizes the difference between the input and the output is calculated in Equation 4 in each window.
  • ⁇ is a normalization parameter to prevent the linear coefficient a k from growing.
  • Equation 4 is given according to linear regression according to "Draper, N., Smith, H, Applied Regression Analysis, 2 edn., John Wiley (1981)".
  • ⁇ k and ⁇ k 2 are the mean and variance values of the guide image I in the window ⁇ k , respectively.
  • Equations 5 and 6 are equations for calculating linear coefficients a k and b k according to Equation 4.
  • Equation 7 the guided image G u is calculated as in Equation 7.
  • the linear coefficient a k is a linear edge retention coefficient and the linear edge retention coefficient a k decreases as the normalization parameter ⁇ increases.
  • the normalization parameter ⁇ can be seen as an edge preserving coefficient that indicates the degree of edge retention on the guided image G u .
  • FIG 5 shows an example of image quality improvement performance of the guided filter according to the guide image and the window size and the edge retention coefficient.
  • columns 1 of (a) and (b) represent input images, and input images of (a) and (b) are identical images.
  • Column 2 is a guide image and (a) and (b) use different guide images.
  • Columns 3 to 6 represent guided images whose image quality is improved by the guided filter when the window size ⁇ and the edge retention coefficient ⁇ are changed, respectively. As shown in FIG. 5, it can be seen that the guided filter is effective in removing blur and improving image quality of the vein image by selecting an appropriate guide image.
  • Equation (8) The relation between the guide image I, the input image p of the guide filter, and the guided image G u may be expressed as shown in Equation (8).
  • Equation 8 the kernel weight of Equation 8 based on Equations 3 to 7 may be expressed as Equation 9.
  • the Gabor filter makes it possible to extract the energy of the local frequency band according to a specific scale ( ⁇ ) and orientation ( ⁇ k ) from an object extracted by a useful spectral decomposition method. Therefore, it is widely used to analyze texture information by expressing features according to scale and direction of objects as vectors.
  • Gabor filters are divided into even symmetric Gabor filters used for ridge extraction and odd symmetric Gabor filters used for edge extraction.
  • the Gabor filter is applied to extract ridges on the guided image filtered by the guided filter. Therefore, a random symmetric Gabor filter is applied to the guarded image.
  • the even symmetric guided gabor filter of the present invention may be represented as a combined form of an even symmetric Gabor filter and a guided filter, as shown in Equation (10).
  • the direction ( ⁇ k ) of the Gabor filter is set in eight directions. Set to. Since the direction of the Gabor filter ( ⁇ k ) is set in eight directions, the direction of the Gabor filter ( ⁇ k ) is 0 °, 22,5 °, 45 °, 67.5 °, 90 °, 112.5 °, 135 °, 157.5 ° Is set to. However, the number of Gabor filter directions ( ⁇ k ) can be adjusted.
  • the filtered images in eight Gabor filter directions ⁇ k are stored in the guided Gabor filter storage.
  • FIG. 6 shows the finger vein pattern filtered in eight Gabor filter directions.
  • Direction ( ⁇ k) direction ( ⁇ k) of the Gabor represents a specified filtered vein image
  • each filter in accordance with the Gabor filter top of the image is set to eight, respectively in Fig. 6 (a) to (h) is 0 °, 22
  • the case of 5 degrees, 45 degrees, 67.5 degrees, 90 degrees, 112.5 degrees, 135 degrees, and 157.5 degrees is shown.
  • the lower image represents average absolute deviations (AADs) corresponding to the upper image.
  • FIG. 7 shows the performance of a Gabor filter in accordance with the present invention.
  • an image disposed at the top is an original image and an image converted by a conventional image conversion technique, (a) an original image, and (b) an image converted by a global histogram.
  • c) represents an image converted by a local histogram blocked with a size of [32, 16].
  • the bottom image represents an image to which a Gabor filter is applied to the corresponding top image, respectively.
  • the performance of the image filtered by the guided filter is preserved when the Gabor filter is applied to the image filtered by the guided filter.
  • the image g filtered by the guide filter using the augmented GUID image includes noise that is not suitable for performing matching of the acquired finger vein pattern image.
  • the parameters of the guided filter (edge retention coefficient ⁇ and window size ⁇ ) may be adjusted according to the acquired state of the image.
  • the present invention is directed to the extraction of finger vein pattern using a guided Gabor filter, but the substantially extracted finger vein pattern is generally used for user authentication, and user authentication requires image matching.
  • image matching the feature of the finger vein pattern is extracted and the image matching operation is performed on the feature of the extracted finger vein pattern.
  • the filtered image may be divided into a plurality of small blocks.
  • the filtered image is divided into 16 ⁇ 16 small blocks.
  • an 8-dimensional vector based on statistical information may be constructed instead of the pixel-based vector.
  • N is the number of block matrix (F mn ) pixels at, and ⁇ k mn is It is shown in Figure 6 as the average of the size of.
  • image matching may be performed using a support vector machine (SVM) classifier, which is a kind of supervised learning that can be applied to classification and regression as one of classification algorithms.
  • SVM classifiers according to the present invention are each for image matching It is possible to obtain 768 [96 ⁇ 8] feature vectors represented by. Each feature vector V may be calculated as shown in Equation 13.
  • the calculated feature vector V is used in image matching for user authentication.
  • a test was performed using a database in which 106 open finger vein data were stored.
  • images of the two-handed index finger, middle finger, and ring finger of each 106 people were acquired six times, and a total of 3,816 finger vein images were stored.
  • the pixel size of each image was stored as 320 x 240.
  • FIG 8 shows the finger vein pattern extraction performance according to the finger vein pattern extraction method of the present invention.
  • FIG. 8 shows three kinds of low quality images were tested by four finger vein extraction methods.
  • (a) shows a maximum curvature method
  • (b) shows a wide line tracking method
  • (c) shows a finger vein pattern extracted by a Gabor filter.
  • (d) shows the finger vein pattern extracted by the guided Gabor filter according to the present invention.
  • Images 1 and 2 are low contrast images at light and dark intensity, respectively, and image 3 is an image affected by illumination.
  • the bonded Gabor filter effectively extracts clear vein patterns without being affected by the thickness and brightness of the vein image.
  • the finger vein pattern extraction method according to the other guided Gabor filter according to the present invention does not require classification or classification of images, and the pretreatment time for one image takes 0.122 seconds, which is much higher than that of other finger vein extraction methods. You can see it works quickly. That is, user authentication can be performed in real time.
  • the 768 feature vectors extracted above were evaluated for matching performance.
  • the finger vein data was divided into six groups for training and testing, and applied in the form of (the number of training groups, the number of test groups) to train the matching system.
  • Cosine-like measurements and SVM were used as classifiers, and the performance between the Gabor and Guarded Gabor filters was evaluated.
  • SVM Simple Vector Machines
  • the guided gabor filter according to the present invention makes the guided filter clear the edge contour of the vein by using a guide image.
  • the Gabor filter clarifies the ridges in the guided image filtered by the guided filter, making it easier to distinguish between the background and the finger vein pattern.
  • the vein profile is extracted from eight directions.
  • the guided Gabor filter according to the present invention do not require image segmentation, easy haze removal of the image, easy to protect the edge and slope of the finger vein. It also enables high speed operation and obtains clear finger vein contours with low noise. In addition, the image quality deterioration is low even for low quality images.
  • FIG. 10 shows a finger vein pattern extraction apparatus according to an embodiment of the present invention.
  • the finger vein pattern extracting apparatus 10 extracts the finger vein pattern using the guided Gabor filter.
  • the finger vein pattern extraction apparatus 10 includes an image acquisition unit 110, a preprocessor 120, a guided filter unit 130, a Gabor filter unit 140, a quality estimation unit 150, and features.
  • An extraction unit 160 is provided.
  • the image acquisition unit 110 may capture a vein image of the user, and may be implemented as an image capturing means such as a camera including an image sensor (or an infrared sensor).
  • the vein image should be obtained, and since the vein is darkened by absorbing infrared light as described above, it is preferable that the vein is implemented by an infrared lamp, an infrared camera, or an infrared sensor.
  • the image acquisition unit 110 may be omitted.
  • the preprocessor 120 performs a preprocessing operation on the vein image acquired by the image acquirer 110.
  • the preprocessor 120 may perform operations such as image rotation and ROI setting as preprocessing operations to facilitate image matching.
  • the preprocessing unit 120 is a configuration necessary for ease of image matching, and may be omitted in some cases.
  • the image acquisition unit 110 and the preprocessor 120 are divided and illustrated for convenience of description, but the preprocessor 120 may be implemented by being included in the image acquisition unit 110.
  • the guided filter unit 130 receives the vein image from the preprocessor if the preprocessor 120 is present, and receives the vein image from the image acquisition unit 110 if the preprocessor 120 does not exist. Perform a filtering operation. Guided filtering uses a guide image to protect vein patterns in the vein image and improves the quality of the vein image by reducing the effects of background effects such as blur, lighting, ambient conditions, and changes in blood flow.
  • the Gabor filter 140 receives the guided filtered image from the guided filter unit 130, and easily extracts the ridge line of the vein pattern from the guided image to clarify the distinction between the vein pattern and the background.
  • the quality estimator 150 receives the filtered vein image from the Gabor filter 140 and determines whether the quality of the received vein image is greater than or equal to a predetermined reference quality, and transmits the vein image to the feature extractor 160 if the quality is higher than the reference quality. do. Since the quality estimator 150 estimates the quality of an image, there are various known methods, and thus, a detailed description thereof will be omitted.
  • the feature extractor 160 receives the vein image from the quality estimator 150 and extracts a feature of the received vein image.
  • FIG. 11 shows an example of a user authentication system according to the present invention.
  • the user authentication system includes a vein pattern extraction device 10, an interface device 20, and a database 30. Since the vein pattern extraction apparatus 10 is shown in FIG. 10, a detailed description thereof will be omitted.
  • the interface device 20 may be implemented as at least one of a display device and an acoustic device. If the vein image received by the quality estimator 150 of the vein pattern extraction apparatus 10 is determined to be an image having a lower quality than the reference quality, The interface device 20 requests the user to rescan the vein image through the image acquisition unit 110.
  • the database 30 stores the vein image obtained by the vein pattern extraction apparatus 10 when the user is registered and filtered to extract features. Subsequently, upon requesting a user's seat, the vein pattern extracting apparatus 10 searches for the stored vein image matching the characteristic of the obtained vein image and notifies the interface apparatus 10 of the matching search result.
  • the interface device 10 displays the matching search result to the user.
  • an additional locking device may be provided.
  • the locking device may be configured to be unlocked if a matching search result from the database 30 determines that a matching vein pattern exists.
  • the present invention can also be used for images other than fingers. That is, in the present invention, the input image may be not only a finger image but also an image of another body part such as a back of a hand or a wrist, not a hand or a wrist. That is, the present invention is not limited to the finger vein pattern but may be applied to an image including all kinds of vein patterns that can be identified by the user.
  • the method according to the invention can be embodied as computer readable code on a computer readable recording medium.
  • the computer-readable recording medium includes all kinds of recording devices in which data that can be read by a computer system is stored. Examples of the recording medium include a ROM, a RAM, a CD-ROM, a magnetic tape, a floppy disk, an optical data storage device, and the like, and also include a carrier wave (for example, transmission through the Internet).
  • the computer readable recording medium can also be distributed over network coupled computer systems so that the computer readable code is stored and executed in a distributed fashion.

Abstract

L'invention concerne un procédé et un dispositif d'extraction de motifs de veines de doigt à l'aide d'un filtre de Gabor guidé. Le dispositif d'extraction de motifs de veines de doigt, selon la présente invention, comporte : une unité de traitement préalable qui permet d'obtenir une image capturée comprenant un motif de veines d'un utilisateur à l'aide de la lumière dans le proche infrarouge, et qui définit une région d'intérêt qui comprend une veine de doigt dans l'image capturée ; une unité de filtre guidé qui effectue un filtrage guidé pour l'image capturée obtenue dans une unité d'obtention d'image à l'aide d'une image-guide prédéfinie, et qui génère une image guidée ayant une meilleure qualité d'image que l'image capturée ; une unité de filtre de Gabor qui effectue, par rapport à l'image guidée, le filtrage de Gabor afin d'extraire la forme d'une crête à partir de l'image guidée, et qui génère une image de filtrage. Ainsi, il est possible d'extraire à grande vitesse un motif de veines de doigt très précis en utilisant une capacité d'amélioration de la qualité d'image du filtre guidé et une capacité d'extraction de crête du filtre de Gabor.
PCT/KR2012/008410 2012-07-25 2012-10-16 Procédé et dispositif d'extraction de motifs de veines de doigt à l'aide d'un filtre de gabor guidé WO2014017697A1 (fr)

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