WO2005008568A1 - Method for acquiring a fingerprint image by sliding and rolling a finger - Google Patents

Method for acquiring a fingerprint image by sliding and rolling a finger Download PDF

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Publication number
WO2005008568A1
WO2005008568A1 PCT/KR2004/001794 KR2004001794W WO2005008568A1 WO 2005008568 A1 WO2005008568 A1 WO 2005008568A1 KR 2004001794 W KR2004001794 W KR 2004001794W WO 2005008568 A1 WO2005008568 A1 WO 2005008568A1
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WIPO (PCT)
Prior art keywords
fingerprint
image
images
sensor
finger
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PCT/KR2004/001794
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French (fr)
Inventor
Jai-Hie Kim
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Yonsei University
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Publication of WO2005008568A1 publication Critical patent/WO2005008568A1/en

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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
    • G06V40/12—Fingerprints or palmprints
    • G06V40/1335—Combining adjacent partial images (e.g. slices) to create a composite input or reference pattern; Tracking a sweeping finger movement
    • 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
    • G06V10/16—Image acquisition using multiple overlapping images; Image stitching

Definitions

  • This invention relates to a fingerprint authentication system with a small sensor. More specifically, the invention relates to a fingerprint enrollment method for acquiring sequential fingerprint images by rolling and sliding a finger on a small sensor, and makes a wide fingerprint image by mosaicking the acquired images.
  • Background Art
  • Fingerprint-based verification systems are provided widely because they are convenient to use and relatively superior to other biometrics systems with respect to the price and performance.
  • a small sensor e.g, solid-state sensors
  • the physical limitation - e.g, size of the sensor - probably results in lack of information about the fingerprint. Therefore the relatively small amount of common region between the template and query impressions results in degraded performance, like a higher rate of false rejects and/or false accepts.
  • An example of the overlapped region between template and query impressions is illustrated in FIG. 1.
  • Fingermatrix, Inc. (US. Pat. No. 4,553,837) invented the device shown FIG. 2 which can acquire a whole fingerprint image.
  • the device illustrated in FIG. 2 rotates a scanner cylindrically around a fingerprint and captures a whole fingerprint image.
  • Cross Match Technologies (US. Pat. No. 6,483,932) invented a rolled fingerprint scanner illustrated in FIG 3.
  • a rolled fingerprint scanner captures image sequences which are acquired by rolling a finger on a large-sized flat sensor and mosaic them for a whole fingerprint image.
  • the device of Cross Match Technologies requires the large senor so the device is mainly used in the specific application like AFB.
  • some researchers eg,, A.K Jain, D.J Lee, et al
  • the conventional devices need several appended devices or a large sensor which can cover the whole fingerprint region so the size of the system becomes bigger and the cost becomes very high too.
  • some researchers have tried to get a wide fingerprint image from several partial fingerprint images captured with a small sensor. Disclosure of Invention Technical Problem
  • the present invention is related to the fingerprint authentication system with a small sensor.
  • the system realized by this invention guides a user to roll his or her finger on the sensor and also slide his/her finger simultaneously, which makes the system obtain a wide area of fingerprint stably even with a small sensor.
  • the invention selects several images among the total acquired images based on the quality check algorithm, and then mosaics the images in the temporal order.
  • the invention estimates the global alignment parameters which align tvvo images coarsely by matching the image blocks between tvvo images and finds the corresponding blocks again between tvvo images hierarchically by using the global alignment parameters.
  • the invention regards the difference between the transformation parameters of each local block and the global transformation parameters as the local deformation and to compensate for the deformation warps tvvo images by 2-pass mesh warping
  • the invention assigns the gray value in the warped image weighted by the coherence of images.
  • FIG. 1 shows the small common area between a query and template image
  • FIG 2. shows the device illustrated in US. Pat. No. 4,553,837;
  • FIG 3. shows the device illustrated in US. Pat. No. 6,483,932;
  • FIG. 4 is a block diagram of this invention.
  • FIG. 5 is a flow chart showing the image mosaicking and feature extraction block in FIG. 4 in detail;
  • FIG. 6 is a flow chart of the image acquisition part shown in FIG. 5;
  • FIG. 7 shows that a user rolls and slides his finger horizontally on the sensor
  • FIG. 8 are the sequential images captured by the enrollment method shown in FIG. 7;
  • FIG. 9 is a mosaicked image with the images shown in FIG. 8;
  • FIG. 10 shows that a user slides his finger vertically on the sensor
  • FIG. 11 are the sequential images captured by the enrollment method shown in FIG. 10;
  • FIG. 12 is a mosaicked image with the images shown in FIG. 11;
  • FIG. 13 shows that a user rolls and slides his finger in arbitrary direction on the sensor;
  • FIG. 14 shows the positions of the finger against the sensor enrolled by the method shown in FIG. 13;
  • FIG. 15 are the sequential images captured by the enrollment method shown in FIG. 13;
  • FIG. 16 is a mosaicked image with the images shown in FIG. 15;
  • FIG. 17 is a flow chart which explains the image mosaicking and deformation compensating process for tvvo images;
  • FIG. 18 shows tvvo enrolled fingerprint images shown in FIG. 17;
  • FIG. 19 is a coarsely aligned image after being processed in 1020 blocks in FIG. 17;
  • FIG. 20 is an example image which is divided into several blocks;
  • FIG. 21 is a mosaicked image after taking all procedure shown in FIG. 17. Best Mode [33] FIG. 1 explains that the common area between a query and template image is so small, because of the small sensor, that the performance can be deteriorated. [34] FIG. 4 is a general flow chart of this invention.
  • the fingerprint sensor 410 captures the sequential images enrolled by rolling and sliding a finger on the sensor by a user. A wide mosaicked image is constructed from the captured images and features are extracted from the mosaicked image in the block 420. The extracted features are stored in the database 430.
  • FIG. 5 shows the detail of the block 420 in FIG. 4.
  • the image acquisition block 510 selects good quality images from the sequential images enrolled by rolling and sliding a finger on the sensor 410 by the user, and guides a user to enroll his fingerprint correctly.
  • the image mosaicking block 520 makes the captured images a wide mosaicked image. When mosaicking the captured images, the deformation of the mosaicked image caused by finger's motion on the sensor is compensated in the process 530.
  • the feature extraction process 540 extracts feature vectors from the mosaicked image and store the feature vectors in the database 430.
  • FIG. 6 explains the image acquisition block 510 in detail.
  • the system checks the existence of the fingerprint on the fingerprint sensor 410. If the fingerprint exists, the fingerprint image is stored into the temporary buffer. That is, all the images, captured during from putting a finger on the sensor to taking it off the sensor, are stored in the temporary buffer. If the number of the images stored in the buffer is over N, the system doesn't capture any fingerprint image and checks the qualities of the images. If the quality of the images satisfies the system criterion, the system executes the image mosaicking process 520 with these images otherwise the system requires a user to reenroll his or her fingerprint.
  • FIG. 7 shows an enrollment method according to this invention.
  • a user rolls his or her finger on the sensor and at the same time, slides his or her finger to prevent his or her finger off the sensor.
  • FIG. 8 is the fingerprint images captured sequentially by the enrollment method shown in FIG. 7. Since the captured fingerprint images (See FIG. 8) covers the horizontal region of a finger, if the images are mosaicked like FIG. 9, the system can acquire the wide fingerprint image with a very small sensor.
  • FIG. 10 shows another enrollment method according to this invention.
  • a user slides his finger vertically on the sensor by a user.
  • FIG. 11 are the fingerprint images captured sequentially by the enrollment method shown in FIG. 10.
  • the captured fingerprint images (See FIG. 11) covers the vertical region of a finger.
  • FIG. 12 is the mosaicked image with the images (See FIG. 11).
  • the system guides a user to roll his finger horizontally and slide the finger vertically on the sensor like that in FIG. 13.
  • FIG. 14 shows the position of a finger against the sensor by rolling and sliding a finger in the vertical and horizontal directions.
  • FIG. 15 is the sequential images captured by the enrollment method shown in FIG. 13.
  • FIG. 15 covers most part of a fingerprint so that the mosaicked image with the sequential images shown in FIG. 16 can represent the whole fingerprint.
  • the enrollment schemes illustrated in FIG. 7, FIG. 10 and FIG. 13 can acquire sequential images which may cover most parts of a fingerprint and the images are highly correlated each other so that it makes the system mosaic the images easily.
  • FIG. 17 is a flow chart which explains how to make a mosaicked image with the sequential images.
  • the each image is normalized with respect to the mean and variance of the intensity value of them to be the same and tvvo images are then aligned coarsely with a global alignment parameter calculated by the normalized cross-correlation between tvvo images. Since, in coarse alignment process, the common area between tvvo images can be calculated roughly, the system tries to align the common area more precisely and compensate for the local deformation in the next steps.
  • the common area of single image is divided into several blocks and each block is used to find the corresponding block in the common area of the other image hierarchically in the block matching procedure 1040.
  • the single image is warped to the other image by 2-pass mesh warping with the corresponding points which are the centers of the corresponding blocks.
  • the gray value of the common area is assigned with the weighted sum of the gray value from each image according to the quality of each image.
  • FIG. 11 shows the result images acquired from the procedure illustrated in FIG. 17.
  • FIG. 18 are tvvo example images enrolled by our enrollment scheme.
  • FIG. 19 is the coarsely aligned image after the step 1020.
  • FIG. 20 shows that the common area of the single image is divided into several blocks in the step 1030.
  • FIG. 21 is the final mosaicked image after processing the all steps illustrated in FIG. 17.

Abstract

This invention provides stably capturing strategy of several sequential fingerprint images and their stitching method, which eventually broaden range of enrolling image and prospects for increasing the verification performance of the fingerprint authentication system especially equipped with a small-sized sensor. The whole process consists of 4 parts. First, the fingerprint enrolling procedure captures several sequential images caused by rolling and sliding a finger on a sensor. Second procedure registers several images to make one mosaicked image. It produces a wide region of a fingerprint even with a small-sized sensor. The third compensates for the deformation of the mosaicked image caused by finger's motion on the sensor and the last extracts features from the mosaicked image and stores them into the database.

Description

Description METHOD FOR ACQUIRING A FINGERPRINT IMAGE BY SLIDING AND ROLLING A FINGER Technical Field
[1] This invention relates to a fingerprint authentication system with a small sensor. More specifically, the invention relates to a fingerprint enrollment method for acquiring sequential fingerprint images by rolling and sliding a finger on a small sensor, and makes a wide fingerprint image by mosaicking the acquired images. Background Art
[2] Fingerprint-based verification systems are provided widely because they are convenient to use and relatively superior to other biometrics systems with respect to the price and performance. Especially, a small sensor (e.g, solid-state sensors) has the advantage, such that it can be applied to many appliances (e.g, laptop computers, cellular phones). However, the physical limitation - e.g, size of the sensor - probably results in lack of information about the fingerprint. Therefore the relatively small amount of common region between the template and query impressions results in degraded performance, like a higher rate of false rejects and/or false accepts. An example of the overlapped region between template and query impressions is illustrated in FIG. 1.
[3] To overcome this problem, Fingermatrix, Inc. (US. Pat. No. 4,553,837) invented the device shown FIG. 2 which can acquire a whole fingerprint image. The device illustrated in FIG. 2 rotates a scanner cylindrically around a fingerprint and captures a whole fingerprint image. Cross Match Technologies (US. Pat. No. 6,483,932) invented a rolled fingerprint scanner illustrated in FIG 3. Unlike the device in FIG. 2, instead of rotating the scanner, a rolled fingerprint scanner captures image sequences which are acquired by rolling a finger on a large-sized flat sensor and mosaic them for a whole fingerprint image.
[4] However the device of Cross Match Technologies requires the large senor so the device is mainly used in the specific application like AFB. In order to acquire a wide fingerprint image with a small sized sensor, some researchers (eg,, A.K Jain, D.J Lee, et al) have studied about the mosaicking method which registers several partial fingerprint images to get a wide fingerprint image. In order to capture a whole fingerprint image, the conventional devices need several appended devices or a large sensor which can cover the whole fingerprint region so the size of the system becomes bigger and the cost becomes very high too. Also some researchers have tried to get a wide fingerprint image from several partial fingerprint images captured with a small sensor. Disclosure of Invention Technical Problem
[5] However the conventional devices collect fingerprint images by dabbing a finger on the sensor so that the collected fingerprint images can be too correlated or un- correlated. If the images are too correlated, there is no gain in integrating the images into single fingerprint image. Otherwise, it is very difficult to integrating the images. Technical Solution
[6] The present invention is related to the fingerprint authentication system with a small sensor. The system realized by this invention guides a user to roll his or her finger on the sensor and also slide his/her finger simultaneously, which makes the system obtain a wide area of fingerprint stably even with a small sensor.
[7] In addition, since temporally adjacent images among the acquired images are highly correlated, they can be easily registered. However, accurate registration is unavailable because physical friction when sliding a finger on the sensor can cause the local deformation, which degenerates quality of mosaicked images.
[8] To mosaic the images correctly, first of all, the invention selects several images among the total acquired images based on the quality check algorithm, and then mosaics the images in the temporal order. During the mosaicking process, the invention estimates the global alignment parameters which align tvvo images coarsely by matching the image blocks between tvvo images and finds the corresponding blocks again between tvvo images hierarchically by using the global alignment parameters.
[9] The invention regards the difference between the transformation parameters of each local block and the global transformation parameters as the local deformation and to compensate for the deformation warps tvvo images by 2-pass mesh warping
[10] Finally, the invention assigns the gray value in the warped image weighted by the coherence of images. Advantageous Effects
[11] As described above, in accordance with a fingerprint enrollment method, since the system realized by this invention guides a user to roll his or her finger on the sensor and also slide his/her finger simultaneously, there is a merit that the system obtain a wide area of fingerprint stably even with a small sensor. Additionally, since temporally adjacent images among the acquired images are highly correlated, they can be easily registered. Description of Drawings
[12] FIG. 1 shows the small common area between a query and template image;
[13] FIG 2. shows the device illustrated in US. Pat. No. 4,553,837;
[14] FIG 3. shows the device illustrated in US. Pat. No. 6,483,932;
[15] FIG. 4 is a block diagram of this invention;
[16] FIG. 5 is a flow chart showing the image mosaicking and feature extraction block in FIG. 4 in detail; [17] FIG. 6 is a flow chart of the image acquisition part shown in FIG. 5;
[18] FIG. 7 shows that a user rolls and slides his finger horizontally on the sensor;
[19] FIG. 8 are the sequential images captured by the enrollment method shown in FIG. 7; [20] FIG. 9 is a mosaicked image with the images shown in FIG. 8;
[21] FIG. 10 shows that a user slides his finger vertically on the sensor;
[22] FIG. 11 are the sequential images captured by the enrollment method shown in FIG. 10; [23] FIG. 12 is a mosaicked image with the images shown in FIG. 11;
[24] FIG. 13 shows that a user rolls and slides his finger in arbitrary direction on the sensor; [25] FIG. 14 shows the positions of the finger against the sensor enrolled by the method shown in FIG. 13; [26] FIG. 15 are the sequential images captured by the enrollment method shown in FIG. 13; [27] FIG. 16 is a mosaicked image with the images shown in FIG. 15;
[28] FIG. 17 is a flow chart which explains the image mosaicking and deformation compensating process for tvvo images; [29] FIG. 18 shows tvvo enrolled fingerprint images shown in FIG. 17;
[30] FIG. 19 is a coarsely aligned image after being processed in 1020 blocks in FIG. 17; [31] FIG. 20 is an example image which is divided into several blocks; and
[32] FIG. 21 is a mosaicked image after taking all procedure shown in FIG. 17. Best Mode [33] FIG. 1 explains that the common area between a query and template image is so small, because of the small sensor, that the performance can be deteriorated. [34] FIG. 4 is a general flow chart of this invention. The fingerprint sensor 410 captures the sequential images enrolled by rolling and sliding a finger on the sensor by a user. A wide mosaicked image is constructed from the captured images and features are extracted from the mosaicked image in the block 420. The extracted features are stored in the database 430.
[35] FIG. 5 shows the detail of the block 420 in FIG. 4. The image acquisition block 510 selects good quality images from the sequential images enrolled by rolling and sliding a finger on the sensor 410 by the user, and guides a user to enroll his fingerprint correctly. The image mosaicking block 520 makes the captured images a wide mosaicked image. When mosaicking the captured images, the deformation of the mosaicked image caused by finger's motion on the sensor is compensated in the process 530. The feature extraction process 540 extracts feature vectors from the mosaicked image and store the feature vectors in the database 430.
[36] FIG. 6 explains the image acquisition block 510 in detail. In the acquisition process, first of all, the system checks the existence of the fingerprint on the fingerprint sensor 410. If the fingerprint exists, the fingerprint image is stored into the temporary buffer. That is, all the images, captured during from putting a finger on the sensor to taking it off the sensor, are stored in the temporary buffer. If the number of the images stored in the buffer is over N, the system doesn't capture any fingerprint image and checks the qualities of the images. If the quality of the images satisfies the system criterion, the system executes the image mosaicking process 520 with these images otherwise the system requires a user to reenroll his or her fingerprint.
[37] FIG. 7 shows an enrollment method according to this invention. A user rolls his or her finger on the sensor and at the same time, slides his or her finger to prevent his or her finger off the sensor. FIG. 8 is the fingerprint images captured sequentially by the enrollment method shown in FIG. 7. Since the captured fingerprint images (See FIG. 8) covers the horizontal region of a finger, if the images are mosaicked like FIG. 9, the system can acquire the wide fingerprint image with a very small sensor.
[38] FIG. 10 shows another enrollment method according to this invention. A user slides his finger vertically on the sensor by a user. FIG. 11 are the fingerprint images captured sequentially by the enrollment method shown in FIG. 10. The captured fingerprint images (See FIG. 11) covers the vertical region of a finger. FIG. 12 is the mosaicked image with the images (See FIG. 11). To acquire the whole fingerprint image, the system guides a user to roll his finger horizontally and slide the finger vertically on the sensor like that in FIG. 13. FIG. 14 shows the position of a finger against the sensor by rolling and sliding a finger in the vertical and horizontal directions. FIG. 15 is the sequential images captured by the enrollment method shown in FIG. 13. FIG. 15 covers most part of a fingerprint so that the mosaicked image with the sequential images shown in FIG. 16 can represent the whole fingerprint. The enrollment schemes illustrated in FIG. 7, FIG. 10 and FIG. 13 can acquire sequential images which may cover most parts of a fingerprint and the images are highly correlated each other so that it makes the system mosaic the images easily.
[39] FIG. 17 is a flow chart which explains how to make a mosaicked image with the sequential images. For example, in order to stitch tvvo images, first, the each image is normalized with respect to the mean and variance of the intensity value of them to be the same and tvvo images are then aligned coarsely with a global alignment parameter calculated by the normalized cross-correlation between tvvo images. Since, in coarse alignment process, the common area between tvvo images can be calculated roughly, the system tries to align the common area more precisely and compensate for the local deformation in the next steps. In the step 1030, the common area of single image is divided into several blocks and each block is used to find the corresponding block in the common area of the other image hierarchically in the block matching procedure 1040. In the local deformation compensation step 1050, the single image is warped to the other image by 2-pass mesh warping with the corresponding points which are the centers of the corresponding blocks. Finally, the gray value of the common area is assigned with the weighted sum of the gray value from each image according to the quality of each image.
[40] FIG. 11 shows the result images acquired from the procedure illustrated in FIG. 17. FIG. 18 are tvvo example images enrolled by our enrollment scheme. FIG. 19 is the coarsely aligned image after the step 1020. FIG. 20 shows that the common area of the single image is divided into several blocks in the step 1030. FIG. 21 is the final mosaicked image after processing the all steps illustrated in FIG. 17.

Claims

Claims
[1] A fingerprint enrollment method for extracting feature vectors from sequential fingerprint images acquired by rolling and sliding a finger on a sensor and storing the extracted feature vectors into a database, comprising the steps of: a) capturing the sequential fingerprint images acquired by rolling and sliding the finger on the sensor; b) preprocessing input images and mosaicking the inputted images into a whole fingerprint image; c) fine tuning for a local deformation caused by a motion of the finger on the sensor; and d) extracting the feature vectors from a whole fingerprint image mosaicked in the step b) and storing the feature vectors into the database.
[2] The fingerprint enrollment method as claimed in claim 1, wherein the step a) comprises the sub-steps of: aa) checking the existence of the fingerprint on the sensor; ab) capturing a fingerprint image with a time interval between an n-th image and an n+l-th image and storing the fingerprint image at a temporary buffer; ac) checking a quality of fingerprint image stored in the temporary buffer; ad) requiring a user to reenroll if the quality of each fingerprint image is not good enough.
[3] The fingerprint enrollment method as claimed in claim 2, wherein, in the step a), a required number of fingerprint images is set to N, and the step a) is finished when the number of the acquired fingerprint images equals to N.
[4] The fingerprint enrollment method as claimed in claim 1, wherein, the step b) comprises the sub-steps of: ba) reducing a noise in the fingerprint image; bb) enhancing a contrast between valley and ridge; be) making a gray scale image a binary image; bd) thinning ridges whose width becomes single pixel;
[5] The fingerprint enrollment method as claimed in claim 1, wherein the step b) comprises the sub-steps of: be) estimating transformation parameters to align single image to other globally; and bf) representing that a common area of single image matches the common area of the other image finely. [6] The fingerprint enrollment method as claimed in claim 5, wherein the transformation parameter estimation transforms single image globally into the other to maximize a normalized cross-correlation of the common area between tvvo images. [7] The fingerprint enrollment method as claimed in claim 5, wherein the process making the common area between tvvo images matched finely, comprises the sub-steps of: i) dividing the common area of single image into several blocks; ii) finding corresponding blocks for the blocks of the single image in the other image; iii) finding optimal transformation parameters for each corresponding block; and iv) wrapping the common area of the single image to it of the other image, which is implemented when a compensating for the local deformation based on the transformation parameters of each block; [8] The fingerprint enrollment method as claimed in claim 7, wherein the compensation for the local deformation warps the single image to the other in terms of a control points located a center of corresponding blocks.
PCT/KR2004/001794 2003-07-18 2004-07-19 Method for acquiring a fingerprint image by sliding and rolling a finger WO2005008568A1 (en)

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KR1020030048985A KR100613697B1 (en) 2003-07-18 2003-07-18 Method for Acquire of Fingerprint Image by Sliding and Rolling
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