Biometric authentication system, biometric authentication method, and recording medium

A biometric authentication system (1) records, in a storage unit in an associating manner, position information of a feature area on an image of biometric information whose biometric authentication is successful and reference biometric information referred to in the successful biometric authentication, the feature area being specific to a living-body, and detects a feature area that is specific to a living-body from an image of biometric information of an input object to be authenticated, and narrows down the reference biometric information stored in the storage unit to reference biometric information to be compared with the biometric information of the object to be authenticated, based on the degree of similarity between each position information, and authenticates the biometric information of the object to be authenticated by comparing the reference biometric information obtained as a narrowing-down result and the biometric information of the object to be authenticated.

FIELD

The present invention relates to a biometric authentication system, a biometric authentication method, and a biometric authentication program.

BACKGROUND

Examples of a biometric authentication technique include “1:1 authentication” and “1:N authentication.” The 1:1 authentication refers to a method that performs authentication by comparing biometric information input by a user and biometric information registered in advance in association with the identification information of the user. The 1:N authentication refers to a method that performs authentication by comparing biometric information input by a user and the biometric information of N people registered in advance. When the 1:N authentication is employed, because the biometric data input by the user is compared with the biometric data of N people registered in advance, a lager registration number N of biometric data requires more time to output an authentication result.

Examples of a technique that reduces authentication time in the 1:N authentication include a fingerprint comparison apparatus that compares selection parameters representing feature quantities of fingerprints between an input fingerprint and registered fingerprints, thereby narrowing down the registered fingerprints to be compared with the input fingerprint. In this fingerprint comparison apparatus, the selection parameters are compared before comparison, which include the ratio of a ridge area with respect to the entire fingerprint area, the space between a ridge and a valley, and the number of endpoints and bifurcations that a ridge or valley of a fingerprint has.

However, the above-mentioned prior art is problematic in that the accuracy of narrowing down is not stable as will be described below.

Specifically, the above-mentioned fingerprint comparison apparatus performs narrowing down on the precondition that the fingerprint does not change for the same user between the time when the fingerprint was registered and the time when the fingerprint is used for authentication. It is difficult for the above-mentioned fingerprint comparison apparatus to cope with narrowing down when a temporary change occurs in a living body due to a scratch, chapping, or the like, because when the temporary change occurs in a fingerprint due to a scratch, chapping, or the like, a difference in a selection parameter to be compared between an input fingerprint and registered fingerprints increases even if the fingerprint is of the same user. In the above-mentioned fingerprint comparison apparatus, therefore, even when the registered fingerprints are narrowed down by comparing the selection parameters between the input fingerprint and the registered fingerprints, the registered fingerprint of the user is not always included in the registered fingerprints that are obtained as a narrowing-down result, and are compared with the input fingerprint.

SUMMARY

According to an aspect of the embodiment of the invention, a biometric authentication system includes a registering unit that records, in a storage unit in an associating manner, position information of a feature area on an image of biometric information whose biometric authentication is successful and reference biometric information referred to in the successful biometric authentication, the feature area being specific to a living-body. The biometric authentication system includes a detecting unit that detects a feature area that is specific to a living-body from an image of biometric information of an input object to be authenticated. The biometric authentication system includes a narrowing-down unit that narrows down the reference biometric information stored in the storage unit to reference biometric information to be compared with the biometric information of the object to be authenticated, based on position information of the feature area detected by the detecting unit and the position information of the feature area stored in the storage unit. The biometric authentication system includes an authenticating unit that authenticates the biometric information of the object to be authenticated by comparing the reference biometric information obtained as a narrowing-down result by the narrowing-down unit and the biometric information of the object to be authenticated.

DESCRIPTION OF EMBODIMENTS

The following describes embodiments of a biometric authentication system, a biometric authentication method, and a biometric authentication program disclosed by the present invention in detail with reference to the drawings. The embodiments do not limit the disclosed technique. The embodiments may be appropriately combined to the extent that the processing details do not contradict each other.

First Embodiment

System Configuration

Described first is a configuration of a biometric authentication system according to the present embodiment.FIG. 1is a diagram illustrating the configuration of a biometric authentication system according to the first embodiment. The biometric authentication system1illustrated inFIG. 1houses terminal devices10A to10C and an authentication server30. The example ofFIG. 1assumes a case in which 1:N fingerprint authentication is performed between fingerprint data of a user input from either one of the terminal devices10A to10C and a plurality pieces of fingerprint data registered in advance.

The terminal devices10A to10C and the authentication server30are connected by a network3in a communicable manner. As an embodiment of the network3, communication networks such as the Internet, a local area network (LAN), and a virtual private network (VPN) may be employed.

Although the example ofFIG. 1illustrates three terminal devices and one authentication server, the disclosed system is not limited to the illustrated configuration. Specifically, the biometric authentication system1only needs to house at least one each of the terminal device and the authentication server, and any number of terminal devices and authentication servers may be housed. When the terminal devices10A to10C are described below without being discriminated from each other, they may be represented as the terminal device10.

The terminal device10is an information processing unit that is used by a user. As an embodiment of the terminal device10, mobile terminals such as a cellular phone, a personal handyphone system (PHS), a personal digital assistant (PDA), and a smartphone may be employed as well as a stationary terminal such as a personal computer (PC). The following description will be made with an assumption that the user logs in to a PC.

To log in to the terminal device10, the terminal device10receives the inputting of a fingerprint image of a user in place of an account and a password and permits or prohibits a log-in by the user to the terminal device10in accordance with the result of fingerprint authentication performed by the authentication server30described below.

Specifically, the terminal device10generates fingerprint data to be used when fingerprint authentication is performed by the authentication server30described below, from an image obtained by reading a fingerprint by a fingerprint sensor (not illustrated). The image obtained by reading a fingerprint by the fingerprint sensor of the terminal device10may be referred to as the fingerprint image below. The fingerprint data generated from the fingerprint image may be referred to as the input fingerprint data, and the fingerprint data registered in advance in the authentication server30described below may be referred to as the registered fingerprint data below.

Furthermore, the terminal device10generates, from the fingerprint image read by the fingerprint sensor, narrowing-down data to be used to perform narrowing down to the registered fingerprint data to be compared with the input fingerprint data by the authentication server30described below. The narrowing-down data generated from the fingerprint image may be referred to as the input narrowing-down data, and the narrowing-down data registered in advance in the authentication server30described below may be referred to as the registered narrowing-down data below.

After that, the terminal device10transmits the fingerprint data and the narrowing-down data generated from the fingerprint image to the authentication server30described below. The terminal device10thus transmits the fingerprint data and the narrowing-down data generated from the fingerprint image instead of the fingerprint data itself to the network3, thereby preventing the fingerprint, which is a piece of personal information of the user, from leaking to the outside. Although a case is exemplified in which the terminal device10generates the fingerprint data and the narrowing-down data from the fingerprint image, a configuration may be employed in which the terminal device10transmits the fingerprint image to the authentication server30as it is and the authentication server30generates the fingerprint data and the narrowing-down data.

When an authentication result by the authentication server30described below is a success, the terminal device10permits a log-in by the user to the terminal device10. In other words, the terminal device10allows the user to log in thereto by automatically inputting an account name and a password. The security of identification of the user is thereby improved over password authentication, and risks that a password is forgotten or leaked are reduced.

In contrast, when the authentication result by the authentication server30described below is a failure, the terminal device10prohibits a log-in by the user to the terminal device10. In this case, the terminal device10may output a notification that prompts the user to input the finger image again, may give a warning of unsuccessful authorization to use the terminal device10, or may lock the operation of the terminal device10.

The authentication server30is a server device that provides a fingerprint authentication service. The authentication server30, upon reception of the input fingerprint data and the input narrowing-down data from the terminal device10, performs narrowing down to the registered fingerprint data to be compared with the input fingerprint data prior to fingerprint authentication, using the input narrowing-down data and the registered narrowing-down data. As an example, the authentication server30performs narrowing down to registered fingerprint data corresponding to registered narrowing-down data whose degree of narrowing-down similarity calculated from the input narrowing-down data and the registered narrowing-down data is within a predetermined ratio with respect to the total number N of pieces of the registered fingerprint data, for example, within the top one-tenth (=N/10). As another example, the authentication server30performs narrowing down to registered fingerprint data corresponding to registered narrowing-down data whose degree of narrowing-down similarity calculated from the input narrowing-down data and the registered narrowing-down data is not less than a predetermined threshold.

After that, the authentication server30compares the input fingerprint data and the registered fingerprint data obtained as a narrowing-down result, thereby performing authentication. As an example, the authentication server30repeats processing that determines whether the degree of comparison similarity calculated from the input fingerprint data and the registered fingerprint data is not less than a predetermined threshold until the input fingerprint data is compared with all pieces of registered fingerprint data obtained earlier as a narrowing-down result. When the degree of comparison similarity is not less than the threshold with respect to at least one piece of registered fingerprint data, the authentication server30replies to the terminal device10that the authentication of the input fingerprint data is successful. In contrast, when the degree of comparison similarity is less than the threshold with respect to all pieces of registered fingerprint data, the authentication server30replies to the terminal device10that the authentication of the input fingerprint data is unsuccessful.

The biometric authentication system1according to the present embodiment registers, in a storage unit in an associating manner, position information of an area in which a fingerprint-specific feature is unclear on a fingerprint image corresponding to the input fingerprint data whose fingerprint authentication is successful and the registered fingerprint data referred to in the successful fingerprint authentication. The biometric authentication system1according to the present embodiment then detects an area in which a fingerprint-specific feature is unclear from a fingerprint image corresponding to input fingerprint data that has been newly input. Furthermore, based on the degree of similarity between the position information of the area obtained as a detection result and the position information of the area stored in the storage unit, the biometric authentication system1according to the present embodiment narrows down the registered fingerprint data stored in the storage unit to registered fingerprint data to be compared with the input fingerprint data. Furthermore, the biometric authentication system1according to the present embodiment compares the registered fingerprint data obtained as a narrowing-down result with the input fingerprint data, thereby performing authentication.

Thus, even when the fingerprint of the user changes due to a scratch or chapping, the biometric authentication system1according to the present embodiment adds the position information of the area whose fingerprint-specific feature is unclear on the fingerprint image due to the scratch or chapping, to the registered fingerprint data as narrowing-down data once authentication is successful. As a result, when fingerprint data having a scratch or chapping is input in later authentication, the biometric authentication system1according to the present embodiment performs narrowing down to registered fingerprint data having position information similar to the position information of that area. The biometric authentication system1according to the present embodiment therefore has an increased probability that the registered fingerprint data of the user who inputs the fingerprint is included in the narrowing-down result.

The biometric authentication system1according to the present embodiment can therefore stabilize the accuracy of narrowing down. Furthermore, because the biometric authentication system1according to the present embodiment has an increased probability that the registered fingerprint data of the user who inputs the fingerprint is included in the narrowing-down result, it can prevent a failure in authentication following a failure in narrowing down and reduce an authentication time from the inputting of a fingerprint to the outputting of an authentication result.

Configuration of Terminal Device10

Described next is a configuration of a terminal device according to the present embodiment.FIG. 2is a block diagram illustrating the configuration of the devices included in the biometric authentication system according to the first embodiment. As illustrated inFIG. 2, this terminal device10includes a communication interface (I/F) unit11, a fingerprint sensor12, a fingerprint data generating unit13, a narrowing-down data generating unit14, and an operating system (OS) executing unit15. The terminal device10may have, in addition to the functional units illustrated inFIG. 2, various functional units such as various input devices and voice output devices included in a conventional PC.

The communication I/F unit11is an interface that controls communications with other devices such as the authentication server30. For example, the communication I/F unit11transmits fingerprint data generated by the fingerprint data generating unit13described below and narrowing-down data generated by the narrowing-down data generating unit14described below to the authentication server30and receives an authentication result from the authentication server30. As an embodiment of the communication I/F unit11, a network interface card (NIC) such as a LAN card or a modem may be employed.

The fingerprint sensor12is a sensor that reads a fingerprint. As an installation example of the fingerprint sensor12, an embodiment in which the fingerprint sensor12is incorporated into the terminal device10may be employed, or an embodiment in which it is connected to the terminal device10through a universal serial bus (USB) or the like may be employed. As an example of the detection method of the fingerprint sensor12, any detection method may be employed including a capacitance type, an electric-field detection type, an optical type, a thermosensitive type, and a pressure-sensitive type. An example of the reading method of the fingerprint sensor12may be of a slide type in which a finger pad, that is, the central part of a fingertip is slid or may be a stamp type in which a finger pad is placed.

The fingerprint data generating unit13is a processing unit that generates fingerprint data for use in fingerprint authentication from a fingerprint image. As an example, the fingerprint data generating unit13generates fingerprint data from a fingerprint image read by the fingerprint sensor12in accordance with any authentication method such as the minutiae method, the pattern matching method, and the frequency analysis method employed by an authenticating unit36of the authentication server30described below.

For example, when the minutiae method is employed by the authenticating unit36described below, the fingerprint data generating unit13generates the direction and position relation of feature points, such as endpoints and bifurcations of ridges, included in a pattern of the fingerprint image and the correlation between the feature points as fingerprint data. When the pattern matching method is employed by the authenticating unit36described below, the fingerprint data generating unit13generates an image in which the fingerprint image is binarized or thinned as fingerprint data. When the frequency analysis method is employed by the authenticating unit36described below, the fingerprint data generating unit13generates a waveform spectral series when a cross-section obtained by slicing a pattern of the fingerprint image is regarded as a waveform, as fingerprint data.

The narrowing-down data generating unit14is a processing unit that generates narrowing data for use in the narrowing down of the registered fingerprint data, from the fingerprint image. The narrowing-down data generating unit14includes, as illustrated inFIG. 2, a dividing unit14a, a first generating unit14b, a detecting unit14c, a second generating unit14d, and a correcting unit14e.

The dividing unit14ais a processing unit that divides the fingerprint image into blocks with a certain size. As an example, the dividing unit14asearches the fingerprint image read by the fingerprint sensor12, thereby detecting a reference point of the fingerprint, for example, a fingerprint center that is the center of a pattern (a whorl) forming the fingerprint. Based on the fingerprint center detected in the searching of the fingerprint image, the dividing unit14acuts an area targeted for later processing such as generation of narrowing-down data, out of the fingerprint image and divides the image of the area into blocks.

Described now is the cutting out of the fingerprint image.FIG. 3is a diagram for describing how a fingerprint image is cut out. As illustrated inFIG. 3, the dividing unit14asearches a fingerprint image20read by the fingerprint sensor12. Through the searching, the dividing unit14adetects a fingerprint center21that is the center of a pattern forming the fingerprint, from the fingerprint image20. The dividing unit14athen sets a cut-out area22of the fingerprint image20with the fingerprint center21in the fingerprint image20as the center or the gravity center. The dividing unit14asets a rectangle with a longitudinal direction23being the vertical direction with respect to the wrinkle of the first joint of the finger, that is, the fingertip direction as the cut-out area22. The dividing unit14asets the size of the cut-out area22to be such a size that the area can be divided into a certain number of blocks. The dividing unit14athen cuts the cut-out area22out of the fingerprint image20, thereby extracting a cut-out fingerprint image24.

The dividing unit14aperforms the cutting out of the fingerprint image20in accordance with reference information transmitted from the first generating unit14bdescribed below and the second generating unit14ddescribed below, for example, the type of a singular point as the reference point of the fingerprint or the size of the cut-out area22. It may be defined in advance in the dividing unit14athat the cutting out is performed by the same cutting-out method used when generating first narrowing-down data and second narrowing-down data registered in a storage unit32of the authentication server30.

After thus performing the cutting out of the fingerprint image, the dividing unit14adivides the fingerprint image into blocks with the number of pixels corresponding to a value that is about a few times, for example, double the representative size of the space between adjacent ridges in a finger of an adult as the size of a side of the blocks, so that the blocks each include part of a plurality of ridges. For example, when the representative value of the space between ridges in a finger of an adult is about 0.2 mm, the dividing unit14adivides the fingerprint image into blocks with the number of pixels corresponding to about 0.4 mm as the size of a side of the block. The dividing unit14aoutputs the fingerprint image divided into blocks to the first generating unit14bdescribed below and the detecting unit14cdescribed below.

The size of one side of the block above may be changed arbitrarily by a system administrator. As an example, when the generation of users is limited to a specific generation like in a school, the dividing unit14amay automatically set the number of pixels that constitute blocks based on the representative value of the space between ridges in a finger of the specific generation.

The first generating unit14bis a processing unit that generates the first narrowing-down data related to fingerprint-specific features using the fingerprint image divided into blocks by the dividing unit14a. The following description will be made with an assumption that the first generating unit14bgenerates narrowing-down data related to minutiae as the first narrowing-down data. As an example, the first generating unit14bcalculates, for each block of the fingerprint image, the widths of ridges detected within the block, the spaces between ridges detected within the block, the number of minutiae detected within the block, or the like. The widths of ridges, the space between ridges, and the number of minutiae thus calculated are transmitted as the first narrowing-down data to the authentication server30. The first narrowing-down data generated by the first generating unit14bmay be referred to as the first input narrowing-down data, and the first narrowing-down data registered in the authentication server30may be referred to as the first registered narrowing-down data below.

The detecting unit14cis a processing unit that detects an area in which a fingerprint-specific feature is unclear from the fingerprint image divided into blocks by the dividing unit14a. The area in which a fingerprint-specific feature is unclear on the fingerprint image, that is, an area of lower quality for performing narrowing down than an area in which a fingerprint-specific feature is clear may be referred to as a low-quality area below.

The detecting unit14cdetects the low-quality area on the fingerprint image by occurrence factor. Factors of the occurrence of the above-mentioned low-quality area are classified into scratch, crack, adhesion of a water droplet, chapping, and the like. The water droplet includes perspiration. The chapping includes peeling of skin, callous, and wear.FIG. 4AtoFIG. 4Dare diagrams for describing each classification of the low-quality area. The reference numeral40A illustrated in FIG.4A is given to a fingerprint image with no low-quality area. The reference numeral40B illustrated inFIG. 4Bis given to a fingerprint image in which a scratch is imaged. The reference numeral40C illustrated inFIG. 4Cis given to a fingerprint image in which a water droplet is imaged. The reference numeral40D illustrated inFIG. 4Dis given to a fingerprint image in which chapping is imaged. It is assumed that in the examples ofFIG. 4AtoFIG. 4Dthe fingerprints imaged in the fingerprint images40A to40D are the fingerprints of the same user.

As illustrated inFIG. 4A, because in the fingerprint image40A there is no low-quality area on the image, a valley41A appears whiter than a ridge42A, the ridge42A appears blacker than the valley41A, and the valley41A, the ridge42A, and the boundary therebetween appear clear. When the first narrowing-down data is generated from the fingerprint image40A, narrowing-down data with higher quality can be obtained than in a case in which the first narrowing-down data is generated from a fingerprint image having a low-quality area like the fingerprint images40B to40D illustrated inFIG. 4BtoFIG. 4D.

As illustrated inFIG. 4BtoFIG. 4D, a scratch41B, a water droplet41C, and chapping41D, which are factors that make a fingerprint-specific feature unclear, are present in the fingerprint images40B to40D, respectively. In the case of the fingerprint image40B in which the scratch41B is imaged, as illustrated inFIG. 4B, because a ridge that was present before the formation of the scratch is damaged and its projection is smaller after the damage of the ridge than before, the pixel value of the damaged part is higher than that of the ridge. In the case of the fingerprint image40C in which the water droplet41C is imaged, as illustrated inFIG. 4C, because the valley is filled with the water droplet41C, the pixel value of the valley is lower than before the valley was filled with the water droplet41C. In the case of the fingerprint image40D in which the chapping41D is imaged, as illustrated inFIG. 4D, because the ridge that was present before the occurrence of the chapping41D is damaged and its projection is smaller than before the damage of the ridge, the pixel value of the damaged part is higher than that of the ridge. Given these situations, owing to the scratch41B, the water droplet41C, and the chapping41D, it is difficult to accurately calculate the widths of ridges, the spaces between ridges, and how minutiae are formed using the first generating unit14b. When the first narrowing-down data is generated from the fingerprint image40B, the fingerprint image40C, and the fingerprint image40D, therefore, only narrowing-down data that provides higher frequency of a failure in the narrowing down to the person who inputs the fingerprint is obtained as compared to a case in which the first narrowing-down data is generated from the fingerprint image40A illustrated inFIG. 4A.

As an embodiment, the detecting unit14cmay employ the following technique as a technique that extracts a scratch and crack on a fingerprint image. Examples of the technique include “Marcelo de Almeida Oliveira, Neucimar Jeronimo Leite: ‘Reconnection of Fingerprint Ridges Based on Morphological Operators and Multiscale Directional Information’, SIBGRAPI'04, 122-129, 2004.” The detecting unit14cdetects a block overlapping an area in which a scratch or crack extracted from a fingerprint image in accordance with the above-mentioned technique is present as a low-quality area with a classification of “scratch (crack).”

UsingFIG. 5toFIG. 8, the following describes a method for detecting a low-quality area.FIG. 5is a diagram illustrating an example of a fingerprint image.FIG. 6is a diagram illustrating the identification numbers of blocks.FIG. 7is a diagram illustrating the fingerprint image divided into blocks.FIG. 8is a diagram illustrating a detection result of a low-quality area. The figures within the blocks illustrated inFIG. 6each indicate the identification number of a block. The black-filled part within the blocks illustrated inFIG. 8indicates the low-quality area.

As illustrated inFIG. 5, a fingerprint image50A in which a scratch51A is imaged is divided into 24 (=6 vertical by 4 horizontal) blocks with the identification numbers1to24by the dividing unit14a. When the fingerprint image50A is thus divided, as illustrated inFIG. 7, a fingerprint image50C divided into blocks with the identification numbers1to24is obtained. The detecting unit14c, using the above-mentioned technique, extracts a scratch51C from the fingerprint image50C and detects a block overlapping the area at which the scratch51C is present as a low-quality area. In this case, as illustrated inFIG. 8, because the area of the scratch51C overlaps the blocks with the identification numbers14,15,19,23, and24, they are detected as a low-quality area with a classification of “scratch (crack)” by the detecting unit14c.

As another embodiment, the detecting unit14ccalculates the degree of unclearness of each block from a fingerprint image using a statistical method or a frequency analysis method. Examples of the statistical method include a method that smoothes a local ridge direction and calculates a difference in the smoothed ridge direction as the degree of unclearness. Examples of the frequency analysis method include a method that, using the fast Fourier transform (FFT), converts a fingerprint image from the spatial domain into the frequency domain and calculates the value of the maximum power spectrum or the kurtosis of the energy distribution of each frequency component as the degree of unclearness. The degree of unclearness thus calculated is an indicator such that a higher degree indicates a higher possibility that the continuity of a pattern of a ridge or a valley is locally broken.

The detecting unit14cthen performs threshold determination on an average pixel value obtained by averaging the pixel values within any block whose degree of unclearness is not less than a predetermined value out of the blocks whose degrees of unclearness have been calculated from the fingerprint image, thereby detecting a low-quality area with a classification of “water droplet” or a classification of “chapping.” The reason why the block whose degree of unclearness is not less than a predetermined value is thus targeted for the detection of a low-quality area is because in an area with a high possibility that the continuity of a pattern of a ridge or a valley is locally broken, the possibility of the adhesion of water droplet or the occurrence of chapping is also high.

For example, the detecting unit14cdetermines whether an average pixel value within a block is not more than a non-valley threshold, thereby detecting a low-quality area with a classification of “water droplet.” In other words, as illustrated inFIG. 4C, when the valley is filled with a water droplet, the pixel value of the filled part is lower than that of the original valley. For this reason, when the average pixel value within a block is not more than a threshold that is just enough to estimate not to be a valley, the block can be estimated to be a low-quality area with a classification of “water droplet.” The detecting unit14cthus detects a block whose average pixel value within the block is not more than a non-valley threshold as a low-quality area with a classification of “water droplet.”

Conversely, the detecting unit14cdetermines whether an average pixel value within a block is not less than a non-ridge threshold, thereby detecting a low-quality area with a classification of “chapping.” In other words, as illustrated inFIG. 4D, when the ridge is damaged with “chapping,” the pixel value of the damaged part is higher than that of the original ridge, because the projection of the ridge is lower than the original. For this reason, when the average pixel value within a block is not less than a threshold that is just enough to estimate not to be a ridge, the block can be estimated to be a low-quality area with a classification of “chapping.” The detecting unit14cthus detects a block whose average pixel value within the block is not less than a non-ridge threshold as a low-quality area with a classification of “chapping.”

The second generating unit14dis a processing unit that generates the second narrowing-down data related to a low-quality area. The second narrowing-down data generated by the second generating unit14dmay be referred to as the second input narrowing-down data, and the second narrowing-down data registered in the authentication server30may be referred to as the second registered narrowing-down data below.

As an example, the second generating unit14dgenerates the second narrowing-down data that includes the identification number of a block forming the low-quality area detected by the detecting unit14cand the reference information used when the fingerprint image is divided into blocks by the dividing unit14aby the classification of occurrence factor. The second generating unit14dthen transmits the second narrowing-down data generated by the classification of occurrence factor to the authentication server30. When the low-quality area is corrected by the correcting unit14edescribed below, the second narrowing-down data with a classification of “scratch (crack)” out of the second narrowing-down data generated by the classification of occurrence factor is regenerated using the low-quality area after correction.

The reason why the reference information is thus included in the second narrowing-down data is because alignment is performed between a block included in the second input narrowing-down data and a block included in the second registered narrowing-down data. Examples of the reference information include the identification number of a block at which a singular point such as the center of the whorl of a fingerprint or a delta is present. Although a case is exemplified here in which the identification number of the block forming the low-quality area is regarded as the second narrowing-down data, the position information of the low-quality area is not always generated block by block. For example, the second generating unit14dmay use the pixel positions of low-quality areas with a classification of “scratch (crack),” a classification of “water droplet,” and a classification of “chapping” on a fingerprint image, as the second narrowing-down data.

The correcting unit14eis a processing unit that corrects the low-quality area detected by the detecting unit14c. As an example, described is a case of correcting a low-quality area with a classification of occurrence factors of “scratch (crack)”. The scratch or crack on a fingerprint image read by the fingerprint sensor12expands and contracts in its size depending on the magnitude of a force with which a user presses a finger pad against the fingerprint sensor12, that is, the input state of a fingerprint. For this reason, when a force with which the user presses the finger against the fingerprint sensor12varies, the force may cause a situation in which the size of a low-quality area varies even for the second input narrowing-down data and the second registered narrowing-down data of the same user.

Specifically, a larger force with which the user presses against the fingerprint sensor12more largely deforms ridges, thereby making the widths of valleys smaller. In contrast, a smaller force with which the user presses against the fingerprint sensor12less deforms ridges, making the widths of valleys larger. In other words, a smaller width of a valley means a larger force with which the finger is pressed against the fingerprint sensor12, thereby contracting the scratch or crack as compared to a case in which an appropriate force is applied in the use of the fingerprint sensor12. A larger width of a valley means a smaller force with which the finger is pressed against the fingerprint sensor12, thereby expanding the scratch or crack as compared to a case in which an appropriate force is applied in the use of the fingerprint sensor12.

The correcting unit14ethus determines whether the distance between ridges detected from a fingerprint image, that is, the width of a valley is within an appropriate range of the width of the valley suggesting an appropriate force applied in the use of the fingerprint sensor12.

As an example, the correcting unit14edetermines whether the average value of the distance between ridges detected at a plurality of locations on a fingerprint image, that is, the width of valleys WAVis within an appropriate range of the width of the valley, that is, “L1≦WAV≦L2.” When the average value of the width of valley WAVis smaller than the lower limit value L1of the appropriate range of the width of the valley, for example, 0.1 mm, it can be estimated that the scratch or crack is in a contracted state as compared to a case in which an appropriate force is applied in the use of the fingerprint sensor12. The lower limit value L1of the appropriate range may be appropriately set in accordance with the average value of the width of valleys of fingerprints of human beings, and further, which reading method of the slide type or the stamp type is employed by the fingerprint sensor12, or the like.

When it is determined that the low-quality area with a classification of “scratch (crack)” detected by the detecting unit14cis in a contracted state, the correcting unit14ethen performs correction to enlarge the low-quality area. As an example, the correcting unit14ecalculates the short axis that is orthogonal to the long axis of the low-quality area with a classification of “scratch (crack)” and enlarges the low-quality area so that the width of the low-quality area in the short axis direction increases a predetermined fold, for example, about twofold. The enlargement rate of the low-quality area may be set to be higher for a larger amount by which the average value of the width of valleys detected from a plurality of locations on the fingerprint image falls short of the lower limit value of the appropriate range of the width of the valley.

FIG. 9is a diagram illustrating an example of a fingerprint image.FIG. 10is diagram illustrating a detection result of a low-quality area.FIG. 11is a diagram illustrating the low-quality area after correction.

The black-filled part within the blocks illustrated inFIG. 10andFIG. 11indicates the low-quality area. In the example ofFIG. 9, a case is assumed in which the average value of the width of valleys WAVis smaller than the lower limit value L1of the appropriate range of the width of the valley.

As illustrated inFIG. 9, a fingerprint image60A in which a scratch61A is imaged is divided into 24 (=6 vertical by 4 horizontal) blocks with the identification numbers1to24by the dividing unit14a. When the fingerprint image60A is thus divided, as illustrated inFIG. 10, the blocks with the identification numbers10,15, and20that are present at positions that overlap the area in which the scratch61A is present are detected as a low-quality area with a classification of “scratch (crack).” The correcting unit14ecalculates the short axis63A that is orthogonal to the long axis62in the low-quality area with a classification of “scratch (crack).” The correcting unit14ethen, as illustrated inFIG. 11, performs the correction of the low-quality area to add the blocks with the identification numbers9,14,19, and24in the same direction as the short axis63A to the low-quality area formed of the identification numbers10,15, and20. The low-quality area after correction is thereby the identification numbers9,10,14,15,19,20, and24.

In contrast, when the average value WAVof the width of valleys is larger than the upper limit value L2of the appropriate range of the width of the valley, for example, 0.3 mm, it can be estimated that the scratch or crack is in an expanded state as compared to a case in which an appropriate force is applied in the use of the fingerprint sensor12. The upper limit value L2of the appropriate range may be appropriately set in accordance with the average value of the width of valleys of fingerprints of human beings, and further, which reading method of the slide type or the stamp type is employed by the fingerprint sensor12, or the like.

The correcting unit14ethen performs correction to reduce the low-quality area with a classification of “scratch (crack)” detected by the detecting unit14c. As an example, the correcting unit14ecalculates the short axis that is orthogonal to the long axis of the low-quality area with a classification of “scratch (crack)” and reduces the low-quality area so that the width of the low-quality area in the short axis direction increases a predetermined fold, for example, about two-thirds fold. The reduction rate of the low-quality area may be set to be higher for a larger amount by which the average value of the width of valleys detected from a plurality of sites on the fingerprint image exceeds the upper limit value of the appropriate range of the width of the valley.

FIG. 12is a diagram illustrating an example of a fingerprint image.FIG. 13is a diagram illustrating a detection result of a low-quality area.FIG. 14is a diagram illustrating the low-quality area after correction. The black-filled part within the blocks illustrated inFIG. 13andFIG. 14indicates the low-quality area. In the example ofFIG. 12, a case is assumed in which the average value of the width of valleys WAVis larger than the upper limit value L2of the appropriate range of the width of the valley.

As illustrated inFIG. 12, a fingerprint image60B in which a scratch61B is imaged is divided into 24 (=6 vertical by 4 horizontal) blocks with the identification numbers1to24by the dividing unit14a. When the fingerprint image60B is thus divided, as illustrated inFIG. 13, the blocks with the identification numbers9,10,13,14,15,18,19,20,23, and24that are present at positions that overlap the area in which the scratch61B is present are detected as a low-quality area with a classification of “scratch (crack).” The correcting unit14ecalculates the short axis63B that passes through the center of the long axis62B of the low-quality area. The correcting unit14ethen, as illustrated inFIG. 14, performs the correction of the low-quality area to remove the blocks with the identification numbers13,18, and23that are present in the same direction as the short axis63B from the low-quality area formed of the identification numbers9,10,13,14,15,18,19,20,23, and24. The low-quality area after correction is thereby the identification numbers9,10,14,15,19,20, and24.

After the low-quality area is thus corrected, the second generating unit14dregenerates the second narrowing-down data including the identification numbers of the blocks forming the low-quality area corrected by the correcting unit14eand the reference information used at the time of division into blocks. When the average value of the width of valleys WAVis within the appropriate range of the width of the valley, that is, “L1≦WAV≦L2,” the correction of the low-quality area by the correcting unit14eis not performed, and the second narrowing-down data generated by the second generating unit14dis transmitted to the authentication server30.

In the present embodiment, although a case is exemplified in which the low-quality area is enlarged or reduced by the size of the block obtained through division by the dividing unit14a, the processing executed by the correcting unit14eis not limited thereto. For example, to achieve the enlargement or reduction of the low-quality area by finer unit, the correcting unit14emay enlarge or reduce the low-quality area divided into smaller blocks than the blocks divided by the dividing unit14a. As an example, the blocks divided by the dividing unit may be further divided.

The OS executing unit15is a processing unit that controls the execution of an OS, which is basic software controlling the entire computer system.

As an example, the OS executing unit15performs processing related to a log-on and log-off. For example, when an authentication result received from the authentication server30is a success, the OS executing unit15permits a log-in by the user to the terminal device10. In other words, the OS executing unit15automatically inputs an account name and a password, thereby allowing the user to log in thereto.

In contrast, when the authentication result received from the authentication server30is a failure, the OS executing unit15prohibits a log-in by the user to the terminal device10. In this case, the OS executing unit15may output a notification that prompts the user to input the finger image again to a display unit or voice output unit (not illustrated) or may give a warning of unsuccessful authorization to use the terminal device10to the display unit or voice output unit (not illustrated). The OS executing unit15may also lock the operation of the terminal device10.

For the fingerprint data generating unit13, the narrowing-down data generating unit14, and the OS executing unit15, various integrated circuits and electronic circuits may be employed. Part of a functional unit included in the narrowing-down data generating unit14may be a separate integrated circuit or electronic circuit. Examples of the integrated circuit include an application specific integrated circuit (ASIC). Examples of the electronic circuit include a central processing unit (CPU) and a micro processing unit (MPU).

Configuration of Authentication Server

Described next is the configuration of the authentication server according to the present embodiment. As illustrated inFIG. 2, the authentication server30includes a communication I/F unit31, the storage unit32, a first calculating unit33, a second calculating unit34, a narrowing-down unit35, the authenticating unit36, and a registering unit37. The authentication server30may include, in addition to the functional units illustrated inFIG. 2, various functional units that a known server device includes, for example, various input devices and voice output devices.

The communication I/F unit31is an interface that controls communications with other devices such as the terminal device10and the like. For example, the communication I/F unit31receives the input fingerprint data, the first input narrowing-down data, and the second input narrowing-down data from the terminal device10and transmits the authentication result of the input fingerprint data to the terminal device10. As an embodiment of the communication I/F unit31, network interface cards such as a LAN card and a modem can be employed.

The storage unit32is a storage unit that stores therein the input fingerprint data, the first input narrowing-down data, and the second input narrowing-down data in association with each other. As an example, in order to calculate a first degree of narrowing-down similarity between the first input narrowing-down data and the first registered narrowing-down data, the storage unit32is referred to by the first calculating unit33described below. As another example, in order to calculate a second degree of narrowing-down similarity between the second input narrowing-down data and the second registered narrowing-down data, the storage unit32is referred to by the second calculating unit34described below. As still another example, in order to perform narrowing down to the registered fingerprint data to be compared with the input fingerprint data, the storage unit32is referred to by the narrowing-down unit35described below.

As an example, in order to compare the input fingerprint data and the registered fingerprint data, the storage unit32is referred to by the authenticating unit36described below. As another example, in order to register, in an associating manner, the second narrowing-down data input together with the input fingerprint data whose biometric authentication is successful at the authenticating unit36described below and the registered fingerprint data referred to in the successful biometric authentication, the storage unit32is referred to by the registering unit37.

The fingerprint data is the registered fingerprint data registered in advance in the authentication server30, which is fingerprint data compliant with any authentication method such as the minutiae method, the pattern matching method, and the frequency analysis method employed by the authenticating unit36described below. The first narrowing-down data is the first registered narrowing-down data registered in advance in the authentication server30and is narrowing-down data that includes the width of a ridge, the space between ridges, the number of minutiae, or the like associated with each other for each block. The second narrowing-down data is the second registered narrowing-down data registered in advance in the authentication server30and is narrowing-down data that includes the identification numbers of blocks forming a low-quality area and the reference information of the blocks.

For the storage unit32, a semiconductor memory element and a storage device can be employed. Examples of the semiconductor memory include a video random access memory (VRAM), a random access memory (RAM), and a flash memory. Examples of the storage device include a hard disk and an optical disk.

The first calculating unit33is a processing unit that calculates the first degree of narrowing-down similarity from the first input narrowing-down data and the first registered narrowing-down data. As an example, the first calculating unit33calculates the first degree of narrowing-down similarity from the first input narrowing-down data and the first registered narrowing-down data in any block forming an area that is not a low-quality area in both the input fingerprint data and the registered fingerprint data. An area other than the low-quality area, that is, a high-quality area in which a fingerprint-specific feature is clear on a fingerprint image may be denoted as a high-quality area below.

Specifically, the first calculating unit33refers to the both reference information of the first input narrowing-down data and the first registered narrowing-down data, thereby performing alignment of the blocks of the two. The first calculating unit33then calculates a high-quality area, that is, a set of blocks each having an identification number other than the identification numbers of blocks forming a low-quality area in the input fingerprint data. The first calculating unit33then calculated a high-quality area in the registered fingerprint data. The first calculating unit33then calculates an area in which the high-quality areas of the input fingerprint data and the registered fingerprint data are present in common. Then, in the area in which the high-quality areas of the input fingerprint data and the registered fingerprint data are present in common, the first calculating unit33compares the first input narrowing-down data and the first registered narrowing-down data, thereby calculating the first degree of narrowing-down similarity. The first calculating unit33calculates the first degree of narrowing-down similarity repeatedly by the classification of occurrence factor and performs the calculation repeatedly until the first degree of narrowing-down similarity is calculated with all pieces of first registered narrowing-down data.

As an example of an algorithm that thus calculates the first degree of narrowing-down similarity, the first calculating unit33can employ Formula (1) above. “I” in Formula (1) above indicates the input fingerprint data. “T” in Formula (1) above indicates the registered fingerprint data. “S(I,T)” in Formula (1) above indicates the first degree of narrowing-down similarity between the input fingerprint data I and the registered fingerprint data T by classification. “U” in Formula (1) above indicates a set of blocks corresponding to a high-quality area common in both the input fingerprint data I and the registered fingerprint data T. “Sα(I,T)” in Formula (1) above indicates the degree of similarity between the first input narrowing-down data and the first input narrowing-down data of the registered fingerprint data T at a block α.

The second calculating unit34is a processing unit that calculates the second degree of narrowing-down similarity from the second input narrowing-down data and the second registered narrowing-down data.

Described first is a case of calculating the second degree of narrowing-down similarity on the classification “scratch (crack).” Specifically, the second calculating unit34refers to the both reference information of the second input narrowing-down data and the second registered narrowing-down data, thereby performing alignment of the blocks of the two. The second calculating unit34then calculates the second degree of narrowing-down similarity using the following Formula (2).

“I” in Formula (2) above indicates the input fingerprint data. “T” in Formula (2) above indicates the registered fingerprint data. “C1(I,T)” in Formula (2) above indicates a correlation value of a low-quality area related to the classification “scratch (crack)” between the input fingerprint data I and the registered fingerprint data T. “N(I,T)” in Formula (2) above indicates the number of common blocks forming a low-quality area with a classification of “scratch (crack)” in the input fingerprint data I and the registered fingerprint data T. “N(I)” in Formula (2) above indicates the number of blocks forming the low-quality area with a classification of “scratch (crack)” of the input fingerprint data I. “N(T)” in Formula (2) above indicates the number of blocks forming the low-quality area with a classification of “scratch (crack)” of the registered fingerprint data T.

Thus, as more blocks form a low-quality area common in the second input narrowing-down data and the second registered narrowing-down data, a higher second degree of narrowing-down similarity is calculated by the second calculating unit34. For this reason, the second calculating unit34calculates the second degree of narrowing-down similarity to be higher when scratches or cracks having the same shape are present at the same position in the second input narrowing-down data and the second registered narrowing-down data and calculates the second degree of narrowing-down similarity to be lower when cracks having different shapes are present at the same position. As a result, seemingly-low-value information of an unclear fingerprint-specific feature on a fingerprint image is effectively used for narrowing down.

Described next is a case of calculating the second degree of narrowing-down similarity on the classification “chapping.” Because the chapping is likely to vary in a shorter period than a scratch or crack, the second registered narrowing-down data is preferably used in a limited period. In order to calculate the second degree of narrowing-down similarity on the classification “chapping,” the second calculating unit34determines whether the registered fingerprint data is within a predetermined period after the previous successful authentication or the registration of fingerprint data. The period is preferably within two weeks to one month, which is an approximate period during which skin is replaced. The second calculating unit34then calculates the second degree of narrowing-down similarity if the registered fingerprint data is within the predetermined period after the previous successful authentication or the registration of fingerprint data.

Specifically, the second calculating unit34refers to the both reference information of the second input narrowing-down data and the second registered narrowing-down data, thereby performing alignment of the blocks of the two. The second calculating unit34then calculates the second degree of narrowing-down similarity using the following Formula (3).

“I” in Formula (3) above indicates the input fingerprint data. “T” in Formula (3) above indicates the registered fingerprint data. “C2(I,T)” in Formula (3) above indicates a correlation value of a low-quality area related to the classification “chapping” between the input fingerprint data I and the registered fingerprint data T. “N(I,T)” in Formula (3) above indicates the number of common blocks forming a low-quality area with a classification of “chapping” between the input fingerprint data I and the registered fingerprint data T. “N(I)” in Formula (3) above indicates the number of blocks forming the low-quality area with a classification of “chapping” of the input fingerprint data I. “N(T)” in Formula (3) above indicates the number of blocks forming the low-quality area with a classification of “chapping” of the registered fingerprint data T.

The narrowing-down unit35is a processing unit that narrows down the registered fingerprint data stored in the storage unit32to the registered fingerprint data to be compared with the input fingerprint data. As an example, the narrowing-down unit35adds the first degree of narrowing-down similarity calculated by the first calculating unit33and the second degree of narrowing-down similarity calculated by the second calculating unit34, thereby calculating the integrated degree of similarity in which the both degrees of narrowing-down similarity of the high-quality area and the low-quality area are reflected. The narrowing-down unit35then performs narrowing down to registered fingerprint data corresponding to registered narrowing-down data whose integrated degree of similarity is within a predetermined ratio with respect to the total number N of the registered fingerprint data, for example, within the top one-tenth (=N/10). As another example, the narrowing-down unit35performs narrowing down to registered fingerprint data corresponding to registered narrowing-down data whose integrated degree of similarity is not less than a predetermined threshold. In calculating the integrated degree of similarity, the narrowing-down unit35may perform any statistical processing, not only addition.

The authenticating unit36is a processing unit that compares the registered fingerprint data obtained as a narrowing-down result by the narrowing-down unit35and the input fingerprint data to perform authentication. As an example, the authenticating unit36compares the registered fingerprint data narrowed down by the narrowing-down unit35and the input fingerprint data, thereby calculating a comparison score. The authenticating unit36determines whether the maximum comparison score out of comparison scores calculated earlier is not less than a predetermined threshold.

When the maximum comparison score is not less than the threshold, the authenticating unit36determines that fingerprint authentication is successful between the registered fingerprint data having the maximum comparison score and the input fingerprint data. When the maximum comparison score is less than the threshold, the authenticating unit36compares the registered fingerprint data other than the registered fingerprint data obtained as a narrowing-down result by the narrowing-down unit35and the input fingerprint data, thereby calculating a comparison score. When the maximum comparison score is less than the threshold then, the authenticating unit36then determines that the authentication of the input fingerprint data is unsuccessful. In contrast, when the maximum comparison score is not less than the threshold, the authenticating unit36determines that fingerprint authentication is successful between the registered fingerprint data having the maximum comparison score and the input fingerprint data. The authenticating unit36then transmits the authentication result to the terminal device10. The authenticating unit36can employ any authentication method such as the minutiae method, the pattern matching method, or the frequency analysis method.

The registering unit37is a processing unit that registers, in the storage unit32in an associating manner, the second narrowing-down data input together with the input fingerprint data whose biometric authentication is successful at the authenticating unit36and the registered fingerprint data referred to in the successful biometric authentication.

As an example, when authentication is successful between the registered fingerprint data obtained as a narrowing-down result by the narrowing-down unit35and the input fingerprint data, or when authentication is successful between the registered fingerprint data other than the registered fingerprint data obtained as a narrowing-down result by the narrowing-down unit35and the input fingerprint data, the registering unit37starts up processing. When the second narrowing-down data has been already registered in association with the registered fingerprint data by the storage unit32, the registering unit37registers in an overwriting manner the second narrowing-down data input together with the input fingerprint data whose authentication is successful this time. The registering unit37registers in an overwriting manner the second narrowing-down data for each of classifications “scratch (crack),” “chapping,” and “water droplet.”

As another example, the registering unit37registers or registers in an overwriting manner not only the second input narrowing-down data but also the first input narrowing-down data. If the first registered narrowing-down data is overwritten onto the new first input narrowing-down data, information including a low-quality area is also overwritten, which may degrade the quality of the first registered narrowing-down data. For this reason, the registering unit37registers or registers in an overwriting manner only a high-quality area by the classification of occurrence factor calculated together with the first degree of narrowing-down similarity by the first calculating unit33.

For the first calculating unit33, the second calculating unit34, the narrowing-down unit35, the authenticating unit36, and the registering unit37, various integrated circuits and electronic circuits may be employed. Examples of the integrated circuits include an ASIC. Examples of the electronic devices include a CPU and an MPU.

Flow of Processing

Described next is the flow of processing of the devices of the biometric authentication system according to the present embodiment. The following describes (1) log-in processing performed by the terminal device10, and then (2) biometric authentication processing performed by the authentication server30.

FIG. 15is a flowchart illustrating the procedure of log-in processing according to the first embodiment. This log-in processing starts up if a fingerprint image is read by the fingerprint sensor12of the terminal device10with the terminal device10started up.

As illustrated inFIG. 15, the fingerprint data generating unit13generates fingerprint data to be used for fingerprint authentication from a fingerprint image (Step S101). The dividing unit14athen divides the fingerprint image into blocks with a certain size (Step S102).

Subsequently, using the fingerprint image divided into blocks by the dividing unit14a, the first generating unit14bgenerates first narrowing-down data related to a fingerprint-specific feature (Step S103). The detecting unit14cthen detects a low-quality area from the fingerprint image divided into blocks by the dividing unit14a(Step S104). The second generating unit14dthen generates second narrowing-down data related to the low-quality area (Step S105).

The correcting unit14edetermines whether the width of a valley detected from the fingerprint image is within an appropriate range of the width of the valley suggesting an appropriate force applied in the use of the fingerprint sensor12(Step S106). If the width of the valley is within the appropriate range (Yes at Step S106), the flow skips the processing of Steps S107and S108and advances to Step S109.

If the width of the valley is not within the appropriate range (No at Step S106), the correcting unit14eperforms correction to enlarge or reduce the low-quality area detected by the detecting unit14c(Step S107). Thereafter, the second generating unit14dregenerates the second narrowing-down data including the identification numbers of the blocks forming the low-quality area corrected by the correcting unit14eand the reference information used at the time of division into blocks (Step S108).

The communication I/F unit11transmits the input fingerprint data, the first input narrowing-down data, and the second input narrowing-down data to the authentication server30(Step S109). The communication I/F unit11then receives an authentication result from the authentication server30(Step S110).

If the authentication result is a success (Yes at Step S111), the OS executing unit15permits a log-in by the user to the terminal device10(Step S112) and ends the processing. If the authentication result is a failure (No at Step S111), the OS executing unit15does not permit a log-in and ends the processing.

The processing of Steps S103and S104illustrated inFIG. 15and the processing Steps S105and S108illustrated inFIG. 15are not always performed in the illustrated order and may be performed with the order reversed.

(2) Biometric Authentication Processing

FIG. 16is a flowchart illustrating the procedure of biometric authentication processing according to the first embodiment. This biometric authentication processing is processing that is repeatedly performed so long as the power of the authentication sever is on. The biometric authentication starts up upon receiving the input fingerprint data, the first input narrowing-down data, and the second input narrowing-down data from the terminal device10.

As illustrated inFIG. 16, the first calculating unit33calculates the first degree of narrowing-down similarity from the first input narrowing-down data and the first registered narrowing-down data (Step S301). The second calculating unit34calculates the second degree of narrowing-down similarity from the second input narrowing-down data and the second registered narrowing-down data (Step S302).

Thereafter, using the first degree of narrowing-down similarity and the second degree of narrowing-down similarity, the narrowing-down unit35calculates the integrated degree of similarity in which both the degrees of narrowing-down similarity of the high-quality area and the low-quality area are reflected (Step S303). The narrowing-down unit35then performs narrowing down to registered fingerprint data corresponding to the registered narrowing-down data whose integrated degree of similarity is within a predetermined ratio with respect to the total number N of the registered fingerprint data, for example, within the top one-tenth (=N/10) (Step S304).

The authenticating unit36compares the registered fingerprint data obtained as a narrowing-down result by the narrowing-down unit35and the input fingerprint data (S305). If authentication is unsuccessful between the registered fingerprint data obtained as a narrowing-down result by the narrowing-down unit35and the input fingerprint data (No at Step S306), the authenticating unit36compares the registered fingerprint data other than registered fingerprint data obtained as a narrowing-down result by the narrowing-down unit35and the input fingerprint data (Step S307).

As a result, if authentication is successful between the registered fingerprint data other than registered fingerprint data obtained as a narrowing-down result by the narrowing-down unit35and the input fingerprint data (Yes at S308), the registering unit37performs the following processing. Specifically, the registering unit37registers, in the storage unit32in an associating manner, the second narrowing-down data, which is out of the data previously obtained as a narrowing-down result and input together with the input fingerprint data whose biometric authentication is successful next, and the registered fingerprint data referred to in the successful biometric authentication (Step S309). The authenticating unit36then transmits the authentication result to the terminal device10(Step S310) and ends the processing.

If authentication is unsuccessful even between the registered fingerprint data other than registered fingerprint data obtained as a narrowing-down result by the narrowing-down unit35and the input fingerprint data (No at Step S308), the authenticating unit36transmits the authentication result to the terminal device10(Step S310) and ends the processing.

If authentication is successful between the registered fingerprint data obtained as a narrowing-down result by the narrowing-down unit35and the input fingerprint data (Yes at Step S306), the registering unit37performs the following processing. Specifically, the registering unit37registers, in the storage unit32in an associating manner, the second narrowing-down data input together with the input fingerprint data whose biometric authentication by the authenticating unit36is successful and the registered fingerprint data referred to in the successful biometric authentication (Step S309). The authenticating unit36then transmits the authentication result to the terminal device10(Step S310) and ends the processing.

The processing of Steps S301and S302illustrated inFIG. 16are not always performed in the illustrated order and may be performed with the order reversed.

Effect of First Embodiment

As described above, the biometric authentication system1according to the present embodiment, even when the fingerprint of the user changes due to a scratch or chapping, adds the position information of the area whose fingerprint-specific feature has become unclear on the fingerprint image because of the scratch or chapping to the registered fingerprint data as narrowing-down data once authentication is successful. As a result, the biometric authentication system1according to the present embodiment, when fingerprint data having a scratch or chapping is input in later authentication, performs narrowing down to registered fingerprint data having position information similar to the position information of that area. The biometric authentication system1according to the present embodiment therefore has an increased probability that the registered fingerprint data of the user who inputs the fingerprint is included in the narrowing-down result.

The biometric authentication system1according to the present embodiment can therefore stabilize the accuracy of narrowing down. Furthermore, because the biometric authentication system1according to the present embodiment has an increased probability that the registered fingerprint data of the user who inputs the fingerprint is included in the narrowing-down result, it can prevent a failure in authentication following a failure in narrowing down and reduce authentication time from the inputting of a fingerprint to the outputting of an authentication result.

The biometric authentication system1according to the present embodiment narrows down the registered fingerprint data to the registered fingerprint data having position information similar to the position information of the low-quality data detected from the input fingerprint image. As a result, the biometric authentication system1according to the present embodiment can effectively use low-value information of an unclear fingerprint-specific feature a fingerprint image for narrowing down.

The biometric authentication system1according to the present embodiment narrowing the registered fingerprint data down to the registered fingerprint data having position information similar to the position information of the low-quality area detected from the input fingerprint image and having a feature quantity similar to the first input narrowing-down data in an area other than the low-quality area. The biometric authentication system1according to the present embodiment can thereby effectively use information on the entire fingerprint image including low-value information of an unclear fingerprint-specific feature on a fingerprint image for narrowing down.

The biometric authentication system1according to the present embodiment corrects the position information of the low-quality area in accordance with the input state of the input fingerprint data. The biometric authentication system1according to the present embodiment performs narrowing down to the registered fingerprint data to be compared with the input fingerprint data based on the position information of the corrected low-quality area and the stored position information of the low-quality area. As a result, the biometric authentication system1according to the present embodiment can prevent variations in low-quality areas detected from the fingerprint of the same user. The biometric authentication system1according to the present embodiment can therefore stabilize the accuracy of narrowing down.

Second Embodiment

An embodiment of the disclosed system has been so far described. The present invention may be implemented in a variety of different embodiments. The following describes another embodiment included in the present invention.

Morphological Operations

Although a case was exemplified in which the correcting unit14eenlarges or reduces the low-quality area in the short axis direction in the above first embodiment, the processing performed by the correcting unit14eis not limited thereto. For example, in the disclosed system, when the average value of the width of valleys WAVis smaller than the lower limit value L1of the appropriate range of the width of the valley, the correcting unit14emay enlarge the number of blocks of the low-quality area by expansion through the morphological operations. In the disclosed system, when the average value of the width of valleys WAVis larger than the upper limit value L2of the appropriate range of the width of the valley, the correcting unit14emay reduce the number of blocks of the low-quality area by contraction through the morphological operations.

Standalone

Although a case is exemplified in which fingerprint authentication is performed by a client-server system in the above first embodiment, fingerprint authentication is not always performed by the system. For example, the functions of the fingerprint data generating unit13, narrowing-down data generating unit14, the first calculating unit33, the second calculating unit34, the narrowing-down unit35, the authenticating unit36, and the registering unit37may be installed in a notebook personal computer or a cellular phone to be implemented stand-alone.

Authentication Environment

Although a case of being applied to log-in authentication for a PC is exemplified in the above first embodiment, the disclosed system is not limited thereto and can be widely applied to environments in which biometric authentication is used, including a case of performing biometric authentication on a log-in to an application and a case of performing biometric authentication on entering and leaving a room.

Other than Fingerprint

Although a case of performing fingerprint authentication is exemplified in the above first embodiment, the disclosed system is not limited thereto and can be similarly applied to other biometric authentication using a palm print or a vein. For example, vein authentication has a similar problem that there is a temporary change in biometric information due to a thrombus in a vein, and thus the disclosed system can be similarly applied there.

Distribution and Integration

The components of the illustrated device are not always requested to be physically configured as illustrated. In other words, a specific form of the distribution and integration of the device is not limited to the illustrated one, and part of or the entire the device may be configured through functional or physical distribution and integration in an arbitrary unit depending on various loads and use conditions. For example, the first calculating unit33, the second calculating unit34, the narrowing-down unit35, the authenticating unit36, or the registering unit37may be connected as an external device of the authentication server30through a network. The first calculating unit33, the second calculating unit34, the narrowing-down unit35, the authenticating unit36, or the registering unit37each may be included in another device and are connected through a network to cooperate, thereby achieving the above functions of the biometric authentication system. The fingerprint data generating unit13, the narrowing-down data generating unit14, and part of or the entire functions included in the narrowing-down data generating unit14may be connected as an external device through a network. The fingerprint data generating unit13, the narrowing-down data generating unit14, and part of or the entire functions included in the narrowing-down data generating unit14each may be included in another device and are connected through a network to cooperate, thereby achieving the above functions of the biometric authentication system.

Biometric Authentication Program

Various pieces of processing described in the above embodiment may be achieved by executing a program prepared in advance with a computer such as a personal computer or a workstation. The following describes an example of a computer that executes a biometric authentication program having the same functions as the above embodiments usingFIG. 17.

FIG. 17is a diagram for describing an example of a computer that executes the biometric authentication program according to the first embodiment and the second embodiment. As illustrated inFIG. 17, this computer100includes an operation unit110a, a speaker110b, a camera110c, a display120, and a communicating unit130. The computer100also includes a CPU150, a ROM160, an HDD170, and a RAM180. These units with the reference numerals110to180are connected through a bus140.

The HDD170stores therein in advance a biometric authentication program170athat exhibits the same functions as the first calculating unit33, the second calculating unit34, the narrowing-down unit35, the authenticating unit36, and the registering unit37described in the above first embodiment. The biometric authentication program170amay be appropriately integrated or separated in the same manner as the components illustrated inFIG. 2, such as the first calculating unit33, the second calculating unit34, the narrowing-down unit35, the authenticating unit36, and the registering unit37. Namely, all data illustrated as stored in the HDD170does not always have to be stored in the HDD170, and it is sufficient if data for each process is stored in the HDD170.

The CPU150reads the biometric authentication program170aout of the HDD170and loads it into the RAM180. As illustrated inFIG. 17, thereby, the biometric authentication program170afunctions as a biometric authentication process180a. This biometric authentication process180aloads various types of data read out of the HDD170into their own areas appropriately assigned on the RAM180, and based on the loaded various types of data, various types of processing are executed. The biometric authentication process180aincludes the processing executed by the first calculating unit33, the second calculating unit34, the narrowing-down unit35, the authenticating unit36, and the registering unit37illustrated inFIG. 2, for example, the processing illustrated inFIG. 15andFIG. 16. Furthermore, all the processing units illustrated as virtually realized on the CPU150do not always have to operate on the CPU150, and it is sufficient if a processing unit for each process is virtually realized.

The biometric authentication program170adoes not always have to be stored in the HDD170or the ROM160in advance. For example, the program is stored in portable physical media such as a flexible disk, that is, an FD, a CD-ROM, a DVD disk, an optical disk, and an IC card, which are inserted into the computer100. The computer100may acquire the program from the portable physical media to execute it. The program may be stored in another computer or a server device connected to the computer100through a public network, the Internet, a LAN, a WAN, or the like, and the computer100may acquire the program therefrom and execute it.

An embodiment of a biometric authentication system disclosed by the present application provides the effect of stabilizing the accuracy of narrowing down.