Personal authentication application and business application incorporating same

Abstract

[Problem] To provide a personal authentication application that achieves both security and personal information protection while using biological image data to perform personal authentication.　[Solution] The present invention comprises: a biological image data acquisition module 110 for acquiring, as biological image data, image data of a user or an image data portion that has been obtained by cutting out a specific region of the image data; a biological image spatial frequency conversion data creation module 120 for converting the biological image data into a spatial frequency domain by a spatial frequency conversion tool 121 to obtain biological image spatial frequency conversion data; a biological image sequence data extraction module 130 for obtaining, as biological image sequence data, a numerical value of the number of occurrences for each frequency in the obtained biological image spatial frequency conversion data; and a biological hash data creation module 140 for converting the obtained biological image sequence data as input data into a hash value by a hash function tool 141 to obtain biological hash data.

Description

Personal authentication applications and business applications incorporating them

[0001] The present invention relates to an identity authentication application that runs on a computer system. One example relates to an identity authentication application that ensures that an individual logs in and starts use properly, and that the individual finishes use and logs out properly. Identity authentication applications that run on computer systems include those that run during online login and those that run during offline login, but those that run during online login are sometimes called eKYC applications (electric Know Your Customer Applications). The identity authentication application of the present invention is not limited to business applications that are logged in to, and can be applied to business applications for a variety of purposes. For example, the present invention can be applied to business applications in which an individual logs in at the start of use, authenticates themselves before starting use of the application, and then logs out and closes the application after authenticating themselves when finished.

[0002] Various applications are running in computer systems, and various security measures are also being implemented. The term "security measures" has a broad meaning, but for example, measures that authenticate an individual who is authorized to access an application file used by the application and require the input of various code information, such as a password, when accessing and opening the application system so that only that individual can use the application file are widely adopted. There are various types of identity authentication applications in the prior art. Depending on the security level, some require the user to simply enter a password from a keyboard, some require the user to enter ID information from a portable IC card along with the password, and some require the user to enter biometric image data of the user, such as a face image, fingerprint image, vein pattern image, palm image, iris image, or voice data, for identity authentication.

[0003] In particular, personal authentication applications that use biometric image data are difficult to impersonate and have a high level of security. After completing personal authentication using biometric image data, users can log in to business applications, input various data, view data, and operate applications.

[0004] FIG. 14 is a diagram briefly explaining the operation of launching a typical application, opening an application file, and then exiting and closing it. This is an example, showing a typical operation of opening and closing an application file. When a user selects the icon of the application 10 they wish to use with a pointing device such as a mouse and launches it by double-clicking or other operation, a password entry column pops up to confirm usage authority, as shown in the upper part of FIG. 14 . It is common for users to be required to enter a password before starting to use the application 10. Note that some applications 10 may require the entry of ID information from an IC card or biometric image data in addition to a password.

[0005] Japanese Patent Application Laid-Open No. 2006-277193

[0006] Here, conventional personal authentication applications using biometric image data are highly secure and technically worthy of praise. However, from the user's perspective, the biometric image data serving as the correct data for matching must be registered and stored in the server system or cloud system of the operator of the personal authentication application they wish to use. However, biometric image data is highly private as personal information, and it is strongly anticipated that users will feel uneasy or reluctant to store their own biometric image data in the operator's server system or cloud system.

[0007] The business applications that are expected to be used are diverse, including medical consultations, shopping payments, payments for various services, service use at financial institutions, building entry and exit, home healthcare (acquisition of lifestyle information and health data within the home), mail order use, and logistics use (delivery and receipt), and it is expected that users will feel uneasy or reluctant to entrust their own biometric image data to the operator's server system or cloud system. Legal measures to protect personal information are also being established.

[0008] On the other hand, if someone impersonates you and falsely enters data or uses services when using various business applications, it could have an impact on your life and property, so it is necessary to raise the security level to prevent impersonation.

[0009] Therefore, the present invention aims to provide an "identity authentication application" and a "business application incorporating an identity authentication application" that achieve both security and personal information protection, by using biometric image data that is difficult to impersonate when using business application files, while eliminating the anxiety that users may feel about entrusting their own biometric image data to an operational system.

[0010] In order to achieve the above object, the personal authentication application of the present invention is an personal authentication application that can be used on a computer system, and includes: a biometric image data acquisition module that acquires image data of a user or an image data portion extracted from a specific region of the image data as biometric image data; a biometric image spatial frequency conversion data generation module that includes a spatial frequency conversion tool and converts the biometric image data acquired by the biometric image data acquisition module into the spatial frequency domain using the spatial frequency conversion tool to obtain biometric image spatial frequency conversion data; a biometric image sequence data extraction module that obtains the numerical value of the number of occurrences for each frequency as biometric image sequence data from the obtained biometric image spatial frequency conversion data; and a biometric hash data generation module that includes a hash function tool and converts the biometric image sequence data obtained by the sequence data extraction module into a hash value as input data to obtain biometric hash data.

[0011] With the above configuration, the identity authentication application of the present invention does not store or use the user's biometric image data acquired by the "biometric image data acquisition module" as image data on a server on a network, but converts the user's biometric image data into biometric hash data, and then stores and uses the biometric hash data. The nature of hash function tools makes it virtually irreversible to reverse-calculate the sequence obtained through the hash function tool back to the original sequence. In other words, the "biometric hash data," which is the final calculation result of the identity authentication application of the present invention, is data unique to the user, enabling a high level of identity authentication while ensuring a high level of privacy security for personal information by preventing the user's "biometric information, such as a facial image," from being stored or used on the network.

[0012] The personal authentication application of the present invention has an enrollment phase and an authentication phase. The enrollment phase is performed as follows: When the user enters the biometric image data during enrollment, the biometric image spatial frequency conversion data generation module executes an enrollment conversion data generation process, converting the spatial frequency conversion data obtained from the biometric image data using the spatial frequency conversion tool into enrollment biometric image spatial frequency conversion data; the biometric image sequence data extraction module executes an enrollment biometric image sequence data extraction process, extracting the numerical value of the number of occurrences per frequency from the enrollment biometric image spatial frequency conversion data as the biometric image sequence data; and the biometric hash data generation module executes an enrollment biometric hash data generation process, converting the biometric image sequence data obtained during enrollment into a hash value to generate enrollment biometric hash data, and an enrollment biometric hash data transmission process, transmitting the generated enrollment biometric hash data to a database in the user terminal's storage device or a cloud server. In other words, in the personal authentication application, the biometric hash data generated during the enrollment phase is generated as "enrollment biometric hash data." This registered "enrollment biometric hash data" is also user-specific data and serves as source data enabling a high level of personal authentication. In the registration phase, the registration biometric hash data storage processing module stores the registration biometric hash data generated for each user at the time of registration in a storage device of the user terminal or in a database of a server on the cloud.

[0013] The authentication phase proceeds as follows: When the user inputs the biometric image data during identity authentication, the biometric image spatial frequency conversion data generation module executes a transformation data generation process during identity authentication, converting the spatial frequency conversion data obtained from the biometric image data using the spatial frequency conversion tool into biometric image spatial frequency conversion data for identity authentication; the biometric image sequence data extraction module executes a biometric image sequence data extraction process during identity authentication, extracting the numerical values ​​of the number of occurrences per frequency from the biometric image spatial frequency conversion data for identity authentication as the biometric image sequence data for identity authentication; and the biometric hash data generation module executes an authentication biometric hash data generation process, converting the biometric image sequence data for identity authentication into a hash value to generate authentication biometric hash data. In other words, in the identity authentication application, the biometric hash data generated during the authentication phase is used as "authentication biometric hash data." This "authentication biometric hash data" is also user-specific data and serves as source data enabling a high level of identity authentication.

[0014] In the matching process of the authentication phase, an identity authentication matching processing module receives the authentication biometric hash data generated during identity authentication from the user, and performs a matching process with the registered biometric hash data stored in the storage device of the user terminal or in a database on a cloud server to execute identity authentication. If this matching process is successful, the user is authenticated as the identity of the user, and if the matching process is unsuccessful, the user is not authenticated as the identity of the user. With the above configuration, the matching process can be executed correctly, and the correct results of a match and a mismatch with another person can be obtained.

[0015] Next, in order to improve the authentication accuracy in the personal authentication application, it is preferable to take the following measures.

[0016] The first technique is to organize the data by performing scattering and discretization processing on the frequencies and the number of occurrences thereof. In the biometric image sequence data extraction module, it is preferable that the frequencies to be counted in the extraction processing of the biometric image sequence data and the count results of the number of occurrences for each frequency are compiled into spatial frequency conversion values ​​that are scatter data.

[0017] Here, if the number of frequency occurrences is counted too precisely and even small numerical differences are captured and matched, the matching accuracy rate may be too low even for the true person. Therefore, it is preferable to summarize the number of frequency occurrences as a discrete numerical value as the spatial frequency conversion value. With the above configuration, it is possible to prevent the occurrence of a problem in which the matching accuracy rate is too low even for the true person.

[0018] The third innovation is to use multiple biometric images to input. Here, the biometric images to be input are of the same body part. In other words, if the biometric images are of the user's face, multiple facial images are used. Therefore, the registration biometric hash data is generated using multiple pieces of image data as the biometric image data for registration input by the user during the registration. The authentication biometric hash data is generated using multiple pieces of image data as the biometric image data for identity authentication input by the user during the identity authentication. In this way, using multiple biometric images makes it easier to stabilize the matching process results.

[0019] The fourth innovation is that when multiple biometric images are used, a frequency band that is likely to appear with a high degree of coincidence in the processing of the multiple biometric images is extracted as a specific frequency band for matching. That is, in the processing of each data derived from the multiple biometric images in the biometric image sequence data extraction module, a specific frequency band in which the number of occurrences counted for each frequency subjected to the scattering processing is the same discrete value is selected, and the discrete value of the number of occurrences in the specific frequency band is obtained as the biometric image sequence data, and the biometric hash data generation module generates the biometric hash data from the biometric image sequence data corresponding to the specific frequency band. With the above configuration, it is possible to extract only the frequency band in the target biometric image that is likely to have stable image quality, thereby making the matching process more stable.

[0020] The fifth innovation is to utilize check data to reduce erroneous determinations in addition to biometric hash data. That is, it is preferable that the biometric hash data generation module outputs, as check data, the absolute values ​​of the upper and lower limits of the specific frequency band and the absolute value of the number of occurrences of the specific frequency band, in addition to the generated biometric hash data for the specific frequency band. With the above configuration, by utilizing separate check data in addition to the matching result using biometric hash data, it may be useful in checking for so-called erroneous determinations (cases where a match is made with a different person or a case where a match is not made with the person in question).

[0021] Furthermore, variations in the configuration of the present invention will be described. The tool that can be used as the spatial frequency transformation tool is not limited to one. For example, a Fourier transform tool or a wavelet transform tool can be used. Any other tool that can be used as the spatial frequency transformation tool can also be used.

[0022] Next, usable biometric image data may include any one of a face image, a palm image, a vein image, a fingerprint image, an iris image, and a voice data image, or a combination thereof. It is possible to set an image area to be used for each biometric image data. For example, a face image may be a partial image of a face image, such as the forehead, eyes, and nose. Furthermore, a vein image may be an image of the first to third joints of a specific finger, such as the index finger or middle finger. A fingerprint image may be an image of a 2 cm square in the center of the pad of a specific finger, such as the index finger or middle finger. Voice data may be voice information in which a specific word is pronounced.

[0023] Next, in order to increase the security level of the personal authentication application of the present invention, multiple types of hash function tools are prepared, allowing the administrator to use different tools depending on the time and situation. There are various hash function tools, such as SHA-256, RIPEMD-160, BLAKE, CRC32, and CRC64, and preparing these tools and allowing the administrator to use different tools depending on the time and situation can be useful in improving security.

[0024] In the above configuration, since it is not known in advance which sequence transformation hash function tool module will be selected in the usage phase, it is preferable to obtain conversion results for all hash function tools that can be selected at the time of use in the registration phase. Note that there is no need to ask the registrant to input biometric image data multiple times; based on the biometric image data input once, in the registration phase, it is sufficient to switch and convert the hash function tools that can be selected at the time of use, obtain registration biometric hashes for all patterns, and register them as registration hash data for each user.

[0025] Next, the business application according to the present invention uses the above-described personal authentication application, and can be a business application in which login is possible only on the condition that the user's identity has been authenticated by the personal authentication application at the time of login. Furthermore, as an option for the business application of the present invention, it is also preferable to have a system in which logout is possible only on the condition that the user's identity has been authenticated by the personal authentication application at the time of logout. According to the above configuration, personal authentication can be performed at the start and end of use of the business application, thereby improving the security level. In other words, personal authentication is performed at the time of login to use the business application, which is the intended use, to ensure the security level. Furthermore, if personal authentication is also performed at the time of logout as an option, the security level can be further improved.

[0026] 11 is a diagram simply illustrating the configuration of an identity authentication application 100 according to a first embodiment. FIG. 12 is a diagram illustrating a facial image captured and input by user 1 in the enrollment phase. FIG. 13 is a diagram illustrating a scatter plot plotted by the biometric image spatial frequency conversion data generation module 120. FIG. 3 is an enlarged view of the graph in a specific frequency band of FIG. 3. FIG. 14 is a diagram in which parts belonging to the same grid in the specific frequency band graph are hatched. FIG. 15 is a diagram illustrating a facial image captured and input by user 1 in the authentication phase. FIG. 16 is a diagram illustrating a scatter plot plotted by the biometric image spatial frequency conversion data generation module 120. FIG. 17 is an enlarged view of the graph in a specific frequency band of FIG. 7. FIG. 18 is a diagram in which parts belonging to the same grid in the specific frequency band graph are hatched. FIG. 19 is a diagram illustrating a facial image captured and input by user 2 (another person) in the authentication phase. FIG. 19 is a diagram illustrating a scatter plot plotted by the biometric image spatial frequency conversion data generation module 120 based on the image of user 2 (another person). FIG. 11 is an enlarged view of the graph in a specific frequency band of FIG. 12. FIG. 19 is a diagram in which parts belonging to the same grid in the specific frequency band graph based on the image of user 2 (another person). FIG. 19 is a diagram illustrating an outline of the operation of a conventional general application.

[0027] The best mode for carrying out the present invention will be specifically described below with reference to examples, although the present invention is not limited to these examples.

[0028] An outline of an identity authentication application and its operation according to the present invention will be described below as Example 1. The identity authentication application according to Example 1 will be described. FIG. 1 is a diagram simply illustrating the configuration of an identity authentication application 100 according to Example 1. As shown in FIG. 1, the identity authentication application 100 is installed on a smartphone or a computer system, or is provided in a form that can be used on a network 3 as an ASP. In Example 1, as an example, a configuration will be described in which a user uses a smartphone 1 and a cloud system 2 is provided on the network 3. It is also possible to use a configuration in which the user uses a shared terminal installed in a government agency instead of the smartphone 1, or a configuration in which the user uses a server managed by an administrator on the network 3.

[0029] 1, an identity authentication application 100 built in a smartphone 1 includes a biometric image data acquisition module 110, a biometric image spatial frequency conversion data generation module 120, a biometric image sequence data extraction module 130, a biometric hash data generation module 140, and a registered biometric hash data transmission processing module 150. Also, in the configuration example shown in Fig. 1, a server or cloud system 2 on a network 3 includes an authentication server 200, which includes an authentication registration database 210, a registration processing module 220, an identity authentication matching processing module 230, and a data communication module 240.

[0030] The biometric image data acquisition module 110 is a program module that inputs biometric image data from a device that inputs the biometric image data. For example, the device may be a camera, reader, scanner, microphone, or the like, depending on the biometric image data to be acquired. In the example of FIG. 1 , the user terminal 1 is equipped with a camera 111. To prevent impersonation and fraud, it is preferable to acquire biometric image data by taking a live biometric image or the like via a device equipped or controlled by the personal authentication application 100, rather than simply accepting biometric image data brought by the user on a USB memory stick or the like.

[0031] Various types of biometric image data are expected to be handled, including face image data, palm image data, vein image data, fingerprint image data, iris image data, and voice data images. For example, for face image data, face image data captured by an imaging camera is input. For example, for palm image data, palm image data scanned by a palm reader is input. For example, for vein image data, vein image data scanned by a vein reader is input. For example, for fingerprint image data, fingerprint image data scanned by a fingerprint reader is input. For example, for iris image data, iris image data scanned by an iris reader is input. For example, for voice data images, an image in which voice data of a fixed phrase input by a microphone is displayed as a frequency image is input.

[0032] Here, the biometric image data may be the entire biometric image data of the user captured by the camera 111 or the like, but may also be an image data portion obtained by cutting out a specific image region from the user's biometric image data. For example, the biometric image data acquisition module 110 may cut out the so-called T-zone, such as the forehead, eyes, and nose, as a specific image region for a face image, cut out the first to third joints of a specific finger, such as the index finger or middle finger, as a specific image region for a vein image, cut out a 2 cm square in the center of the pad of a specific finger, such as the index finger or middle finger, as a specific image region for a fingerprint image, or cut out the audio data of the pronunciation of a specific word as a specific data region for audio data, and acquire these specific image regions as biometric image data.

[0033] The biometric image spatial frequency conversion data generation module 120 converts the biometric image data acquired by the biometric image data acquisition module 110 into the spatial frequency domain to obtain biometric image spatial frequency conversion data. The biometric image spatial frequency conversion data generation module 120 includes a spatial frequency conversion tool 121, a frequency low-pass filter 122, a histogram processing module 123, and a scatter data creation processing module 124.

[0034] The spatial frequency transformation tool 121 is a program module that expands input biometric image data into a series in the frequency domain. Various spatial frequency transformation tools are possible. Any tool that processes a mathematical method for expanding biometric image data into a series in the frequency domain may be used, such as a Fourier transform tool or a wavelet transform tool. There are also various types of Fourier transform tools, such as a two-dimensional discrete Fourier transform and a fast Fourier transform (FFT). Here, a Fourier transform tool is used as the spatial frequency transformation tool 121 as an example.

[0035] Fourier series expansion is a well-established mathematical technique widely used in image engineering, including image recognition and image analysis. It is a mathematical engine that converts the structure of biometric image data into the frequency domain and expands it into a polynomial. The expansion result of the spatial frequency conversion tool 121 is that the image data is expanded for each frequency, and the frequency components and their numbers are obtained.

[0036] In this configuration example, the biometric image spatial frequency conversion data generation module 120 includes a frequency low-pass filter 122, and performs frequency low-pass processing by the frequency low-pass filter 122 to leave only data below a predetermined frequency and cut out data above the predetermined frequency from the spatial frequency conversion data obtained using the spatial frequency conversion tool 121. By cutting out the high-frequency components from the spatial frequency conversion data of the biometric image but leaving the low-frequency components, the characteristic elements of the biometric image are not lost and the data volume is reduced, which is an advantage.

[0037] Next, in this example, the biometric image spatial frequency conversion data generation module 120 includes a histogramming processing module 123, which converts the biometric image spatial frequency conversion data into a histogram. A histogram is created by aggregating and organizing data into classes (bins), each of which is a relatively prime interval. In this configuration example, the bin width applied by the histogramming processing module 123 is not limited, but as an example, the spatial frequency on the horizontal axis can be set to 3500. This histogramming process counts the number of occurrences of data obtained by Fourier transform that correspond to the spatial frequency of the set bin width as belonging to that bin. In the example described below, a histogram processing example is given in which histogram bins are set with a bin width of 0.000467 in the spatial frequency range of 0.00467 to 0.0060 after frequency low-pass processing, and the number of occurrences belonging to each bin is counted.

[0038] Next, the biometric image spatial frequency conversion data generation module 120 includes a scatter data creation processing module 124. The scatter data creation processing module 124 plots a scatter diagram with the spatial frequency bins set by the histogram processing module 123 on the horizontal axis and the data on the number of occurrences counted for each bin on the vertical axis. In this example, after conversion by the spatial frequency conversion tool 121, the biometric image spatial frequency conversion data generation module 120 performs low-pass processing by the frequency low-pass filter 122, histogram processing by the histogram processing module 123, and the count result of the number of occurrences of the scattered spatial frequencies by the scatter data creation processing module, as biometric image spatial frequency conversion data.

[0039] In the example described below, the results of the histogram processing (the number of occurrences was counted by setting histogram bins with a bin width of 0.000467 in the spatial frequency range of 0.00467 to 0.0060 after frequency low-pass filtering) are plotted as a scatter plot. Because the number of occurrences is large, it is also possible to count them on a logarithmic axis. In the example described below, plot results are obtained in the range of log 0.1 to log 4.0.

[0040] The biometric image sequence data extraction module 130 obtains, as biometric image sequence data, the numerical value of the number of occurrences for each frequency for the biometric image spatial frequency conversion data obtained by the biometric image spatial frequency conversion data generation module 120. The biometric image sequence data extraction module 130 includes a three-dimensional object determination processing module 131, a specific frequency band determination processing module 132, and a gridding processing module 133. Assuming that the biometric image data acquisition module 110 acquires a plurality of biometric images, namely biometric image data 1 and biometric image data 2, in this example, the biometric image spatial frequency conversion data generation process in the biometric image spatial frequency conversion data generation module 120 described above generates a scatter diagram plotting the spatial frequency and the number of occurrences for each image, as well as scatter data lines connecting the scatter data.

[0041] The biometric image sequence data extraction module 130 includes a three-dimensional object determination processing module 131. The three-dimensional object determination processing module 131 compares the scatter data derived from biometric image data 1 with the scatter data derived from biometric image data 2. If the count values ​​for the number of occurrences are the same across the entire frequency range, it can be determined that the biometric image data 1 and the biometric image data 2 were originally the exact same biometric image data. In other words, it can be determined that the captured image was a simple, two-dimensional object captured by pointing the camera at a separately prepared photograph. On the other hand, if the count values ​​for the number of occurrences differ across many frequency ranges, it can be determined that the biometric image data 1 and the biometric image data 2 were images obtained by live photography of a three-dimensional object. In this way, by acquiring multiple biometric images of a user and analyzing them using the three-dimensional object determination processing module 131, it can be determined whether the biometric images were naturally captured by photographing the user or whether some kind of fraudulent activity is being attempted by using photographs to impersonate the user.

[0042] The specific frequency band determination processing module 132 compares the scatter data line 1 derived from biometric image data 1 and the scatter data line 2 derived from biometric image data 2, plotted with frequency on the horizontal axis and number of occurrences on the vertical axis, to extract the frequency band where the two lines are close to each other and determine that frequency band as the specific frequency band. That is, the module finds a frequency band where a stable and close number of occurrences can be expected across multiple biometric images of the user, and determines that frequency band as the specific frequency band. The specific frequency band can be determined by selecting a point where the difference between the values ​​of the peaks and valleys in the scatter plot is small, noting that the peaks and valleys in the scatter plot represent the number of occurrences per frequency of the scatter data. It is preferable to identify the closest frequency band not only for two consecutive peaks and valleys in the scatter data across multiple biometric images, but also for three or more consecutive peaks and valleys. This is because three or more consecutive peaks and valleys are easier to evaluate as a stable frequency bandwidth than one or two consecutive peaks and valleys.

[0043] By determining the specific frequency band by the specific frequency band determination processing module 132, the biometric image sequence data extraction module 130 does not need to extract biometric image sequence data across the entire frequency range, but can focus on the number of occurrences of frequencies in the specific frequency band that can be expected to appear consistently across multiple biometric images. Extracting biometric image sequence data by narrowing down to this specific frequency band can improve the matching accuracy of personal authentication compared to matching across the entire frequency range.

[0044] The gridding processing module 133 performs gridding of the spatial frequency value on the horizontal axis and the number of occurrences, which is the frequency on the vertical axis, into discretized widths. The gridding referred to here means that the spatial frequency on the horizontal axis is divided into predetermined units by setting bins, while the vertical axis is the counted value of the number of occurrences. Because the number of occurrences is large, capturing the numerical value itself could result in a large impact on the biometric hash generation process and matching process described below. Therefore, the frequency of the number of occurrences on the vertical axis is also devised to be divided into certain widths. As a result, grids are set on both the horizontal and vertical axes, each divided into predetermined intervals. In this way, the gridding processing module 133 sets grids in the space in which the scatter data lines are drawn. Note that the actual processing involves numerical calculations by a computer to determine which grid each scatter point belongs to.

[0045] Then, the sequence data extraction module 130 extracts grids that both of them pass through in common across multiple biometric images in the scatter data of a specific frequency band drawn in the gridded space by the gridding processing module 133. In other words, the sequence data extracted by the sequence data extraction module 130 is the sequence data assigned to the grids that both of them pass through in common.

[0046] The biometric image sequence data extraction module 130 preferably also extracts check data. Here, the check data is the absolute values ​​of the upper and lower limits of a specific frequency band and the absolute value of the number of occurrences of the specific frequency band. Note that this check data is not converted into a hash value by the hash function tool 141, but is sent as an absolute value to the authentication server 200 of the cloud system 2 and used for authentication.

[0047] The biometric hash data generation module 140 includes a hash function tool 141, and uses the biometric image sequence data obtained by the sequence data extraction module 130 as input data, converts it into a hash value using the hash function tool 141, and uses the resulting data as biometric hash data. The hash value converted by the biometric hash data generation module 140 becomes a "registration biometric hash" in the registration phase and an "authentication biometric hash" in the identity authentication phase. When check data is sent from the sequence data extraction module 130, the biometric hash data generation module 140 does not convert the check data into a hash value using the hash function tool 141, but passes it directly to the registration biometric hash data transmission processing module 150. Using the check data as an auxiliary makes it possible to match numerical values, and even if the hash data converted into biometric hash data happens to be the same, comparing the numerical values ​​of the check data is thought to reduce misrecognition.

[0048] There are currently a variety of hash function tools 141 available, including SHA-256, SHA-512, MD5, and RIPEMD-160. SHA-256 and RIPEMD-160 are also used in blockchain. Generally, hash function tools have the property that "forward calculations are easy, but reverse calculations are difficult." By using the hash function tool 141 in the personal authentication application 100 of the present invention, this property of "one-way calculations are easy, but reverse calculations are difficult" is also achieved. This makes it difficult to reconstruct the original biometric image data from the biometric hash output by the personal authentication application 100, significantly enhancing the level of privacy protection and security for personal information protection. Furthermore, generally, hash function tools have the property that "even slight differences in input data result in conversion to completely different hash values." However, by using the hash function tool 141 in the personal authentication application 100 of the present invention, it is expected that there will be differences in the biometric image data of even twins. In other words, if the spoofing is only a minor disguise, the biometric hash obtained by the biometric hash data generation module 140 will be completely different, which significantly increases the security level.

[0049] If the numerical value of the frequency occurrence is used instead of using a grid that discretizes the frequency occurrence, differences may occur even for the same person, and the biometric hash value converted by the biometric hash data generation module 140 may be completely different each time. Therefore, by gridding the biometric image sequence data extracted by the biometric image sequence data extraction module 130 of the present invention as described above, the numerical value of the frequency occurrence is grouped by section, thereby maintaining a reasonable level of authentication accuracy.

[0050] The registered biometric hash data transmission processing module 150 transmits the registered biometric hash data generated by the biometric hash data generation module 140 to a storage device of a user terminal 1 such as a smartphone or a shared terminal or to a cloud system 2 on the network 3. It is preferable that the registered biometric hash data transmission processing module 150 also transmits check data.

[0051] The above is a description of the components of the personal authentication application 100. Next, we will describe the components of the authentication server 200 of the cloud system 2. As shown in Fig. 1, the authentication server 200 is configured to include an authentication registration database 210, a registration processing module 220, a personal authentication matching processing module 230, and a data communication module 240.

[0052] The authentication registration database 210 may be any device that stores data, and may be, but is not limited to, a hard disk, a large-scale memory device, etc. The registration processing module 220 records, registers, and manages the registration biometric hash data transmitted from the personal authentication application 100 in the storage device of the user terminal 1, such as a smartphone or a shared terminal, or in the authentication registration database 210 of a server in the cloud system 2 on the network 3.

[0053] The personal authentication matching processing module 230 performs matching processing and executes personal authentication processing by comparing the registration biometric hash data generated by the personal authentication application 100 at the time of registration and registered in the authentication registration database 210 by the registration processing module 220 with the personal authentication biometric hash data generated by the personal authentication application 100 at the time of authentication and acquired via the data communication module 240. If the frequency band is limited to a specific frequency band, the registration biometric hash data generated within the range of the specific frequency band is compared with the personal authentication biometric hash data, and the matching processing is executed.

[0054] Here, when a specific frequency band is selected for identity authentication processing, the identity authentication matching processing module 230 can also check the matching process with check data to improve identity authentication accuracy. The check data is the absolute values ​​of the upper and lower limits of the specific frequency band and the absolute value of the number of occurrences of the specific frequency band. This check data is not converted into a hash value by the hash function tool 141, so matching between numerical values ​​is possible. Even if the converted hash data happens to be the same as the biometric hash data, comparing the numerical values ​​of this check data is thought to reduce misrecognition. Conversely, even if the biometric image sequence data generated from the individual's biometric image contains subtle differences, resulting in different biometric hash data, comparing the numerical values ​​of the check data is thought to reduce misrecognition.

[0055] The data communication module 240 executes data communication processing on the network 3, and data can be exchanged with the personal authentication application 100 via this data communication module 240. This concludes the description of each component of the example configuration of the personal authentication application 100 and the authentication server 200 of the cloud system 2 shown in FIG.

[0056] Next, using specific user images, we will explain the registration phase of biometric hash data using a registered biometric image in the authentication application 100 and the authentication server 200 of the cloud system 2, and the authentication phase of biometric hash data using an authentication biometric image.

[0057] [Processing Flow and Example of Biometric Hash Data Registration Phase] The processing flow and example of the biometric hash data registration phase will be described. The registrant inputs biometric image data using the biometric image data acquisition module 110 (registrant biometric image data input step). In this example, the user takes and inputs multiple (two) facial images as biometric image data for registration during registration. The user takes live facial images via a camera, which is a device 111 controlled by the biometric image data acquisition module 110.

[0058] Figure 2 shows examples of facial images taken and input by user 1 in the registration phase. (a) is facial image 1 of user 1, and Figure 2(b) is facial image 2 of user 1. At first glance, there appear to be few differences, but these are consecutive photographs, and the facial posture and facial expression are almost identical. This is because consecutive photographs are a prerequisite for practical use, and this example is appropriate for demonstration purposes.

[0059] Next, the biometric image spatial frequency conversion data generation module 120 performs a biometric image spatial frequency conversion process on each of the two sets of facial image data, which are the biometric images acquired in Figure 2. In this processing example, frequency low-pass processing, histogramming, and scatter data creation processes are also performed. Figure 3 displays the processing results by the biometric image spatial frequency conversion data generation module 120 plotted as a scatter diagram, with the horizontal axis representing spatial frequency grouped into bins and the vertical axis representing the number of occurrences of that frequency component. The vertical axis is a logarithmic scale.

[0060] 3, when the graphs derived from the two facial images 1 and 2 of user 1 are checked, it can be seen that the two graphs are very similar, but there are differences between them. The three-dimensional object determination processing module 131 of the biometric image sequence data extraction module 130 determines that the subject has changed subtly during the shooting time of the continuous photographs, resulting in slight changes in the posture angle and expression of the face, and therefore determines that the images are of a three-dimensional face.

[0061] The specific frequency band determination processing module 132 compares the plot results of the scatter data line derived from facial image data 1 of user 1, which is drawn with frequency on the horizontal axis and the number of occurrences on the vertical axis, with the scatter data line derived from facial image data 2, extracts frequency bands where the two are close to each other at three or more consecutive peaks and valleys, and determines these frequency bands as the specific frequency bands. In reality, the specific frequency band determination processing module 132 determines the specific frequency band by performing numerical calculations using computer processing, but in the visualized Figure 3, it can be seen that the plot results for three consecutive points in the frequency bands from 0.004787 to 0.005907 are very close to each other. In this example, the frequency band from 0.004787 to 0.005533 is determined as the specific frequency band.

[0062] Fig. 4 is an enlarged view of the graph in a specific frequency band in Fig. 3. Next, the biometric image sequence data extraction module 130 uses the gridding processing module 133 to grid the space plotted as a scatter plot, with the horizontal axis representing spatial frequency grouped into bins and the vertical axis representing the number of occurrences of that frequency component. In reality, the biometric image sequence data extraction module 130 performs this numerical calculation by computer processing. In the visualized view in Fig. 4, the enlarged view shows that there is a difference in the number of occurrences of both, but it is also clear that they belong to the same grid squares that have been set.

[0063] 5 is a graph of a specific frequency band in which hatching is applied to portions that belong to the same frequency bin on the horizontal axis and to grids with the same number of occurrences on the vertical axis. In practice, the biometric image sequence data extraction module 130 extracts those that pass through the grids with the same number of occurrences through numerical calculations in computer processing. The hatched portions in the visualized FIG. 5 are expressed as squares, and when sets of discrete frequency values ​​and discrete number of occurrences assigned to these 10 squares are extracted as biometric image sequence data, they become (0.004787:0.6625), (0.004880:0.6625), (0.004973:0.6625), (0.005067:0.5500), ... In other words, the biometric image sequence data is a sequence consisting of 20 numerical values ​​of 10 grids: "0.0047870.66250.0048800.66250.0049730.66250.0050670.5500..." Furthermore, if the lower and upper limits of the range of the specific frequency band and its occurrence count are extracted as check data, the four numerical values ​​of 0.004600, 0.005533, 0.4375, and 0.6625 become the check data.

[0064] Next, the biometric hash data generation module 140 uses the hash function tool 141 to calculate a hash value from the biometric image sequence data of 20 numeric values, generating registered biometric hash data. In this example, if SHA256 is used as the hash function, the resulting biometric hash data is "a4133c5fa2bcd0083b00884ac26b5399985e5013eb035b5d8fd173f32ca1a2e5...".

[0065] The registration processing module 220 of the authentication server 200 records and registers the registration biometric hash data sent from the personal authentication application 100 in the authentication registration database 210 of the server in the cloud system 2 on the network 3. The above is the flow of the biometric hash data registration process in the registration phase.

[0066] [Processing Flow and Example of the Biometric Hash Data Authentication Phase] Next, a processing flow and example of the biometric hash data authentication phase will be described. A user to be authenticated inputs biometric image data using the biometric image data acquisition module 110 (authenticator biometric image data input step). Here, the user similarly captures and inputs multiple (two) facial images as biometric image data for authentication during authentication. The user captures live facial images via a camera, which is a device 111 controlled by the biometric image data acquisition module 110. Figure 6 shows an example of a facial image captured and input by user 1 in the authentication phase. Figure 6(a) shows facial image 1 of user 1, and Figure 6(b) shows facial image 2 of user 1.

[0067] Next, the biometric image spatial frequency conversion data generation module 120 performs a biometric image spatial frequency conversion process on each of the two facial image data, which are the biometric images acquired in Figure 6. In this processing example, frequency low-pass processing, histogramming, and scatter data creation processes are also performed. Figure 7 displays the processing results by the biometric image spatial frequency conversion data generation module 120 plotted as a scatter plot, with the horizontal axis representing spatial frequency grouped into bins and the vertical axis representing the number of occurrences of that frequency component. The vertical axis is logarithmic. Since the biometric image spatial frequency conversion results shown in Figure 7 indicate a difference between the two, the three-dimensional object determination processing module 131 determines that the multiple images input during authentication are images of a three-dimensional face.

[0068] The specific frequency band determination processing module 132 compares the plot results of the scatter data line derived from facial image data 1 of user 1, which is drawn with frequency on the horizontal axis and the number of occurrences on the vertical axis, with the scatter data line derived from facial image data 2, extracts the frequency band where the two are close to each other at three or more consecutive peaks and valleys, and determines that frequency band as the specific frequency band. In the visualized example in Figure 7, it can be seen that the plot results for three consecutive points in the frequency band from 0.004787 to 0.005533 are very close to each other. In this example, the frequency band from 0.004787 to 0.005533 is determined as the specific frequency band.

[0069] Fig. 8 is an enlarged view of the graph in a specific frequency band in Fig. 7. The gridding processing module 133 grids the space plotted as a scatter plot, with the horizontal axis representing spatial frequency grouped into bins and the vertical axis representing the number of occurrences of that frequency component. In reality, the biometric image sequence data extraction module 130 performs numerical calculations in computer processing. In the visualized view in Fig. 8, the enlarged view shows that there is a difference in the number of occurrences of both, but it is also clear that they belong to the same grid squares that have been set.

[0070] 9 is a graph of a specific frequency band, in which hatching is applied to portions that belong to the same frequency bin on the horizontal axis and the same number of occurrences on the vertical axis. The hatched portions in the visualized Fig. 9 are expressed as squares, and when the sets of discrete frequency values ​​and discrete occurrences assigned to these 20 squares are extracted as biometric image sequence data, they become (0.004787:0.6625), (0.004880:0.6625), (0.004973:0.6625), (0.005067:0.5500), .... In other words, the biometric image sequence data is a sequence consisting of 20 numerical values ​​of 10 grids: "0.0047870.66250.0048800.66250.0049730.66250.0050670.5500...." Furthermore, when the lower and upper limits of the range of the specific frequency band and the number of occurrences thereof are extracted as check data, the four numerical values ​​of 0.004600, 0.005533, 0.4375, and 0.6625 become the check data.

[0071] Next, the biometric hash data generation module 140 uses the hash function tool 141 to calculate a hash value from the biometric image sequence data for a sequence of 20 numbers, generating biometric authentication hash data. Here, SHA256 is used as the hash function, as was used during enrollment. The resulting biometric hash data is "a4133c5fa2bcd0083b00884ac26b5399985e5013eb035b5d8fd173f32ca1a2e5...". The authentication matching processing module 230 of the authentication server 200 compares the biometric authentication hash data sent from the authentication application 100 with the enrolled biometric hash data registered in the authentication enrollment database 210, performs matching processing, and executes the authentication process. In this example, the biometric hash data matches, and the check data also matches the four numbers 0.004600, 0.005533, 0.4375, and 0.6625, so the person is authenticated and the results are output and reported to the management system.

[0072] In the personal authentication application 100 of the present invention, only biometric hash data is used in both the registration phase and the authentication phase, and the biometric data itself, such as the user's facial image, is not stored anywhere, thereby maintaining a high level of personal information protection.

[0073] Next, an example will be described in which it is determined that a person cannot be authenticated when a different person poses as the person and performs identity authentication. User 2, who is a different person, inputs biometric image data using biometric image data acquisition module 110 (authenticator biometric image data input step). Here, the user similarly captures and inputs multiple (two) facial images as biometric image data for authentication during authentication. The user captures live facial images via a camera, which is a device 111 controlled by biometric image data acquisition module 110. Figure 10 shows an example of a facial image captured and input by user 2 (different person) during the authentication phase. Figure 10(a) shows facial image 1 of user 2 (different person), and Figure 10(b) shows facial image 2 of user 2 (different person).

[0074] Next, the biometric image spatial frequency conversion data generation module 120 performs a biometric image spatial frequency conversion process on each of the two facial image data, which are the biometric images acquired in Figure 10. In this processing example, frequency low-pass processing, histogramming, and scatter data creation processes are also performed. Figure 11 displays the processing results of the biometric image spatial frequency conversion data generation module 120 from the facial image of user 2 (another person), plotted as a scatter plot with the horizontal axis representing spatial frequency grouped into bins and the vertical axis representing the number of occurrences of that frequency component. The vertical axis is logarithmic.

[0075] 11 shows a difference between the two images, so the three-dimensional object determination processing module 131 determines that the multiple images input during authentication are images of a three-dimensional face. If a photograph of the face of user 1, who is the person in question, is held up to the camera 111 and input, the three-dimensional object determination processing module 131 will detect fraudulent input.

[0076] The specific frequency band determination processing module 132 compares the plot results of the scatter data line derived from facial image data 1 of user 1, which is drawn with frequency on the horizontal axis and the number of occurrences on the vertical axis, with the scatter data line derived from facial image data 2, extracts a frequency band where the two are close to each other at three or more consecutive peaks and valleys, and determines that frequency band as the specific frequency band. In the visualized example in Figure 11, it can be seen that the plot results for three consecutive points in the frequency band from 0.004787 to 0.005533 are very close to each other. In this example, the frequency band from 0.004787 to 0.005533 is determined as the specific frequency band.

[0077] Fig. 12 is an enlarged view of the graph in a specific frequency band in Fig. 11. The gridding processing module 133 grids the space plotted as a scatter plot, with the horizontal axis representing spatial frequency grouped into bins and the vertical axis representing the number of occurrences of that frequency component. In reality, the biometric image sequence data extraction module 130 performs numerical calculations in computer processing. In the visualized view in Fig. 12, the enlarged view reveals that there is a difference in the number of occurrences of both, but it is also clear that they belong to the same grid squares that have been set.

[0078] Fig. 13 is a graph of a specific frequency band, in which hatching is applied to portions that belong to the same frequency bin on the horizontal axis and the same number of occurrences on the vertical axis. The hatched portions in the visualized Fig. 13 are expressed as squares, and when the sets of discrete frequency values ​​and discrete number of occurrences assigned to these three squares are extracted as biometric image sequence data, they become (0.004880:0.5500), (0.004973:0.5500), and (0.005067:0.5500). In other words, the sequence "0.0048800.55000.0049730.55000.0050670.5500" consisting of six numerical values ​​of the three grids becomes the biometric image sequence data. Furthermore, when the lower and upper limits of the range of the specific frequency band and its occurrence number are extracted as check data, the four numerical values ​​of 0.004787, 0.4375, 0.005067, and 0.5500 become the check data.

[0079] Next, the biometric hash data generation module 140 uses the hash function tool 141 to calculate a hash value from the six-digit sequence of biometric image sequence data, generating biometric authentication hash data. Here, SHA256 is used as the hash function. The biometric hash data "62f668c8463a310d21b6e4de64b3566960f404586f1ec0678de018f843449bbb" is obtained and sent to the authentication server 200. The authentication matching processing module 230 of the authentication server 200 compares the biometric authentication hash data sent from the authentication application 100 with the registered biometric hash data registered in the authentication registration database 210, performs matching processing, and executes the authentication process. In this case, the matching process is not successful, and the check data also does not match the four numbers 0.004600, 0.005533, 0.4375, and 0.6625 with the four numbers 0.004787, 0.4375, 0.005067, and 0.5500, so it is determined that the person cannot be authenticated, and this result is output and reported to the management system.The above is the processing flow when identity authentication is not successful for a different person.

[0080] Next, a brief description will be given of a business application 300 to which the identity authentication application 100 of the present invention can be applied. The business application 300 is not particularly limited, and various main business applications are envisioned. Examples include medical consultation applications at medical institutions, payment applications for mail-order and in-store shopping, payments for various services, service usage applications at financial institutions, building entry and exit applications, home healthcare applications (acquiring lifestyle information and health data within a home, etc.), and logistics applications (delivery and receipt). Note that various other business applications besides those listed above can also serve as the main business application 300. The identity authentication application 100 of the present invention can be applied to processing procedures requiring eKYC (electronic Know Your Customer) in these business applications 300. In other words, the identity authentication application 100 of the present invention can also be used as an eKYC application.

[0081] The present invention can be widely applied as an identity authentication application or eKYC application that runs on a computer system. It can also be widely applied to a variety of business applications, such as business applications that require identity authentication when logging in and logging out.

Claims

1. An identity authentication application usable on a computer system, comprising: a biometric image data acquisition module that acquires a user's image data or an image data portion extracted from a specific area of ​​the image data as biometric image data; a biometric image spatial frequency converted data generation module that has a spatial frequency conversion tool and converts the biometric image data into the spatial frequency domain using the spatial frequency conversion tool to obtain biometric image spatial frequency converted data; a biometric image sequence data extraction module that obtains the numerical value of the number of occurrences for each frequency from the obtained biometric image spatial frequency converted data as biometric image sequence data; and a biometric hash data generation module that has a hash function tool and uses the biometric image sequence data obtained by the sequence data extraction module as input data and converts it into a hash value to obtain biometric hash data.

2. The identity authentication application described in claim 1, characterized in that when the user inputs the biometric image data at the time of registration, the biometric image spatial frequency conversion data generation module executes a registration time conversion data generation process in which the spatial frequency conversion data obtained from the biometric image data using the spatial frequency conversion tool is converted into biometric image spatial frequency conversion data for registration, the biometric image sequence data extraction module executes a registration biometric image sequence data extraction process in which the numerical value of the number of occurrences for each frequency is extracted from the biometric image spatial frequency conversion data for registration as the biometric image sequence data, and the biometric hash data generation module executes a registration biometric hash data generation process in which the biometric image sequence data obtained at the time of registration is converted into a hash value to be converted into registration biometric hash data, and a registration biometric hash data transmission process in which the generated registration biometric hash data is transmitted to a storage device of the user terminal or to a database of a cloud system on a network.

3. The application for personal authentication according to claim 2, characterized in that, when the user inputs the biometric image data during identity authentication, the biometric image spatial frequency conversion data generation module executes a conversion data generation process during identity authentication in which the spatial frequency conversion data obtained from the biometric image data using the spatial frequency conversion tool is converted into biometric image spatial frequency conversion data for identity authentication, the biometric image sequence data extraction module executes a biometric image sequence data extraction process during identity authentication in which the numerical value of the number of occurrences for each frequency is extracted from the biometric image spatial frequency conversion data for identity authentication as the biometric image sequence data for identity authentication, the biometric hash data generation module executes an authentication biometric hash data generation process in which the biometric image sequence data for identity authentication is converted into a hash value to generate authentication biometric hash data, and an authentication biometric hash data transmission process is executed in which the generated authentication biometric hash data is transmitted to the storage device of the user terminal or to the database of the cloud system on the network.

4. The personal authentication application of claim 3, wherein the storage device of the user terminal or the server on the cloud comprises a registration processing module that stores the registration biometric hash data in the storage device of the user terminal or in the database of the server on the cloud, and an authentication matching processing module that receives the authentication biometric hash data generated at the time of the personal authentication from the user and performs a matching process with the registration biometric hash data stored in the storage device of the user terminal or in the database of the server on the cloud, thereby executing the personal authentication process.

5. The personal authentication application according to claim 4, characterized in that the biometric image spatial frequency conversion data generation module is provided with a frequency low-pass filter, and performs frequency low-pass processing to leave only data below a predetermined frequency of the frequency low-pass filter from the obtained spatial frequency conversion data, and cut out data exceeding the predetermined frequency.

6. The personal authentication application according to claim 5, characterized in that the frequencies to be counted in the extraction process of the biometric image sequence data by the biometric image sequence data extraction module are discretized, and the count results of the number of occurrences for each discretized frequency are also compiled into discretized discrete spatial frequency transformation values.

7. The authentication application as described in claim 6, characterized in that the biometric image data for registration input by the user at the time of registration is multiple pieces of photographed data taken in succession, the biometric image data for identity authentication input by the user at the time of identity authentication is multiple pieces of photographed data taken in succession, the registration biometric hash data is generated using the multiple pieces of biometric image data for registration at the time of registration, and the authentication biometric hash data is generated using the multiple pieces of biometric image data for identity authentication at the time of identity authentication.

8. The personal authentication application described in claim 7, characterized in that in the biometric image sequence data extraction module, in processing each of the data derived from a plurality of biometric images, a specific frequency band in which the number of occurrences counted for each of the discretized frequencies is the same discrete value is selected, and the discrete value of the number of occurrences in the specific frequency band is obtained as the biometric image sequence data, and the biometric hash data generation module generates the biometric hash data from the biometric image sequence data corresponding to the specific frequency band.

9. The personal authentication application described in claim 8, characterized in that the biometric image sequence data extraction module, in processing each of the data derived from a plurality of biometric images, is further provided with a three-dimensional object determination processing module which determines that the input photographic data is a plurality of photographic data taken in succession from a planar object if the number of occurrences counted across the entire range of the discretized frequencies is the same discrete value, and determines that the input photographic data is a plurality of photographic data taken in succession from a three-dimensional object if the number of occurrences counted at some frequencies is a different discrete value.

10. The personal authentication application described in claim 9, characterized in that the biometric hash data generation module outputs, in addition to the generated biometric hash data for the specific frequency band, the absolute values ​​of the upper and lower limits of the specific frequency band and the absolute values ​​of the upper and lower limits of the number of occurrences of the specific frequency band as check data, the registration processing module in the user terminal or the server on the cloud registers the check data in addition to the biometric hash data, and the personal authentication matching processing module performs matching processing of the check data in addition to matching processing of the registered biometric hash data and the authentication biometric hash data to execute personal authentication processing.

11. An authentication application according to any one of claims 1 to 10, characterized in that the spatial frequency transformation tool is a Fourier transformation tool or a wavelet transformation tool.

12. An identity authentication application as described in any one of claims 1 to 10, characterized in that when the biometric image data acquisition module acquires the image data portion cut out from the specific area of ​​the image data as the biometric image data, the specific area is a facial image portion including at least the user's eyes and nose.

13. The personal authentication application according to any one of claims 1 to 10, wherein the biometric image data is any one of a face image, a palm image, a vein image, a fingerprint image, an iris image, and a voice data image, or a combination thereof.

14. An identity authentication application as described in any one of claims 1 to 10, characterized in that the biometric hash data generation module is provided with multiple different types of hash function tools as the hash function tool, and even if the input biometric image sequence data is the same, the conversion result into the biometric hash data will be different if the hash function tool is different, and the biometric hash data generation module is provided with a hash function tool selection processing step for selecting one to be used from the multiple types of hash function tools.

15. A business application incorporating an identity authentication application as claimed in any one of claims 1 to 10, wherein said login is possible on the condition that said user has been authenticated by said identity authentication application at the time of login.

16. The business application according to claim 15, characterized in that logout is possible not only at the time of login but also at the time of logout, on the condition that the user's identity is authenticated by the identity authentication application.

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