Estimation device, estimation method, and recording medium

Abstract

This estimation device comprises: an acquisition means which acquires a rotated fingerprint image obtained by rotating a finger; and an estimation means which estimates the type of the finger included in the rotated fingerprint image by inputting the acquired rotated fingerprint image to a trained model. According to such an estimation device, it is possible to estimate, with high accuracy, the type of the finger included in the rotated fingerprint image.

Description

Estimation device, estimation method, and recording medium

[0001] The present disclosure relates to the technical fields of an estimation device, an estimation method, and a recording medium.

[0002] Devices that perform various processes on fingerprint images are known. For example, Patent Document 1 discloses a technique for correcting the tilt of a fingerprint image when matching the image. Patent Document 2 discloses a technique for determining the finger type of a fingerprint image and detecting an error in the finger type imprint. Patent Document 3 discloses a technique for identifying an abnormal region in a fingerprint using a machine-learned learning model.

[0003] JP 2001-076144 A JP 2016-053989 A JP 2018-165911 A

[0004] This disclosure aims to improve upon the techniques disclosed in the prior art documents.

[0005] One aspect of the estimation device disclosed herein includes an acquisition means for acquiring a rotated fingerprint image captured by rotating a finger, and an estimation means for estimating the type of finger contained in the rotated fingerprint image by inputting the acquired rotated fingerprint image into a trained model.

[0006] One aspect of the estimation method of this disclosure involves using at least one computer to acquire a rotated fingerprint image captured by rotating a finger, and inputting the rotated fingerprint image into a trained model to estimate the type of finger contained in the rotated fingerprint image.

[0007] One aspect of the recording medium of this disclosure is a recording medium having recorded thereon a computer program for causing at least one computer to execute an estimation method for acquiring a rotated fingerprint image captured by rotating a finger, and inputting the rotated fingerprint image into a trained model, thereby estimating the type of finger contained in the rotated fingerprint image.

[0008] 1. A block diagram showing the hardware configuration of the estimation device according to the first embodiment. 2. A block diagram showing the functional configuration of the estimation device according to the first embodiment. 3. A flowchart showing the operation flow of the estimation device according to the second embodiment. 4. A flowchart showing the operation flow of the estimation device according to the second embodiment. 5. A block diagram showing the functional configuration of the estimation device according to the third embodiment. 6. A flowchart showing the operation flow of the estimation device according to the third embodiment. 7. A block diagram showing the functional configuration of the estimation device according to the fourth embodiment. 8. A flowchart showing the operation flow of the estimation device according to the fourth embodiment. 9. A block diagram showing the functional configuration of the estimation device according to the fifth embodiment. 10. A flowchart showing the operation flow of the estimation device according to the fifth embodiment. 11. A block diagram showing the functional configuration of the estimation device according to the sixth embodiment. 12. A flowchart showing the operation flow of the estimation device according to the sixth embodiment. 13. A flowchart showing the operation flow of the estimation device according to a modified example of the sixth embodiment. 14. A schematic diagram showing the hardware configuration of the estimation device according to the seventh embodiment.

[0009] Hereinafter, embodiments of an estimation device, an estimation method, and a recording medium will be described with reference to the drawings.

[0010] First Embodiment An estimation device according to a first embodiment will be described with reference to FIGS. 1 to 3. FIG.

[0011] (Hardware Configuration) First, the hardware configuration of the estimation device according to the first embodiment will be described with reference to Fig. 1. Fig. 1 is a block diagram showing the hardware configuration of the estimation device according to the first embodiment.

[0012] 1 , an estimation device 10 according to the first embodiment includes a processor 11, a RAM (Random Access Memory) 12, and a ROM (Read Only Memory) 13. The estimation device 10 may further include a storage device 14, an input device 15, and an output device 16. The processor 11, RAM 12, ROM 13, storage device 14, input device 15, and output device 16 are connected to each other via a data bus 17.

[0013] The processor 11 loads a computer program. For example, the processor 11 is configured to load a computer program stored in at least one of the RAM 12, the ROM 13, and the storage device 14. Alternatively, the processor 11 may load a computer program stored in a computer-readable storage medium using a storage medium reading device (not shown). The processor 11 may acquire (i.e., load) the computer program from a device (not shown) located outside the estimation device 10 via a network interface. The processor 11 controls the RAM 12, the storage device 14, the input device 15, and the output device 16 by executing the loaded computer program. In particular, in this embodiment, when the processor 11 executes the loaded computer program, a functional block for acquiring a rotated fingerprint image and estimating a finger type is realized within the processor 11. In other words, the processor 11 may function as a controller that executes each control in the estimation device 10.

[0014] The processor 11 may be configured as, for example, a central processing unit (CPU), a graphics processing unit (GPU), a field-programmable gate array (FPGA), a digital signal processor (DSP), an application-specific integrated circuit (ASIC), or a quantum processor. The processor 11 may be configured as one of these, or may be configured to use multiple processors in parallel.

[0015] The RAM 12 temporarily stores computer programs executed by the processor 11. The RAM 12 temporarily stores data that the processor 11 temporarily uses while it is executing the computer programs. The RAM 12 may be, for example, a dynamic random access memory (D-RAM) or a static random access memory (SRAM). Alternatively, other types of volatile memory may be used instead of the RAM 12.

[0016] The ROM 13 stores computer programs executed by the processor 11. The ROM 13 may also store fixed data. The ROM 13 may be, for example, a programmable read-only memory (PROM) or an erasable read-only memory (EPROM). Alternatively, other types of non-volatile memory may be used instead of the ROM 13.

[0017] The storage device 14 stores data that is to be saved long-term by the estimation device 10. The storage device 14 may operate as a temporary storage device for the processor 11. The storage device 14 may include, for example, at least one of a hard disk device, a magneto-optical disk device, an SSD (Solid State Drive), and a disk array device.

[0018] The input device 15 is a device that receives input instructions from a user of the estimation device 10. The input device 15 may include, for example, at least one of a keyboard, a mouse, and a touch panel. The input device 15 may be configured as a mobile terminal such as a smartphone or a tablet. The input device 15 may also be, for example, a device that includes a microphone and is capable of voice input.

[0019] The output device 16 is a device that outputs information related to the estimation device 10 to the outside. For example, the output device 16 may be a display device (e.g., a display) that can display information related to the estimation device 10. The output device 16 may be configured as a mobile terminal such as a smartphone or a tablet. The output device 16 may also be a device that outputs information in a format other than an image. For example, the output device 16 may be a speaker that outputs information related to the estimation device 10 as sound.

[0020] 1 may be provided as an external device of the estimation device 10. For example, the estimation device 10 may be configured to include only the processor 11, RAM 12, and ROM 13 described above, and the other components (i.e., the storage device 14, the input device 15, and the output device 16) may be provided in an external device connected to the estimation device 10. Furthermore, some of the calculation functions of the estimation device 10 may be realized by an external device (e.g., an external server, a cloud, etc.).

[0021] (Functional Configuration) Next, the functional configuration of the estimation device 10 according to the first embodiment will be described with reference to Fig. 2. Fig. 2 is a block diagram showing the functional configuration of the estimation device according to the first embodiment.

[0022] 2, the estimation device 10 according to the first embodiment is configured to include, as components for realizing its functions, a fingerprint image acquisition unit 110 and a finger type estimation unit 120. Each of the fingerprint image acquisition unit 110 and the finger type estimation unit 120 may be a processing block realized by, for example, the above-mentioned processor 11 (see FIG. 1).

[0023] The fingerprint image acquisition unit 110 is configured to be able to acquire a rotated fingerprint image. A rotated fingerprint image is a fingerprint image acquired by rotating a finger, and includes fingerprints on the side of the finger as well as the pad of the finger. The fingerprint image acquisition unit 110 may directly acquire a rotated fingerprint image acquired by, for example, a scanner, or may acquire a rotated fingerprint image stored in a database or the like (i.e., a rotated fingerprint image acquired in the past). The rotated fingerprint image acquired by the fingerprint image acquisition unit 110 is configured to be output to the finger type estimation unit 120.

[0024] The finger type estimation unit 120 is configured to be able to estimate the type of finger (hereinafter referred to as "finger type" as appropriate) included in the rotated fingerprint image acquired by the fingerprint image acquisition unit 110. The finger type estimation unit 120 estimates the finger type by inputting the rotated fingerprint image to a trained model (hereinafter referred to as "trained model" as appropriate). The trained model is a model trained in advance using training data, and when a rotated fingerprint image is input, it outputs the finger type of the rotated fingerprint image. The trained model may be, for example, a model trained by machine learning by inputting pair information of a fingerprint image and a finger type, which is training data. The trained model may be, for example, a neural network trained by deep learning.

[0025] When training a learning model, it is preferable to prepare as much training data as possible in order to improve the accuracy of finger type estimation. However, if the amount of training data that can be prepared is small, the amount of training data may be increased, for example, by generating data similar to the training data or by applying perturbations to the training data. In this case, the training data may be generated using, for example, a generative adversarial network (GAN).

[0026] (Operational Flow) Next, the operational flow of the estimation device 10 according to the first embodiment will be described with reference to Fig. 3. Fig. 3 is a flowchart showing the operational flow of the estimation device according to the first embodiment.

[0027] 3, when the operation of the estimation device 10 according to the first embodiment is started, the fingerprint image acquisition unit 110 first acquires a rotated fingerprint image (step S101). Then, the finger type estimation unit 120 inputs the rotated fingerprint image acquired by the fingerprint image acquisition unit 110 into a learning model (step S102).

[0028] Next, the type of finger included in the rotated fingerprint image is estimated based on the rotated fingerprint image into which the learning model has been input (step S103).Then, the finger type estimation unit 120 outputs finger type information indicating the type of finger included in the rotated fingerprint image based on the output of the learning model (step S104).The finger type information output by the finger type estimation unit 120 may be stored (registered) in association with the rotated fingerprint image, for example.The registration process using finger type information will be described in detail in another embodiment below.

[0029] (Technical Effects) Next, technical effects obtained by the estimation device 10 according to the first embodiment will be described.

[0030] 1 to 3, the estimation device 10 according to the first embodiment estimates the type of finger contained in a rotated fingerprint image by inputting the rotated fingerprint image into a learning model. Finger type estimation based on a rotated fingerprint image alone is not easy, even for a forensic examiner who specializes in fingerprints. However, the estimation device 10 according to this embodiment makes it possible to estimate the type of finger contained in a rotated fingerprint image with high accuracy.

[0031] Second Embodiment An estimation device 10 according to a second embodiment will be described with reference to Figures 4 and 5. The second embodiment differs from the first embodiment described above only in part of the configuration and operation, and other parts may be the same as the first embodiment. Therefore, the following will describe in detail parts that differ from the first embodiment already described, and will omit descriptions of other overlapping parts as appropriate.

[0032] (Functional Configuration) First, the functional configuration of the estimation device 10 according to the second embodiment will be described with reference to Fig. 4. Fig. 4 is a block diagram showing the functional configuration of the estimation device according to the second embodiment. Note that in Fig. 4, the same elements as those shown in Fig. 2 are denoted by the same reference numerals.

[0033] As shown in Fig. 4, the estimation device 10 according to the second embodiment is configured to include, as components for realizing its functions, a fingerprint image acquisition unit 110, a finger type estimation unit 120, a fingerprint image storage unit 130, and a registered finger type determination unit 140. That is, the estimation device 10 according to the second embodiment further includes, in addition to the configuration of the first embodiment (see Fig. 2), a fingerprint image storage unit 130 and a registered finger type determination unit 140. Each of the fingerprint image storage unit 130 and the registered finger type determination unit 140 may be realized by, for example, the above-mentioned processor 11 or the storage device 14 (see Fig. 1).

[0034] The fingerprint image storage unit 130 is configured to be able to store rotated fingerprint images in association with finger types. For example, the fingerprint image storage unit 130 stores a rotated fingerprint image collected from a thumb by adding information indicating that the image is a "thumb." The fingerprint image storage unit 130 may be configured to be able to store rotated fingerprint images of multiple fingers in association with finger types. For example, the fingerprint image storage unit 130 may be configured to store rotated fingerprint images of ten fingers on both hands, each associated with a finger type. Furthermore, the fingerprint image storage unit 130 may be configured to store planar fingerprint images (i.e., fingerprint images of the pads of the fingers that can be collected without rotating the fingers) in addition to rotated fingerprint images. In this case, the planar fingerprint images may also be able to be stored in association with finger types. Furthermore, the fingerprint image storage unit 130 may be configured to store rotated fingerprint images for each user. For example, the fingerprint image storage unit 130 may be configured to store ten rotated fingerprint images of both hands of user A, ten rotated fingerprint images of both hands of user B, and ten rotated fingerprint images of both hands of user C. The rotated fingerprint images stored in the fingerprint image storage unit 130 can be read out by the registered finger type determination unit 140 as needed.

[0035] The fingerprint image storage unit 130 may be configured as a database for storing fingerprint images used in a fingerprint matching system, for example. More specifically, the fingerprint image storage unit 130 may be configured as a database for a fingerprint matching system managed by the police.

[0036] The registered finger type determination unit 140 is configured to be able to determine whether or not the finger type stored in the fingerprint image storage unit 130 (i.e., the finger type stored in association with the rotated fingerprint image) matches the finger type estimated by the finger type estimation unit 120 (i.e., the finger type estimated by inputting the rotated fingerprint image into a learning model).The registered finger type determination unit 140 is configured to be able to output information indicating that the finger type stored in the fingerprint image storage unit 130 is incorrect when the finger type stored in the fingerprint image storage unit 130 does not match the finger type estimated by the finger type estimation unit 120.

[0037] (Operation Flow) Next, the operation flow of the estimation device 10 according to the second embodiment will be described with reference to Fig. 5. Fig. 5 is a flowchart showing the operation flow of the estimation device according to the second embodiment. Note that in Fig. 5, the same processes as those described in Fig. 3 are denoted by the same reference numerals.

[0038] As shown in FIG. 5, when the operation of the estimation device 10 according to the second embodiment is started, the fingerprint image acquisition unit 110 first acquires a rotated fingerprint image stored in the fingerprint image storage unit 130 (step S201).

[0039] Next, the finger type estimation unit 120 inputs the rotated fingerprint image acquired by the fingerprint image acquisition unit 110 into the learning model (step S102).Then, based on the rotated fingerprint image input into the learning model, the finger type included in the rotated fingerprint image is estimated (step S103).

[0040] Next, the registered finger type determination unit 140 reads out the finger type stored in the fingerprint image storage unit 130 (step S202). That is, it reads out information indicating the finger type that is stored in association with the rotated fingerprint image acquired in step S201. Then, the registered finger type determination unit 140 determines whether the finger type stored in the fingerprint image storage unit 130 matches the finger type estimated by the finger type estimation unit 120 (step S203).

[0041] If the finger type stored in the fingerprint image storage unit 130 does not match the finger type estimated by the finger type estimation unit 120 (step S203: NO), the registered finger type determination unit 140 outputs information indicating that the finger type stored in the fingerprint image storage unit 130 is incorrect (hereinafter referred to as "registration error information") (step S204).

[0042] The registered finger type determination unit 140 may display registration error information to the user (administrator) of the device via a display or the like. For example, the registered finger type determination unit 140 may display the rotated fingerprint image in which the finger type is incorrectly stored, the already stored finger type (i.e., the incorrect finger type), and the estimated finger type (i.e., the correct finger type). Furthermore, the registered finger type determination unit 140 may execute a process to correct or delete the finger type for the rotated fingerprint image in which the finger type is incorrectly stored. Alternatively, the registered finger type determination unit 140 may output a message or the like urging the user to re-register the rotated fingerprint image in which the finger type is incorrectly stored. For example, the registered finger type determination unit 140 may output a message saying, "The finger type for this rotated fingerprint image has been registered incorrectly. Please re-register the correct finger type."

[0043] On the other hand, if the finger type stored in the fingerprint image storage unit 130 matches the finger type estimated by the finger type estimation unit 120 (step S203: YES), the processing of step S204 described above is omitted. That is, the registered finger type determination unit 140 does not output registration error information. In this case, the registered finger type determination unit 140 may output information indicating that the finger type stored in the fingerprint image storage unit 130 is correct.

[0044] If multiple rotated fingerprint images are stored in the fingerprint image storage unit 130, the above-described series of processes may be repeatedly executed for each of the multiple rotated fingerprint images. That is, the estimation device 10 according to the present embodiment may determine whether the stored finger type is correct for all of the multiple rotated fingerprint images stored in the fingerprint image storage unit 130.

[0045] (Technical Effects) Next, technical effects obtained by the estimation device 10 according to the second embodiment will be described.

[0046] 4 and 5, the estimation device 10 according to the second embodiment determines whether the finger type of a stored rotated fingerprint image matches the finger type estimated from that rotated fingerprint image. This makes it possible to appropriately determine whether the stored rotated fingerprint image is an incorrect finger type.

[0047] Third Embodiment An estimation device 10 according to a third embodiment will be described with reference to Figures 6 and 7. The third embodiment differs from the first and second embodiments described above only in part of the configuration and operation, and other parts may be the same as the first and second embodiments. Therefore, the following will describe in detail the parts that differ from the embodiments already described, and will omit a description of other overlapping parts as appropriate.

[0048] (Functional Configuration) First, the functional configuration of the estimation device 10 according to the third embodiment will be described with reference to Fig. 6. Fig. 6 is a block diagram showing the functional configuration of the estimation device according to the third embodiment. Note that in Fig. 6, the same elements as those described in Fig. 2 are denoted by the same reference numerals.

[0049] 6, the estimation device 10 according to the third embodiment is configured to include, as components for realizing its functions, a fingerprint image acquisition unit 110, a finger type estimation unit 120, a finger type designation unit 150, and a designated finger type determination unit 160. That is, the estimation device 10 according to the third embodiment further includes the finger type designation unit 150 and the designated finger type determination unit 160 in addition to the configuration of the first embodiment (see FIG. 2). Each of the finger type designation unit 150 and the designated finger type determination unit 160 may be a processing block realized by, for example, the above-described processor 11.

[0050] The finger type designation unit 150 is configured to be able to designate the type of finger from which a rotated fingerprint image is to be captured. For example, the finger type designation unit 150 may designate a preset finger type. Alternatively, the finger type designation unit 150 may designate the finger types from which a rotated fingerprint image is to be captured sequentially according to a preset capture order. The finger type designation unit 150 may output information indicating the designated finger type to the subject from whom a rotated fingerprint image is to be captured. For example, the finger type designation unit 150 may display a message such as "Please scan the fingerprint of your right index finger" on the display.

[0051] The specified finger type determination unit 160 is configured to be able to determine whether or not the finger type specified by the finger type designation unit 150 matches the finger type estimated by the finger type estimation unit 120 using the collected rotated fingerprint image. The specified finger type determination unit 160 is configured to be able to output information indicating that the type of the finger from which the rotated fingerprint image was collected is incorrect when the finger type specified by the finger type designation unit 150 does not match the finger type estimated by the finger type estimation unit 120.

[0052] (Operation Flow) Next, the operation flow of the estimation device 10 according to the third embodiment will be described with reference to Fig. 7. Fig. 7 is a flowchart showing the operation flow of the estimation device according to the third embodiment. Note that in Fig. 7, the same processes as those described in Fig. 3 are denoted by the same reference numerals.

[0053] 7, when the operation of the estimation device 10 according to the third embodiment is started, the finger type designation unit 150 first outputs information designating the finger type from which a rotated fingerprint image is to be acquired (step S301). This results in an operation to scan the designated finger as a target. Then, the fingerprint image acquisition unit 110 acquires a rotated fingerprint image from the target (step S302).

[0054] Next, the finger type estimation unit 120 inputs the rotated fingerprint image acquired by the fingerprint image acquisition unit 110 into the learning model (step S102).Then, based on the rotated fingerprint image input into the learning model, the finger type included in the rotated fingerprint image is estimated (step S103).

[0055] Next, the specified finger type determination unit 160 determines whether the finger type specified by the finger type specifying unit 150 matches the finger type estimated by the finger type estimation unit 120 (step S303). That is, the specified finger type determination unit 160 determines whether the type of finger specified to be collected matches the type of finger actually collected.

[0056] If the finger type specified by the finger type specifying unit 150 does not match the finger type estimated by the finger type estimation unit 120 (step S303: NO), the specified finger type determination unit 160 outputs information indicating that the finger type from which the rotated fingerprint image was collected is incorrect (hereinafter referred to as "collection error information" as appropriate) (step S304).

[0057] The specified finger type determination unit 160 may display the collection error information to the subject (the user who collected the rotated fingerprint image) via a display or the like. For example, the specified finger type determination unit 160 may display the collected rotated fingerprint image, the finger type specified by the finger type designation unit 150, and the actually collected finger type (i.e., the type of finger that was incorrectly collected). Furthermore, the specified finger type determination unit 160 may output a message or the like urging the user to re-collect a rotated fingerprint image of the specified finger type. For example, the specified finger type determination unit 160 may output a message saying, "It appears that the fingerprint of the middle finger of your right hand was scanned by mistake. Please scan the fingerprint of the index finger of your right hand."

[0058] On the other hand, if the finger type specified by the finger type specifying unit 150 and the finger type estimated by the finger type estimation unit 120 match (step S303: YES), the processing of step S304 described above is omitted. That is, the specified finger type determination unit 160 does not output collection error information. In this case, the specified finger type determination unit 160 may output information indicating that the type of the finger from which the rotated fingerprint image was collected is correct.

[0059] (Technical Effects) Next, technical effects obtained by the estimation device 10 according to the third embodiment will be described.

[0060] As described with reference to Figures 6 and 7, the estimation device 10 according to the third embodiment determines whether the type of finger from which a rotated fingerprint image is taken matches the type of a specified finger. This makes it possible to properly detect whether a rotated fingerprint image is taken from the wrong finger. Therefore, a rotated fingerprint image of a specified finger can be taken without making a mistake. Furthermore, if the order of fingers to be taken is specified, rotated fingerprint images can be taken in the specified order. As a result, for example, it is possible to prevent a taken rotated fingerprint image from being registered as that of a different finger.

[0061] Fourth Embodiment An estimation device 10 according to a fourth embodiment will be described with reference to Figures 8 and 9. The fourth embodiment differs from the first to third embodiments described above only in part of the configuration and operation, and other parts may be the same as the first to third embodiments. Therefore, the following will describe in detail parts that differ from the embodiments already described, and will omit descriptions of other overlapping parts as appropriate.

[0062] (Functional Configuration) First, the functional configuration of the estimation device 10 according to the fourth embodiment will be described with reference to Fig. 8. Fig. 8 is a block diagram showing the functional configuration of the estimation device according to the fourth embodiment. Note that in Fig. 8, the same elements as those described in Fig. 2 are denoted by the same reference numerals.

[0063] As shown in Fig. 8, the estimation device 10 according to the fourth embodiment is configured to include, as components for realizing its functions, a fingerprint image acquisition unit 110, a finger type estimation unit 120, and a fingerprint image registration unit 170. That is, the estimation device 10 according to the fourth embodiment further includes a fingerprint image registration unit 170 in addition to the configuration of the first embodiment (see Fig. 2). The fingerprint image registration unit 170 may be realized by, for example, the above-mentioned processor 11 or storage device 14 (see Fig. 1).

[0064] The fingerprint image registration unit 170 is configured to be able to register a rotated fingerprint image by linking it with the finger type of the rotated fingerprint image. That is, the fingerprint image registration unit 170 is configured to be able to register a rotated fingerprint image acquired by the fingerprint image acquisition unit 110 and a finger type estimated from the rotated fingerprint image by linking them with each other. The fingerprint image registration unit 170 may be configured to store (register) the rotated fingerprint image and the finger type in, for example, the fingerprint image storage unit 130 (see FIG. 4) described in the second embodiment.

[0065] (Operation Flow) Next, the operation flow of the estimation device 10 according to the fourth embodiment will be described with reference to Fig. 9. Fig. 9 is a flowchart showing the operation flow of the estimation device according to the fourth embodiment. Note that in Fig. 9, the same processes as those described in Fig. 3 are denoted by the same reference numerals.

[0066] 9, when the operation of the estimation device 10 according to the fourth embodiment is started, the fingerprint image acquisition unit 110 first acquires a rotated fingerprint image (step S101). For example, the fingerprint image acquisition unit 110 acquires a rotated fingerprint image collected from a subject using a scanner or the like.

[0067] Next, the finger type estimation unit 120 inputs the rotated fingerprint image acquired by the fingerprint image acquisition unit 110 into the learning model (step S102).Then, based on the rotated fingerprint image input into the learning model, the finger type included in the rotated fingerprint image is estimated (step S103).

[0068] Next, the fingerprint image registration unit 170 associates the rotated fingerprint image acquired by the fingerprint image acquisition unit 110 with the finger type estimated by the finger type estimation unit 120 and registers them (step S401). For example, if the acquired rotated fingerprint image is estimated to be that of the index finger of the right hand, the fingerprint image registration unit 170 associates the acquired rotated fingerprint image with information indicating that the rotated fingerprint image is that of the index finger of the right hand and registers them.

[0069] (Technical Effects) Next, technical effects obtained by the estimation device 10 according to the fourth embodiment will be described.

[0070] 8 and 9, in the estimation device 10 according to the fourth embodiment, a rotated fingerprint image is registered in association with the type of finger of the rotated fingerprint image. In this embodiment, the finger type estimation unit 120 can estimate the finger type of a rotated fingerprint image with high accuracy, thereby preventing the rotated fingerprint image from being registered with an incorrect finger type. Furthermore, because the finger type of a captured rotated fingerprint image is automatically estimated, the rotated fingerprint image can be properly registered without specifying the order in which the fingers are captured.

[0071] Fifth Embodiment An estimation device 10 according to a fifth embodiment will be described with reference to Figures 10 and 11. The fifth embodiment differs from the fourth embodiment described above only in some configurations and operations, and other parts may be the same as the fourth embodiment. Therefore, the following will describe in detail parts that differ from the embodiments already described, and will omit descriptions of other overlapping parts as appropriate.

[0072] (Functional Configuration) First, the functional configuration of the estimation device 10 according to the fifth embodiment will be described with reference to Fig. 10. Fig. 10 is a block diagram showing the functional configuration of the estimation device according to the fifth embodiment. Note that in Fig. 10, the same elements as those described in Fig. 8 are denoted by the same reference numerals.

[0073] 10 , the estimation device 10 according to the fifth embodiment is configured to include, as components for realizing its functions, a fingerprint image acquisition unit 110, a finger type estimation unit 120, a fingerprint image registration unit 170, and a missing finger type determination unit 180. That is, the estimation device 10 according to the fifth embodiment further includes a missing finger type determination unit 180 in addition to the configuration of the fourth embodiment (see FIG. 8 ). The missing finger type determination unit 180 may be a processing block realized by, for example, the above-described processor 11 (see FIG. 1 ).

[0074] The missing finger type determination unit 180 is configured to be able to determine whether or not there are any missing rotated fingerprint images registered by the fingerprint image registration unit 170. For example, the missing finger type determination unit 180 may be configured to determine whether or not rotated fingerprint images have been registered for all ten fingers on both hands. If there are any missing rotated fingerprint images in the registered rotated fingerprint images, the missing finger type determination unit 180 is configured to output information (hereinafter referred to as "missing information") that prompts the user to collect rotated fingerprint images for the missing fingers. The missing information may include, for example, information indicating the type of the missing finger. The missing information may also include information indicating the type of fingers that have already been collected.

[0075] (Operation Flow) Next, the operation flow of the estimation device 10 according to the fifth embodiment will be described with reference to Fig. 11. Fig. 11 is a flowchart showing the operation flow of the estimation device according to the fifth embodiment. Note that in Fig. 11, the same processes as those described in Fig. 9 are denoted by the same reference numerals.

[0076] 11 , when the operation of the estimation device 10 according to the fifth embodiment is started, the fingerprint image acquisition unit 110 first acquires a rotated fingerprint image (step S101). Then, the finger type estimation unit 120 inputs the rotated fingerprint image acquired by the fingerprint image acquisition unit 110 into a learning model (step S102).

[0077] Next, the type of finger included in the rotated fingerprint image is estimated based on the rotated fingerprint image into which the learning model has been input (step S103).Then, the fingerprint image registration unit 170 associates the rotated fingerprint image acquired by the fingerprint image acquisition unit 110 with the finger type estimated by the finger type estimation unit 120 and registers them (step S401).

[0078] Next, the missing finger type determination unit 180 determines whether or not there are any missing rotated fingerprint images registered by the fingerprint image registration unit 170 (step S501). Here, an example is given in which the process of determining whether or not there are any missing rotated fingerprint images (i.e., the process of step S501) is executed immediately after the process of registering a rotated fingerprint image (i.e., the process of step S401), but the process of step S501 may be executed some time after the process of step S401 is completed. For example, the missing finger type determination unit 180 may determine whether or not there are any missing rotated fingerprint images registered several months or several years ago.

[0079] If there are insufficient registered rotated fingerprint images (step S501: YES), the missing finger type determination unit 180 outputs information indicating the insufficiency (step S502). For example, if there are insufficient rotated fingerprint images of the right index finger, the missing finger type determination unit 180 may output a message such as "There are insufficient fingerprints of the right index finger. Please obtain a fingerprint of the right index finger."

[0080] On the other hand, if there are no missing registered rotated fingerprint images (step S501: NO), the processing of step S502 described above is omitted. That is, the missing finger type determination unit 180 does not output missing information. In this case, the missing finger type determination unit 180 may output information indicating that all rotated fingerprint images are complete.

[0081] (Technical Effects) Next, technical effects obtained by the estimation device 10 according to the fifth embodiment will be described.

[0082] 10 and 11 , in the estimation device 10 according to the fifth embodiment, when a registered rotated fingerprint image contains a missing finger type, information is output to prompt the user to collect a rotated fingerprint image of the missing finger type. This makes it possible to properly detect the missing finger type in the registered rotated fingerprint images. Furthermore, since the user can be prompted to collect a rotated fingerprint image of the missing finger, the missing rotated fingerprint images can be quickly replenished.

[0083] Sixth Embodiment An estimation device 10 according to a sixth embodiment will be described with reference to Fig. 12 and Fig. 13. Note that the sixth embodiment differs only in part of the configuration and operation from the first to fifth embodiments described above, and other parts may be the same as the first to fifth embodiments. Therefore, hereinafter, only the parts that differ from the embodiments already described will be described in detail, and descriptions of other overlapping parts will be omitted as appropriate.

[0084] (Functional Configuration) First, the functional configuration of the estimation device 10 according to the sixth embodiment will be described with reference to Fig. 12. Fig. 12 is a block diagram showing the functional configuration of the estimation device according to the sixth embodiment. Note that in Fig. 12, the same elements as those described in Fig. 2 are denoted by the same reference numerals.

[0085] 12 , the estimation device 10 according to the sixth embodiment is configured to include, as components for realizing its functions, a fingerprint image acquisition unit 110, a finger type estimation unit 120, an image quality determination unit 190, and an alternative registration unit 200. That is, the estimation device 10 according to the sixth embodiment further includes, in addition to the configuration of the first embodiment (see FIG. 2 ), the image quality determination unit 190 and the alternative registration unit 200. Each of the image quality determination unit 190 and the alternative registration unit 200 may be realized by, for example, the above-mentioned processor 11 or the storage device 14 (see FIG. 1 ).

[0086] The image quality determination unit 190 is configured to be able to determine the quality of the rotated fingerprint image acquired by the fingerprint image acquisition unit 110. Specifically, the image quality determination unit 190 is configured to be able to determine whether the quality of the rotated fingerprint image satisfies a predetermined standard. The "predetermined standard" here is a standard that is set in advance as a standard for determining whether the quality of the rotated fingerprint image is sufficient for its operational purpose. For example, the predetermined standard may be set as a standard for determining whether the quality of the rotated fingerprint image is suitable for fingerprint matching.

[0087] The alternative registration unit 200 is configured to be able to register a flat fingerprint image of the same type as the rotated fingerprint image in place of the rotated fingerprint image when it is determined that the quality of the rotated fingerprint image does not meet a predetermined standard. For example, if the quality of the rotated fingerprint image of a right index finger does not meet a predetermined standard, the alternative registration unit 200 registers the flat fingerprint image of the right index finger by linking it to information indicating that the finger type contained in the image is a right index finger. Note that the flat fingerprint image registered in place of the rotated fingerprint image may be an image that has already been registered. In other words, the flat fingerprint image may be one that has been collected separately from the rotated fingerprint image and has already been registered.

[0088] Furthermore, if the quality of the rotated fingerprint image is determined to satisfy a predetermined standard, the alternative registration unit 200 may register the rotated fingerprint image in association with information indicating the finger type. That is, the alternative registration unit 200 may have the same function as the fingerprint image registration unit 170 (see FIG. 8 ) described in the fourth embodiment.

[0089] (Operation Flow) Next, the operation flow of the estimation device 10 according to the sixth embodiment will be described with reference to Fig. 13. Fig. 13 is a flowchart showing the operation flow of the estimation device according to the sixth embodiment. Note that in Fig. 13, the same processes as those described in Fig. 3 are denoted by the same reference numerals.

[0090] 13, when the operation of the estimation device 10 according to the sixth embodiment is started, the fingerprint image acquisition unit 110 first acquires a rotated fingerprint image (step S101). Then, the finger type estimation unit 120 inputs the rotated fingerprint image acquired by the fingerprint image acquisition unit 110 into a learning model (step S102). Then, the learning model estimates the type of finger included in the rotated fingerprint image based on the input rotated fingerprint image (step S103).

[0091] Next, the image quality determination unit 190 determines whether the quality of the rotated fingerprint image acquired by the fingerprint image acquisition unit 110 satisfies a predetermined standard (step S601). The process of determining the quality of the rotated fingerprint image may be executed immediately after the rotated fingerprint image is acquired. That is, the process of step S601 may be executed simultaneously in parallel with the processes of steps S102 and S103, or may be executed one after the other.

[0092] If the quality of the rotated fingerprint image satisfies a predetermined standard (step S601: YES), the alternative registration unit 200 associates the rotated fingerprint image with information indicating the estimated finger type and registers it (step S602). On the other hand, if the quality of the rotated fingerprint image does not satisfy the predetermined standard (step S601: NO), the alternative registration unit 200 registers a flat fingerprint image of the same finger type as the rotated fingerprint image instead of the rotated fingerprint image (step S603).

[0093] (Technical Effects) Next, technical effects obtained by the estimation device 10 according to the sixth embodiment will be described.

[0094] 12 and 13, in the estimation device 10 according to the sixth embodiment, if the quality of a rotated fingerprint image does not satisfy a predetermined standard, a flat fingerprint image is registered instead of the rotated fingerprint image. This prevents a low-quality rotated fingerprint image that is not suitable for operation from being registered. Furthermore, by registering a flat fingerprint image instead of a rotated fingerprint image, it is possible to prevent missing finger types.

[0095] <Modification> The process of alternatively registering a flat fingerprint image described in the sixth embodiment (i.e., the process of step S603 in FIG. 13 ) may be executed when it is determined that the finger type is incorrect. For example, the alternative registration unit 200 may be configured to execute a process of registering a flat fingerprint image instead of an already-stored rotated fingerprint image when it is determined in the registration finger type determination unit 140 described in the second embodiment that the finger type stored in the fingerprint image storage unit 130 does not match the finger type estimated by the finger type estimation unit 120. The flow of operation of this modification will be specifically described below.

[0096] (Operational Flow) The operational flow of the estimation device 10 according to the modified example of the sixth embodiment will be described with reference to Fig. 14. Fig. 14 is a flowchart showing the operational flow of the estimation device according to the modified example of the sixth embodiment. In Fig. 14, the same processes as those described in Fig. 5 and Fig. 13 are denoted by the same reference numerals.

[0097] As shown in FIG. 14, when the operation of the estimation device 10 according to the modified example of the sixth embodiment is started, the fingerprint image acquisition unit 110 first acquires the rotated fingerprint image stored in the fingerprint image storage unit 130 (step S201).

[0098] Next, the finger type estimation unit 120 inputs the rotated fingerprint image acquired by the fingerprint image acquisition unit 110 into the learning model (step S102).Then, based on the rotated fingerprint image input into the learning model, the finger type included in the rotated fingerprint image is estimated (step S103).

[0099] Next, the registered finger type determination unit 140 reads out the finger type stored in the fingerprint image storage unit 130 (step S202). Then, the registered finger type determination unit 140 determines whether the finger type stored in the fingerprint image storage unit 130 matches the finger type estimated by the finger type estimation unit 120 (step S203).

[0100] If the finger type stored in the fingerprint image storage unit 130 does not match the finger type estimated by the finger type estimation unit 120 (step S203: NO), the alternative registration unit 200 registers a flat fingerprint image of the same finger type as the rotated fingerprint image instead of the rotated fingerprint image (step S603). That is, the alternative registration unit 200 overwrites and saves the flat fingerprint image of the correct finger type over the rotated fingerprint image (the rotated fingerprint image with the wrong finger type registered) already stored in the fingerprint image storage unit 130.

[0101] On the other hand, if the finger type stored in the fingerprint image storage unit 130 matches the finger type estimated by the finger type estimation unit 120 (step S203: YES), the process of step S603 described above is omitted. That is, the alternative registration unit 200 does not execute the process of registering a flat fingerprint image instead of a rotated fingerprint image.

[0102] (Technical Effects) Next, technical effects obtained by the estimation device 10 according to the modified example of the sixth embodiment will be described.

[0103] 14, in the estimation device 10 according to the modification of the sixth embodiment, if the registered finger type is incorrect, a flat fingerprint image is registered instead of a rotated fingerprint image. In this way, even if a rotated fingerprint image of the wrong finger type is registered, the mistake can be corrected by replacing it with a flat fingerprint image of the correct finger type.

[0104] Seventh Embodiment An estimation device 10 according to a seventh embodiment will be described with reference to Fig. 15. The seventh embodiment is an embodiment that describes a more specific hardware configuration of the estimation device 10 described in the first to sixth embodiments, and the functional configuration and operational flow thereof may be the same as those of the first to sixth embodiments. Therefore, the following will describe in detail the parts that are different from the embodiments already described, and will omit a description of other overlapping parts as appropriate.

[0105] (Hardware Configuration) First, the hardware configuration of the estimation device 10 according to the seventh embodiment will be described with reference to Fig. 15. Fig. 15 is a schematic diagram showing the hardware configuration of the estimation device according to the seventh embodiment.

[0106] As shown in FIG. 15, the estimation device 10 according to the seventh embodiment includes a touch panel 51, a scanner unit 52, a display 53, and a control unit 54.

[0107] The touch panel 51 is a panel on which the subject whose fingerprint is to be collected touches the finger. To collect a rotated fingerprint image, the subject simply moves their finger in a rotating motion on the touch panel 51. The scanner unit 52 is disposed below the touch panel 51 and scans the fingerprint image of the finger that touches the touch panel 51. The fingerprint image scanned by the scanner unit 52 is acquired by the fingerprint image acquisition unit 110 (see FIG. 2, etc.).

[0108] The display 53 is configured to be able to display the fingerprint image scanned by the scanner unit 52. The display 53 may also be configured to display information about the finger type estimated from the scanned rotated fingerprint image. The display 53 may further be configured to be able to display various information such as the registration error information (see FIGS. 4 and 5) described in the second embodiment, the information indicating the designated finger and the collection error information (see FIGS. 6 and 7) described in the third embodiment, and the missing information (see FIGS. 10 and 11) described in the fifth embodiment.

[0109] The control unit 54 is a controller including, for example, the processor 11, and is configured to realize the components of the estimation device 10 according to each of the above-described embodiments. Specifically, the control unit 54 may realize the functions of the fingerprint image acquisition unit 110, the finger type estimation unit 120, the fingerprint image storage unit 130, the registered finger type determination unit 140, the finger type designation unit 150, the designated finger type determination unit 160, the fingerprint image registration unit 170, the missing finger type determination unit 180, the image quality determination unit 190, and the alternative registration unit 200.

[0110] (Technical Effects) Next, technical effects obtained by the estimation device 10 according to the seventh embodiment will be described.

[0111] 15, the estimation device 10 according to the seventh embodiment can capture a rotated fingerprint image of a target and estimate the type of finger contained in the rotated fingerprint image with high accuracy. This estimation device 10 can be used, for example, as a device used to register fingerprints in a fingerprint matching system used by the police. It can also be used as a device for capturing fingerprints during immigration inspections at airports, etc.

[0112] <Other Application Examples> The estimation device 10 according to each of the above-described embodiments can also be applied to mobile terminals such as smartphones, general home appliances, and the like.

[0113] For example, when assigning a shortcut function of a mobile device to a specific finger, it is necessary to register the finger in advance (i.e., to associate the type of finger with the function to be executed). However, the estimation device 10 according to the present embodiment can estimate the type of finger that touched the device, thereby eliminating the need to register the finger in advance.

[0114] For example, multiple operations in various applications may be assigned to each finger. Specifically, in a book reading application, a touch with the index finger may execute the "page forward" function, and a touch with the middle finger may execute the "magnifier" function. In a video playback application, a touch with the index finger may execute the "play" function, and a touch with the middle finger may execute the "fast forward" function. Alternatively, when the security lock of the mobile device is released, a function corresponding to the operating finger may be activated after the lock is released.

[0115] It is also possible to treat long presses on the screen with fingers that are not normally used to operate mobile devices as a default function. For example, when the screen is long pressed with the little finger of the left hand, an emergency call such as 110 or 119 may be made, or a call may be automatically made to a number with a high call history.

[0116] When applied to general home appliances, for example, answers to questions asked during initial setup may be selected according to the type of finger used. Specifically, "yes" may be answered with the index finger, and "no" with the middle finger. This type of function is particularly effective in devices that require operation without looking at the screen, such as car navigation systems.

[0117] Furthermore, if it is installed in a remote control for a television or the like, it can be used to change channels or adjust the volume with a specific finger. If it is installed in an electronic piano, it can be used to practice fingering when playing a song.

[0118] The scope of each embodiment also includes a processing method in which a program that operates the configuration of each embodiment to realize the functions of the above-described embodiments is recorded on a recording medium, the program recorded on the recording medium is read as code, and the program is executed on a computer. In other words, a computer-readable recording medium is also included in the scope of each embodiment. Furthermore, each embodiment includes not only a recording medium on which the above-described program is recorded, but also the program itself.

[0119] Examples of recording media that can be used include floppy disks, hard disks, optical disks, magneto-optical disks, CD-ROMs, magnetic tapes, non-volatile memory cards, and ROMs. Furthermore, the scope of each embodiment is not limited to programs that execute processes by themselves, but also includes programs that execute processes by operating on an OS in conjunction with other software or expansion board functions. Furthermore, the program itself may be stored on a server, and part or all of the program may be downloadable from the server to a user terminal. The program may be provided to the user in, for example, a SaaS (Software as a Service) format.

[0120] <Supplementary Notes> The above-described embodiment may be further described as in the following supplementary notes, but is not limited to the following.

[0121] (Supplementary Note 1) The estimation device described in Supplementary Note 1 is an estimation device that includes an acquisition means that acquires a rotated fingerprint image collected by rotating a finger, and an estimation means that estimates the type of finger included in the rotated fingerprint image by inputting the acquired rotated fingerprint image into a trained model.

[0122] (Supplementary Note 2) The estimation device described in Supplementary Note 2 is the estimation device described in Supplementary Note 1, further comprising: a storage means for storing the rotated fingerprint image in association with a finger type; and a first output means for outputting information indicating that the finger type stored in the storage means is incorrect when the finger type estimated from the rotated fingerprint image by the estimation means differs from the finger type stored in association with the rotated fingerprint image.

[0123] (Supplementary Note 3) The estimation device described in Supplementary Note 3 is the estimation device described in Supplementary Note 1 or 2, further comprising: a designation means for designating the type of finger from which the rotated fingerprint image is to be collected; and a second output means for outputting information indicating that the type of finger from which the rotated fingerprint image is to be collected is incorrect when the type of finger estimated by the estimation means from the collected rotated fingerprint image differs from the type of finger designated by the designation means.

[0124] (Supplementary Note 4) The estimation device described in Supplementary Note 4 is the estimation device described in any one of Supplements 1 to 3, further comprising a registration means for registering the collected rotated fingerprint image in association with the finger type estimated by the estimation means.

[0125] (Supplementary Note 5) The estimation device described in Supplementary Note 5 is the estimation device described in Supplementary Note 4, further comprising a third output means for outputting information prompting the user to collect the rotated fingerprint image of a missing finger when the rotated fingerprint image registered by the registration means contains a missing finger type.

[0126] (Supplementary Note 6) The estimation device described in Supplementary Note 6 is the estimation device described in any one of Supplements 1 to 5, further comprising: a determination means for determining whether or not the quality of the rotated fingerprint image satisfies a predetermined standard; and an alternative registration means for registering a flat fingerprint image of the same finger type as the rotated fingerprint image instead of the rotated fingerprint image if the quality of the rotated fingerprint image does not satisfy the predetermined standard.

[0127] (Supplementary Note 7) The estimation device described in Supplementary Note 7 is the estimation device described in any one of Supplements 1 to 6, further comprising: a storage means for storing the rotated fingerprint image in association with a finger type; and an alternative registration means for registering a flat fingerprint image of the same finger type as the rotated fingerprint image in place of the rotated fingerprint image when the finger type estimated by the estimation means from the rotated fingerprint image is different from the finger type stored in association with the rotated fingerprint image.

[0128] (Supplementary Note 8) The estimation device described in Supplementary Note 8 is the estimation device described in any one of Supplements 1 to 7, further including a touch panel on which a subject touches with a finger, and a scanner that scans the finger touching the touch panel to obtain the rotated fingerprint image.

[0129] (Supplementary Note 9) The estimation device described in Supplementary Note 9 is the estimation device described in any one of Supplements 1 to 8, further comprising a display that displays at least one of the rotated fingerprint image or information related to the finger type estimated from the rotated fingerprint image.

[0130] (Supplementary Note 10) The estimation method described in Supplementary Note 10 is an estimation method in which a rotated fingerprint image is acquired by rotating a finger using at least one computer, and the rotated fingerprint image is input into a trained model to estimate the type of finger included in the rotated fingerprint image.

[0131] (Supplementary Note 11) The computer program described in Supplementary Note 11 is a computer program that causes at least one computer to execute an estimation method for acquiring a rotated fingerprint image captured by rotating a finger, and inputting the rotated fingerprint image into a trained model, thereby estimating the type of finger included in the rotated fingerprint image.

[0132] (Appendix 12) The recording medium described in Appendix 12 is a recording medium having recorded thereon a computer program for causing at least one computer to execute an estimation method for acquiring a rotated fingerprint image taken by rotating a finger, and inputting the rotated fingerprint image into a trained model, thereby estimating the type of finger included in the rotated fingerprint image.

[0133] This disclosure may be modified as appropriate within the scope that does not contradict the gist or idea of ​​the invention that can be read from the claims and the entire specification, and estimation devices, estimation methods, and recording media that involve such modifications are also included in the technical idea of ​​this disclosure.

[0134] To the extent permitted by law, this application claims priority based on Japanese Patent Application No. 2023-020932, filed February 14, 2023, the disclosure of which is incorporated herein in its entirety. Furthermore, to the extent permitted by law, all publications and papers mentioned in this specification are incorporated herein by reference.

[0135] 10 Estimation device 11 Processor 51 Touch panel 52 Scanner unit 53 Display 54 Control unit 110 Fingerprint image acquisition unit 120 Finger type estimation unit 130 Fingerprint image storage unit 140 Registered finger type determination unit 150 Finger type designation unit 160 Designated finger type determination unit 170 Fingerprint image registration unit 180 Missing finger type determination unit 190 Image quality determination unit 200 Alternative registration unit

Claims

1. An acquisition means for acquiring a rotated fingerprint image captured by rotating a finger; an estimation means for estimating the type of finger included in the rotated fingerprint image by inputting the acquired rotated fingerprint image into a trained model; An estimation device comprising:

2. a storage means for storing the rotated fingerprint image in association with the type of finger; a first output means for outputting information indicating that the finger type stored in the storage means is incorrect when the finger type estimated by the estimation means from the rotated fingerprint image differs from the finger type stored in association with the rotated fingerprint image; The estimation device according to claim 1 , further comprising:

3. a designation means for designating the type of finger from which the rotated fingerprint image is to be taken; a second output means for outputting information indicating that the type of finger from which the rotated fingerprint image is taken is incorrect when the type of finger estimated by the estimation means from the taken rotated fingerprint image differs from the type of finger designated by the designation means; The estimation device according to claim 1 or 2, further comprising:

4. The method further comprises a registration means for registering the collected rotated fingerprint image in association with the finger type estimated by the estimation means. The estimation device according to claim 1 or 2.

5. a third output means for outputting information prompting the user to take the rotated fingerprint image of a missing finger when the rotated fingerprint image registered by the registration means is insufficient for the finger type; The estimation device according to claim 4 .

6. a determining means for determining whether the quality of the rotated fingerprint image satisfies a predetermined standard; an alternative registration means for registering a flat fingerprint image of the same finger type as the rotated fingerprint image instead of the rotated fingerprint image if the quality of the rotated fingerprint image does not satisfy the predetermined standard; The estimation device according to claim 1 or 2, further comprising:

7. a touch panel that the subject touches with his / her finger; a scanner that scans a finger that touches the touch panel to acquire the rotated fingerprint image; The estimation device according to claim 1 or 2, further comprising:

8. a display that displays at least one of the rotated fingerprint image and information about the finger type estimated from the rotated fingerprint image; The estimation device according to claim 1 or 2.

9. by at least one computer, A rotated fingerprint image is acquired by rotating the finger, The rotated fingerprint image is input into a trained model to estimate the type of finger included in the rotated fingerprint image. Estimation method.

10. At least one computer A rotated fingerprint image is acquired by rotating the finger, The rotated fingerprint image is input into a trained model to estimate the type of finger included in the rotated fingerprint image. A computer program that causes the estimation method to be carried out.