Information processing system, information processing method, and recording medium

Abstract

An information processing system (10) comprises: a score acquisition means (110) that acquires a first score based on the output of a first inference model and a second score based on the out put of a second inference model; a first integrated score calculation means (120) that calculates a first integrated score; a second integrated score calculation means (130) that calculates a second integrated score by performing integration in which the weight of the second score is increased; a score determination means (140) that determines whether or not each of the first integrated score and the second integrated score exceeds a prescribed threshold value, and also determines whether or not the system is in a specific state in which only the second integrated score exceeds the prescribed threshold value; and an output means (150) that outputs an inference result based on whether or not the first integrated score exceeds the prescribed threshold value if the system is not in the specific state, and outputs information different from the inference result if the system is in the specific state.

Description

Information processing system, information processing method, and recording medium

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

[0002] Known systems of this type calculate various scores using a trained estimation model. For example, Patent Literature 1 discloses a system for performing face recognition in which a reference face image that satisfies a score threshold is used as a face image candidate for recognition, and the face image candidate and its calculated score are selected as authentication information. Patent Literature 2 discloses a system for integrating an attribute-dependent score and an attribute-independent score and outputting the result as a matching score.

[0003] As another related technique, for example, Patent Document 3 discloses a technique in which, when an error occurs in face authentication, information indicating how to respond to the person to be authenticated is output.

[0004] International Publication No. 2020 / 050413 International Publication No. 2018 / 173194 International Publication No. 2017 / 043314

[0005] This disclosure aims to improve upon the techniques disclosed in the prior art documents.

[0006] One aspect of the information processing system disclosed herein comprises a score acquisition means for acquiring a first score based on the output of a first inference model and a second score based on the output of a second inference model having characteristics different from those of the first inference model; a first integrated score calculation means for calculating a first integrated score by integrating the first score and the second score; a second integrated score calculation means for calculating a second integrated score by integrating the first score and the second score so that the weight of the second score is greater than when calculating the first integrated score; a score determination means for determining whether each of the first integrated score and the second integrated score exceeds a predetermined threshold, and for determining whether a specific state is present in which only the second integrated score exceeds the predetermined threshold; and an output means for outputting an inference result based on whether the first integrated score exceeds the predetermined threshold if the specific state is not present, and outputting information different from the inference result if the specific state is present.

[0007] One aspect of the information processing method disclosed herein involves using at least one computer to obtain a first score based on the output of a first inference model and a second score based on the output of a second inference model having characteristics different from those of the first inference model, calculate a first integrated score by integrating the first score and the second score, calculate a second integrated score by integrating the first score and the second score so that the weight of the second score is greater than when calculating the first integrated score, determine whether each of the first integrated score and the second integrated score exceeds a predetermined threshold, and determine whether a specific state is present in which only the second integrated score exceeds the predetermined threshold, and if the specific state is not present, output an inference result based on whether the first integrated score exceeds the predetermined threshold, and if the specific state is present, output information different from the inference result.

[0008] One aspect of the recording medium of this disclosure is a computer program recorded on at least one computer that causes the computer to execute an information processing method, which includes obtaining a first score based on the output of a first inference model and a second score based on the output of a second inference model having characteristics different from those of the first inference model, calculating a first integrated score by integrating the first score and the second score, calculating a second integrated score by integrating the first score and the second score so that the weight of the second score is greater than when calculating the first integrated score, determining whether each of the first integrated score and the second integrated score exceeds a predetermined threshold, and determining whether a specific state is present in which only the second integrated score exceeds the predetermined threshold, outputting an inference result based on whether the first integrated score exceeds the predetermined threshold if the specific state is not present, and outputting information different from the inference result if the specific state is present.

[0009] 1 is a block diagram showing the hardware configuration of an information processing system according to a first embodiment. FIG. 2 is a block diagram showing the functional configuration of an information processing system according to the first embodiment. FIG. 3 is a flowchart showing the flow of operation of an information processing system according to the first embodiment. FIG. 4 is a flowchart showing the flow of operation of an information processing system according to a second embodiment. FIG. 5 is a graph showing an area corresponding to a specific state in an information processing system according to a second embodiment. FIG. 6 is a graph showing a method for determining an area corresponding to a specific state in an information processing system according to a third embodiment. FIG. 7 is a graph showing a method for calculating the size of an area corresponding to a specific state in an information processing system according to the third embodiment. FIG. 8 is a block diagram showing the functional configuration of an information processing system according to a fourth embodiment. FIG. 9 is a flowchart showing the flow of operation of an information processing system according to the fourth embodiment. FIG. 10 is a graph showing an area corresponding to a specific state in an information processing system according to a fourth embodiment.

[0010] Hereinafter, embodiments of an information processing system, an information processing method, and a recording medium will be described with reference to the drawings.

[0011] First Embodiment An information processing system according to a first embodiment will be described with reference to FIGS. 1 to 3. FIG.

[0012] (Hardware Configuration) First, the hardware configuration of the information processing system 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 information processing system according to the first embodiment.

[0013] 1 , an information processing system 10 according to the first embodiment includes a processor 11, a RAM (Random Access Memory) 12, a ROM (Read Only Memory) 13, and a storage device 14. The information processing system 10 may further include 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 via a data bus 17.

[0014] 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 information processing system 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 is realized within the processor 11 for integrating scores of inference models and outputting an inference result based on the integrated score. In other words, the processor 11 may function as a controller that executes each control in the information processing system 10.

[0015] 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 demand-side platform (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.

[0016] 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.

[0017] 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 PROM (Programmable Read Only Memory) or an EPROM (Erasable Read Only Memory). Alternatively, other types of non-volatile memory may be used instead of the ROM 13.

[0018] The storage device 14 stores data that is to be saved long-term by the information processing system 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.

[0019] The input device 15 is a device that receives input instructions from a user of the information processing system 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.

[0020] The output device 16 is a device that outputs information related to the information processing system 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 information processing system 10. The output device 16 may also be a speaker or the like that can output information related to the information processing system 10 as audio. 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 information processing system 10 as audio.

[0021] 1 illustrates an example of an information processing system 10 including multiple devices, but all or some of the functions may be realized by a single device (information processing device). In this case, the information processing device 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, input device 15, and output device 16) may be provided by an external device connected to the information processing device. Furthermore, some of the calculation functions of the information processing device may be realized by an external device (e.g., an external server, a cloud, etc.).

[0022] (Functional Configuration) Next, the functional configuration of the information processing system 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 information processing system according to the first embodiment.

[0023] The information processing system 10 according to the first embodiment is configured to be capable of integrating the outputs (scores) of multiple inference models with different characteristics and outputting an inference result. Hereinafter, these inference models will be referred to as a first inference model and a second inference model. The specific aspects of the first inference model and the second inference model are not particularly limited, but they may be configured, for example, as an authentication model used when performing biometric authentication or a detection model used when detecting diseases such as cancer. The information processing system 10 may be configured to include an inference model within the system itself, or may be configured to utilize an inference model external to the system.

[0024] 2, the information processing system 10 according to the first embodiment is configured to include, as components for realizing its functions, a score acquisition unit 110, a first total score calculation unit 120, a second total score calculation unit 130, a score determination unit 140, and an output unit 150. Each of the score acquisition unit 110, the first total score calculation unit 120, the second total score calculation unit 130, the score determination unit 140, and the output unit 150 may be a processing block realized by, for example, the above-mentioned processor 11 (see FIG. 1).

[0025] The score acquisition unit 110 is configured to be able to acquire a first score based on the output of the first inference model and a second score based on the output of the second inference model (i.e., a model with characteristics different from those of the first inference model). The first score and the second score acquired by the score acquisition unit 110 are configured to be output to the first integrated score calculation unit 120 and the second integrated score calculation unit, respectively.

[0026] The first integrated score calculation unit 120 is configured to calculate the first integrated score by integrating the first score and the second score. Although the method for calculating the first integrated score is not particularly limited, the first integrated score calculation unit 120 performs the integration process by weighting the first score and the second score. The first integrated score calculated by the first integrated score calculation unit 120 is configured to be output to the score determination unit 140.

[0027] The second integrated score calculation unit 130 is configured to calculate the second integrated score by integrating the first score and the second score. The method of calculating the second integrated score is not particularly limited, but the second integrated score calculation unit 130 is configured to perform the integration process so that the weight of the second score is increased compared to when the first integrated score is calculated (i.e., the process of the first integrated score calculation unit 120). Therefore, the first integrated score and the second integrated score are different from each other. The first integrated score calculated by the second integrated score calculation unit 130 is configured to be output to the score determination unit 140.

[0028] The score determination unit 140 is configured to be able to perform various determination processes using the first integrated score calculated by the first integrated score calculation unit 120 and the second integrated score calculated by the second integrated score calculation unit 130. Specifically, the score determination unit 140 is configured to be able to determine whether each of the first integrated score and the second integrated score exceeds a predetermined threshold. Note that the "predetermined threshold" here is a value set in advance for producing an inference result. The score determination unit 140 is also configured to be able to determine whether a specific state is present. The "specific state" here refers to a state in which, of the first integrated score and the second integrated score, only the second integrated score exceeds the predetermined threshold. The determination result by the score determination unit 140 is configured to be output to the output unit 150.

[0029] The output unit 150 is configured to be able to output various information depending on the determination result of the score determination unit 140. Specifically, when the score determination unit 140 determines that the specific state is not present, the output unit 140 outputs an inference result based on whether or not the first integrated score exceeds a predetermined threshold. On the other hand, when the score determination unit 140 determines that the specific state is present, the output unit 140 outputs information different from the inference result. Specific examples of information different from the inference result will be described in detail in other embodiments described later.

[0030] (Operation Flow) Next, the operation flow of the information processing system 10 according to the first embodiment will be described with reference to Fig. 3. Fig. 3 is a flowchart showing the operation flow of the information processing system according to the first embodiment.

[0031] 3, when the operation of the information processing system 10 according to the first embodiment is started, the score acquiring unit 120 first acquires a first score based on the output of the first inference model and a second score based on the output of the second inference model (step S101). The score acquiring unit 120 may acquire the first score and the second score simultaneously, or may acquire them sequentially.

[0032] Next, the first integrated score calculation unit 120 calculates a first integrated score based on the first score and the second score acquired by the score acquisition unit 120 (step S102). Furthermore, the second integrated score calculation unit 130 calculates a second integrated score based on the first score and the second score acquired by the score acquisition unit 120 (step S103). Note that the processes of steps S102 and S103 may be executed in tandem or simultaneously in parallel.

[0033] Next, the score determination unit 140 determines whether or not the specific state exists based on the first integrated score calculated by the first integrated score calculation unit 120 and the second integrated score calculated by the second integrated score calculation unit 130 (step S104). If it is determined that the specific state does not exist (step 104: NO), the score determination unit 140 performs inference based on the first integrated score, and the output unit 150 outputs the inference result (step S105). On the other hand, if it is determined that the specific state exists (step 104: YES), the output unit 150 outputs information other than the inference result.

[0034] (Technical Effects) Next, technical effects obtained by the information processing system 10 according to the first embodiment will be described.

[0035] As described with reference to Figures 1 to 3, in the information processing system 10 according to the first embodiment, a first integrated score and a second integrated score are calculated from the first score of the first inference model and the second score of the second inference model. Then, an inference result or information other than the inference result is output depending on the determination result using the first integrated score and the second integrated score. In this way, it is possible to output more appropriate information by taking into account the characteristics of each of the multiple models. The output taking into account the characteristics of the inference model will be described in detail in other embodiments described later.

[0036] Second Embodiment An information processing system 10 according to a second embodiment will be described with reference to Figures 4 and 5. Note that the second embodiment differs from the first embodiment described above only in some configurations and operations, and other parts may be the same as the first embodiment. Therefore, the following will describe in detail the parts that differ from the first embodiment already described, and will omit explanations of other overlapping parts as appropriate.

[0037] (Authentication Model) First, an inference model used in the information processing system 10 according to the second embodiment will be described.

[0038] In the information processing system 10 according to the second embodiment, the first inference model and the second inference model are configured as authentication models. The authentication model is a model that receives information about a subject as input and authenticates whether the subject is a registered user. The authentication model may be configured as a biometric authentication model that performs authentication processing using a biometric image (or features extracted from a biometric image), for example. For example, the authentication model may be configured as a face authentication model that performs authentication using a face image, or an iris authentication model that performs authentication using an iris image. In this case, the first model and the second model may be configured as models that receive information about different modalities as input. For example, the first inference model may be configured as a face authentication model, and the second authentication model may be configured as an iris authentication model. In this case, the information processing system 10 may be configured as an authentication system capable of performing multimodal authentication.

[0039] In the following, an example will be described in which the first inference model and the second inference model are configured as face recognition models. The first inference model is configured as a model that is broadly adapted to general faces in general (hereinafter referred to as a "general-purpose model"), and the second inference model is configured as a model specialized for certain faces (hereinafter referred to as an "expert model"). Examples of expert models include a model specialized for faces wearing masks and a model specialized for profile views.

[0040] (Flow of Operation) Next, the flow of operation of the information processing system 10 according to the second embodiment will be described with reference to Fig. 4. Fig. 4 is a flowchart showing the flow of operation of the information processing system according to the second embodiment.

[0041] 4, when the operation of the information processing system 10 according to the second embodiment is started, a facial image to be authenticated is input to each authentication model (i.e., the general-purpose model and the expert model) (step S201). Note that features extracted from the facial image may be input to the confirmation certificate model.

[0042] Next, the score acquiring unit 120 acquires the first score calculated by the generic model (step S202), and the second score calculated by the expert model (step S203).

[0043] Next, the first integrated score calculation unit 120 calculates a first integrated score based on the first score and the second score acquired by the score acquisition unit 120 (step S204). Furthermore, the second integrated score calculation unit 130 calculates a second integrated score based on the first score and the second score acquired by the score acquisition unit 120 (step S205). As already described, the second integrated score is calculated so that the weight of the first score (i.e., the score calculated by the expert model) is greater than that of the first integrated score.

[0044] Next, the score determination unit 140 determines whether the subject is in a specific state based on the first integrated score calculated by the first integrated score calculation unit 120 and the second integrated score calculated by the second integrated score calculation unit 130 (step S206). If it is determined that the subject is not in a specific state (step 206: NO), the score determination unit 140 performs authentication processing based on the first integrated score, and the output unit 150 outputs the authentication result (step S207). For example, if the first integrated score exceeds a predetermined threshold, the output unit 150 outputs information indicating authentication OK (i.e., the subject is a registered user). On the other hand, if the first integrated score does not exceed the predetermined threshold, the output unit 150 outputs information indicating authentication NG (i.e., the subject is not a registered user).

[0045] On the other hand, if it is determined that the subject is in a specific state (step 206: YES), guidance information is output to the subject as information other than the authentication result. The guidance information is, for example, information requesting the subject to perform a predetermined action. The guidance information may be information that prompts the subject to move so that the subject's face is in a position suitable for image capture. Specifically, a message such as "Please move your face closer" may be output. Alternatively, the guidance information may be information that prompts the subject to remove their mask. Specifically, a message such as "Please remove your mask" may be output. Such guidance information may be displayed on a display or output as audio, for example.

[0046] (Specific State) Next, the specific state in the information processing system 10 according to the second embodiment will be specifically described with reference to Fig. 5. Fig. 5 is a graph showing a region corresponding to the specific state in the information processing system according to the second embodiment.

[0047] 5, the specific state determined by the information processing system 10 according to the second embodiment is defined as a shaded area. Specifically, the specific state corresponds to a state in which the first score (i.e., the score of the generic model) is low, and therefore the result is rejected (determined to be someone else) even if the second score (i.e., the score of the expert model) is high.

[0048] The above-described specific state may occur, for example, when the subject is wearing a mask. When the subject is wearing a mask, even if the subject is the person in question (i.e., a registered user), the first score is calculated to be low because the face is hidden by the mask. On the other hand, if the expert model is configured as a mask-specific model, the subject can be accurately recognized even when wearing a mask, and the second score is calculated to be high. In such a state, the subject should be determined to be the person in question, but if a determination is made based only on the first score, the first integrated score will also be low due to the low first score, and there is a risk that the subject will be determined to be someone else.

[0049] However, in this embodiment, the second integrated score calculated by increasing the weight of the expert score determines whether or not the state is one in which an inappropriate determination such as that described above may be made (i.e., a specific state). If the state is one in the specific state, the authentication result is not output, and information other than the authentication result is output. Note that in this embodiment, guidance information is given as an example of information other than the authentication result, but information other than guidance information may also be output. For example, if the state is one in the specific state, alert information (i.e., information that warns that accurate authentication may not be performed) may be output.

[0050] (Technical Effects) Next, technical effects obtained by the information processing system 10 according to the second embodiment will be described.

[0051] 4 and 5, in the information processing system 10 according to the second embodiment, an authentication result is output when the specific state is not present, and information other than the authentication result is output when the specific state is present. In this way, it is possible to prevent an incorrect authentication result from being output when the specific state is present. Furthermore, by outputting guidance information when the specific state is present, it is possible to request the subject to perform a predetermined action, thereby resolving the specific state.

[0052] Third Embodiment An information processing system 10 according to a third embodiment will be described with reference to Figures 6 and 7. Note that the third embodiment illustrates an example of a method for determining the area corresponding to the specific state (see Figure 5) described in the second embodiment, and other aspects may be the same as those of the first and second embodiments. Therefore, the following will describe in detail the aspects that differ from the embodiments already described, and will omit a description of other overlapping aspects as appropriate.

[0053] (Area Setting Method) First, a method for setting an area corresponding to a specific state in the information processing system 10 according to the third embodiment will be described with reference to Fig. 6. Fig. 6 is a graph showing a method for determining an area corresponding to a specific state in the information processing system according to the third embodiment.

[0054] As shown in Figure 6, the size of the region corresponding to the specific state can be determined by determining α (i.e., a value corresponding to the width of the region). In this case, the value of α may be set based on the false positive rate (i.e., the probability of identifying a different person as the person in question) in the authentication process. For example, α may be determined so that the occurrence probability of the region corresponding to the specific state in the figure is the same as the occurrence probability of the region corresponding to the false positive rate.

[0055] By the way, the false positive rate is P ＦＡ （α） Then, the first integrated score τ １ can be calculated using the following formula (1), and the second integrated score τ ２ can be calculated as in the following formula (2).

[0056]

[0057]

[0058] As mentioned above, the second integrated score τ ２ may be calculated based on the false positive rate. （α） corresponds to the weight based on the inference accuracy, and -logP ＦＡ （α） correspond to the first score and the second score.

[0059] (Area Size Calculation) Next, a method for calculating the size of an area corresponding to a specific state in the information processing system 10 according to the third embodiment will be described with reference to Fig. 7. Fig. 7 is a graph showing a method for calculating the size of an area corresponding to a specific state in the information processing system according to the third embodiment.

[0060] 7, the occurrence probability of the area corresponding to the specific state can be calculated in the same manner as calculating the occurrence probability of the trapezoidal area in the figure. Specifically, the occurrence probability P of the area corresponding to the specific state can be calculated using the following formula (3).

[0061]

[0062] Note that c = ln10. Here, each axis is the negative logarithm of the false positive rate. Furthermore, each score is assumed to be independent (uncorrelated) from the others (i.e., it is assumed that each model has different characteristics).

[0063] (Technical Effects) Next, technical effects obtained by the information processing system 10 according to the third embodiment will be described.

[0064] 6 and 7, in the information processing system 10 according to the third embodiment, the size of the area corresponding to the specific state is set based on the false positive rate. This allows the probability of the specific state occurring to be set to an appropriate value. Therefore, for example, it is possible to prevent excessive increases in the number of times that information other than the authentication result is output due to frequent occurrence of the specific state.

[0065] Fourth Embodiment An information processing system 10 according to a fourth embodiment will be described with reference to Figures 8 to 10. Note that the fourth embodiment differs only in part of the configuration and operation from the first to third embodiments described above, and other parts may be the same as the first to third 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.

[0066] (Functional Configuration) First, the functional configuration of the information processing system 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 information processing system 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.

[0067] As shown in Fig. 8, the information processing system 10 according to the fourth embodiment is configured to include, as components for realizing its functions, a score acquisition unit 110, a first total score calculation unit 120, a second total score calculation unit 130, a score determination unit 140, an output unit 150, and a third total score calculation unit 160. That is, the information processing system 10 according to the fourth embodiment includes, in addition to the configuration of the first embodiment (see Fig. 2), a third total score calculation unit 160. Note that the third total score calculation unit 160 may be a processing block realized by, for example, the above-described processor 11 (see Fig. 1).

[0068] The third integrated score calculation unit 160 is configured to calculate the third integrated score by integrating the first score and the second score. The method of calculating the third integrated score is not particularly limited, but the third integrated score calculation unit 160 is configured to perform an integration process such that the weight of the first score is increased compared to when the first integrated score is calculated (i.e., the process by the first integrated score calculation unit 120). Therefore, the third integrated score is different from both the first integrated score and the second integrated score. The third integrated score calculated by the third integrated score calculation unit 160 is configured to be output to the score determination unit 140.

[0069] The score determination unit 140 according to the fourth embodiment is configured to perform determination using the third integrated score in addition to the first integrated score and the second integrated score. The determination operation of the score determination unit 140 will be described in detail below.

[0070] (Operation Flow) Next, the operation flow of the information processing system 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 information processing system according to the fourth embodiment. Note that in Fig. 9, the same processes as those shown in Fig. 3 are denoted by the same reference numerals.

[0071] As shown in Figure 9, when operation of the information processing system 10 related to the fourth embodiment is started, the score acquisition unit 120 first acquires a first score based on the output of the first inference model and a second score based on the output of the second inference model (step S101).

[0072] Next, the first integrated score calculation unit 120 calculates a first integrated score based on the first score and the second score acquired by the score acquisition unit 120 (step S102). Furthermore, the second integrated score calculation unit 130 calculates a second integrated score based on the first score and the second score acquired by the score acquisition unit 120 (step S103). Furthermore, the third integrated score calculation unit 160 calculates a third integrated score based on the first score and the second score acquired by the score acquisition unit 120 (step S401).

[0073] The third integrated score can be calculated in the same manner as the second integrated score. Specifically, the third integrated score can be calculated by exchanging the first score and the second score in the above-described formula (2).

[0074] Next, the score determination unit 140 determines whether or not the specific state exists based on the first integrated score calculated by the first integrated score calculation unit 120, the second integrated score calculated by the second integrated score calculation unit 130, and the third integrated score calculated by the third integrated score calculation unit 160 (step S402). In particular, the score determination unit 140 according to this embodiment determines that the specific state exists when only the second integrated score exceeds a predetermined threshold or when only the third integrated score exceeds a predetermined threshold.

[0075] If it is determined that the state is not a specific state (step 402: NO), the score determination unit 140 performs inference based on the first integrated score, and the output unit 150 outputs the inference result (step S105). On the other hand, if it is determined that the state is a specific state (step 402: YES), the output unit 150 outputs information other than the inference result.

[0076] (Multiple Specific States) Next, specific states in the information processing system 10 according to the fourth embodiment will be specifically described with reference to Fig. 10. Fig. 10 is a graph showing regions corresponding to specific states in the information processing system according to the fourth embodiment.

[0077] 10, the information processing system 10 according to the fourth embodiment determines a plurality of specific states. Specifically, in the above-described embodiments, examples have been described in which a specific state is determined in which the first score is low and the second score is high, but in this embodiment, a specific state in which the first score is high and the second score is low is also determined.

[0078] (Technical Effects) Next, technical effects obtained by the information processing system 10 according to the fourth embodiment will be described.

[0079] 8 to 10 , in the information processing system 10 according to the fourth embodiment, the specific state is determined using the third integrated score. In this way, multiple specific states can be determined, and therefore, information can be output with greater consideration given to the characteristics of each of the multiple models than when determining only one specific state.

[0080] Fifth Embodiment An information processing system 10 according to a fifth embodiment will be described with reference to Figures 11 and 12. Note that the fifth embodiment differs from the first to fourth embodiments described above only in some of its operations, and other parts may be the same as the first to fourth 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.

[0081] (Model Configuration) First, the configuration of a model used by the information processing system 10 according to the fifth embodiment will be described with reference to Fig. 11. Fig. 11 is a block diagram showing the configuration of a model used by the information processing system 10 according to the fifth embodiment.

[0082] As shown in Figure 11, the information processing system 10 according to the fifth embodiment uses one first inference model and two second inference models A and B. The first inference model outputs a first score. The second inference model A outputs a second score A. The second inference model B outputs a second score B. Therefore, the score acquisition unit 110 in the information processing system 10 according to the fifth embodiment acquires three scores: the first score, the second score A, and the second score B.

[0083] Furthermore, the second integrated score calculation unit 130 according to the fifth embodiment calculates a second integrated score for each second score for each second inference model. In other words, the second integrated score calculation unit 130 calculates a second integrated score for each second score. Specifically, the second integrated score calculation unit 130 calculates a second integrated score A from the first score and the second score A. Furthermore, the second integrated score calculation unit 130 calculates a second integrated score B from the first score and the second score B.

[0084] (Specific State) Next, the specific state in the information processing system 10 according to the fifth embodiment will be specifically described with reference to Fig. 12. Fig. 12 is a graph showing a region corresponding to the specific state in the information processing system according to the fifth embodiment.

[0085] 12, in the information processing system 10 according to the fifth embodiment, when either the first total score A or the second total score B exceeds a predetermined threshold, it is determined that the specific state exists. The size of the area corresponding to this specific state is represented by α and β in the figure. ２ α and β are defined as ２ The value of may be calculated using the formula (3) described in the third embodiment (see FIG. 7). ２ The value of may be calculated so that the occurrence probability of the specific region is the same as the occurrence probability of the region corresponding to the false positive rate.

[0086] Although the case where there are two second inference models has been described here, there may be three or more second inference models. Even in this case, a specific state can be determined using a similar method by calculating a second integrated score corresponding to each of the multiple second inference models (i.e., by calculating a second integrated score for each second score). The i-th second integrated score can be calculated, for example, using the following formula (4).

[0087]

[0088] (Technical Effects) Next, technical effects obtained by the information processing system 10 according to the fifth embodiment will be described.

[0089] 11 and 12, in the information processing system 10 according to the fifth embodiment, a plurality of second integrated scores are calculated using a plurality of second scores. In this way, even when there are a plurality of second inference models, it is possible to output more appropriate information by taking into account the characteristics of each model.

[0090] 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.

[0091] 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.

[0092] <Supplementary Notes> The above-described embodiment may be further described as in the following supplementary notes, but is not limited to the following.

[0093] (Supplementary Note 1) The information processing system described in Supplementary Note 1 is an information processing system including: a score acquisition means for acquiring a first score based on the output of a first inference model and a second score based on the output of a second inference model having characteristics different from those of the first inference model; a first integrated score calculation means for calculating a first integrated score by integrating the first score and the second score; a second integrated score calculation means for calculating a second integrated score by integrating the first score and the second score so that the weight of the second score is greater than when calculating the first integrated score; a score determination means for determining whether each of the first integrated score and the second integrated score exceeds a predetermined threshold, and for determining whether a specific state is being reached in which only the second integrated score exceeds the predetermined threshold; and an output means for outputting an inference result based on whether the first integrated score exceeds the predetermined threshold when the specific state is not being reached, and outputting information different from the inference result when the specific state is being reached.

[0094] (Appendix 2) The information processing system described in Appendix 2 is the information processing system described in Appendix 1, in which the first inference model and the second inference model are authentication models that use information about the subject as input to authenticate whether the subject is a registered user, and the output means outputs an authentication result for the subject when the subject is not in the specific state, and outputs information different from the authentication result when the subject is in the specific state.

[0095] (Supplementary Note 3) The information processing system described in Supplementary Note 3 is the information processing system described in Supplementary Note 2, wherein the output means outputs information requesting the subject to perform a predetermined action when the subject is not in the specific state.

[0096] (Supplementary Note 4) The information processing system according to Supplementary Note 4 is the information processing system according to any one of Supplementary Notes 1 to 3, in which the second integrated score is a value calculated based on a false positive rate.

[0097] (Supplementary Note 5) The information processing system described in Supplementary Note 5 is the information processing system described in any one of Supplements 1 to 4, further comprising a third integrated score calculation means that calculates a third integrated score by integrating the first score and the second score so that the weight of the first score is greater than when calculating the first integrated score, and the score determination means determines that the specific state exists when either the second integrated score or the third integrated score exceeds the predetermined threshold.

[0098] (Appendix 6) The information processing system described in Appendix 6 is the information processing system described in any one of Appendices 1 to 5, wherein the second inference model includes multiple models with different characteristics, the second integrated score means calculates the second integrated score for each of the multiple models, and the score determination means determines that the specific state exists when any of the multiple second integrated scores exceeds the predetermined threshold.

[0099] (Supplementary Note 7) The information processing method described in Supplementary Note 7 is an information processing method that, by at least one computer, obtains a first score based on the output of a first inference model and a second score based on the output of a second inference model having characteristics different from those of the first inference model, calculates a first integrated score by integrating the first score and the second score, calculates a second integrated score by integrating the first score and the second score so that the weight of the second score is greater than when calculating the first integrated score, determines whether each of the first integrated score and the second integrated score exceeds a predetermined threshold, and determines whether a specific state is present in which only the second integrated score exceeds the predetermined threshold, outputs an inference result based on whether the first integrated score exceeds the predetermined threshold if the specific state is not present, and outputs information different from the inference result if the specific state is present.

[0100] (Appendix 8) The recording medium described in Appendix 8 is a recording medium having recorded thereon a computer program for causing at least one computer to execute an information processing method, which includes obtaining a first score based on the output of a first inference model and a second score based on the output of a second inference model having characteristics different from those of the first inference model, calculating a first integrated score by integrating the first score and the second score, calculating a second integrated score by integrating the first score and the second score so that the weight of the second score is greater than when calculating the first integrated score, determining whether each of the first integrated score and the second integrated score exceeds a predetermined threshold, and determining whether a specific state is present in which only the second integrated score exceeds the predetermined threshold, outputting an inference result based on whether the first integrated score exceeds the predetermined threshold if the specific state is not present, and outputting information different from the inference result if the specific state is present.

[0101] (Supplementary Note 9) The computer program described in Supplementary Note 9 is a computer program that causes at least one computer to execute an information processing method, which includes obtaining a first score based on the output of a first inference model and a second score based on the output of a second inference model having characteristics different from those of the first inference model, calculating a first integrated score by integrating the first score and the second score, calculating a second integrated score by integrating the first score and the second score so that the weight of the second score is greater than when calculating the first integrated score, determining whether each of the first integrated score and the second integrated score exceeds a predetermined threshold, and determining whether a specific state is present in which only the second integrated score exceeds the predetermined threshold, outputting an inference result based on whether the first integrated score exceeds the predetermined threshold if the specific state is not present, and outputting information different from the inference result if the specific state is present.

[0102] (Supplementary Note 10) The information processing device described in Supplementary Note 10 is an information processing device comprising: score acquisition means for acquiring a first score based on the output of a first inference model and a second score based on the output of a second inference model having characteristics different from those of the first inference model; first integrated score calculation means for calculating a first integrated score by integrating the first score and the second score; second integrated score calculation means for calculating a second integrated score by integrating the first score and the second score so that the weight of the second score is greater than when calculating the first integrated score; score determination means for determining whether each of the first integrated score and the second integrated score exceeds a predetermined threshold, and for determining whether a specific state is being reached in which only the second integrated score exceeds the predetermined threshold; and output means for outputting an inference result based on whether the first integrated score exceeds the predetermined threshold if the specific state is not being reached, and outputting information different from the inference result if the specific state is being reached.

[0103] 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 information processing systems, information processing methods, and recording media that involve such modifications are also included in the technical idea of ​​this disclosure.

[0104] REFERENCE SIGNS LIST 10 Information processing system 11 Processor 110 Score acquisition unit 120 First total score calculation unit 130 Second total score calculation unit 140 Score determination unit 150 Output unit 160 Third total score calculation unit

Claims

1. a score acquisition means for acquiring a first score based on the output of a first inference model and a second score based on the output of a second inference model having characteristics different from those of the first inference model; a first integrated score calculation means for calculating a first integrated score by integrating the first score and the second score; a second integrated score calculation means for calculating a second integrated score by integrating the first score and the second score such that a weight of the second score is larger than that in the case of calculating the first integrated score; a score determination means for determining whether each of the first integrated score and the second integrated score exceeds a predetermined threshold and determining whether a specific state is present in which only the second integrated score exceeds the predetermined threshold; an output means for outputting an inference result based on whether or not the first integrated score exceeds the predetermined threshold when the specific state is not present, and outputting information different from the inference result when the specific state is present; An information processing system comprising:

2. the first inference model and the second inference model are authentication models that use information about a subject as input to authenticate whether the subject is a registered user, the output means outputs an authentication result of the subject when the subject is not in the specific state, and outputs information different from the authentication result when the subject is in the specific state. The information processing system according to claim 1 .

3. The output means outputs information requesting the subject to perform a predetermined action when the subject is not in the specific state. The information processing system according to claim 2 .

4. The second integrated score is a value calculated based on a false positive rate.

4. The information processing system according to claim 2 or 3.

5. a third integrated score calculation means for calculating a third integrated score by integrating the first score and the second score so that a weight of the first score is larger than that in the case of calculating the first integrated score, The score determination means determines that the specific state exists when either the second integrated score or the third integrated score exceeds the predetermined threshold. The information processing system according to claim 1 .

6. the second inference model includes a plurality of models having different characteristics from each other; The second integrated score calculation means calculates the second integrated score for each of the plurality of models, The score determination means determines that the specific state exists when any one of the plurality of second integrated scores exceeds the predetermined threshold value. The information processing system according to claim 1 .

7. by at least one computer, Obtaining a first score based on the output of a first inference model and a second score based on the output of a second inference model having characteristics different from those of the first inference model; calculating a first integrated score by integrating the first score and the second score; calculating a second integrated score by integrating the first score and the second score such that a weight of the second score is greater than that in the case of calculating the first integrated score; determining whether each of the first integrated score and the second integrated score exceeds a predetermined threshold, and determining whether a specific state is present in which only the second integrated score exceeds the predetermined threshold; When the specific state is not present, an inference result based on whether or not the first integrated score exceeds the predetermined threshold is output, and when the specific state is present, information different from the inference result is output. Information processing methods.

8. At least one computer Obtaining a first score based on the output of a first inference model and a second score based on the output of a second inference model having characteristics different from those of the first inference model; calculating a first integrated score by integrating the first score and the second score; calculating a second integrated score by integrating the first score and the second score such that a weight of the second score is greater than that in the case of calculating the first integrated score; determining whether each of the first integrated score and the second integrated score exceeds a predetermined threshold, and determining whether a specific state is present in which only the second integrated score exceeds the predetermined threshold; When the specific state is not present, an inference result based on whether or not the first integrated score exceeds the predetermined threshold is output, and when the specific state is present, information different from the inference result is output. A computer program for executing an information processing method.