WO2025120713A1 - Information processing device, information processing method, and recording medium - Google Patents

Abstract

This information processing device includes: an extraction means for extracting a plurality of feature points from a pattern image; a classification means for classifying the plurality of feature points into a plurality of clusters; a generation means for generating a fragmented image from the pattern image on the basis of the plurality of clusters; and an output means for outputting the fragmented image.

Description

Information processing device, information processing method, and recording medium

This disclosure relates to the technical fields of information processing devices, information processing methods, and recording media.

Patent Document 1 describes a technology that replaces the coordinate position of each search fingerprint feature point included in the search fingerprint data with the coordinate position of a coordinate system based on a coordinate reference point included in the file fingerprint data, and checks for the presence or absence of any singular points scattered within a coordinate range near the coordinate reference point defined in the fingerprint matching parameters for the search fingerprint feature points located around the coordinate reference point and the group of file fingerprint feature points included in the file fingerprint data, performs a zone check of the coordinate range near the coordinate reference point, and a quality check of the group of feature points located within the coordinate range near the coordinate reference point, and determines whether to use the axis matching method or the position matching method.

JP 2004-265038 A

The objective of this disclosure is to provide an information processing device, an information processing method, and a recording medium that aim to improve upon the technology disclosed in the prior art documents.

One aspect of the information processing device includes an extraction means for extracting a plurality of feature points from a pattern image, a classification means for classifying the plurality of feature points into a plurality of clusters, a generation means for generating a fragmented image from the pattern image based on the plurality of clusters, and an output means for outputting the fragmented image.

One aspect of the information processing method is to extract a plurality of feature points from a pattern image, classify the plurality of feature points into a plurality of clusters, generate a fragmented image from the pattern image based on the plurality of clusters, and output the fragmented image.

In one embodiment of the recording medium, a computer program is recorded to cause a computer to execute an information processing method that extracts a plurality of feature points from a pattern image, classifies the plurality of feature points into a plurality of clusters, generates a fragmented image from the pattern image based on the plurality of clusters, and outputs the fragmented image.

FIG. 1 is a block diagram showing an example of a configuration of an information processing device according to the present disclosure. FIG. 1 is a block diagram showing an example of a configuration of an information processing device according to the present disclosure. 11 is a flowchart illustrating an example of an information processing operation of the information processing device according to the present disclosure. FIG. 1 is a conceptual diagram illustrating an example of an information processing operation of an information processing device according to the present disclosure. FIG. 1 is a block diagram showing an example of a configuration of an information processing device according to the present disclosure. FIG. 1 is a block diagram showing an example of a configuration of an information processing device according to the present disclosure. FIG. 1 is a block diagram showing an example of a configuration of an information processing device according to the present disclosure.

Hereinafter, embodiments of an information processing device, an information processing method, and a recording medium will be described with reference to the drawings.
[1: First embodiment]

A first embodiment of an information processing device, an information processing method, and a recording medium will be described. Hereinafter, a first embodiment of an information processing device, an information processing method, and a recording medium will be described using an information processing device 1 according to this disclosure.
[1-1: Configuration of information processing device 1]

FIG. 1 is a block diagram showing the configuration of an information processing device 1 according to this disclosure. As shown in FIG. 1, the information processing device 1 includes an extraction unit 11, a classification unit 12, a generation unit 13, and an output unit 14.

The extraction unit 11 extracts a plurality of feature points from the pattern image. The classification unit 12 classifies the plurality of feature points into a plurality of clusters. The generation unit 13 generates a fragmented image from the pattern image based on the plurality of clusters. The output unit 14 outputs the fragmented image.

In this disclosure, a fingerprint image is an image of a fingerprint including a plurality of feature points. A fingerprint image is an image that can be acquired from a living body, and includes at least one of a fingerprint, a palm print, and a foot print.
[1-2: Technical Effects of Information Processing Device 1]

The information processing device 1 according to this disclosure generates a fragmented image based on clusters of feature points. Therefore, the information processing device 1 can include meaningful areas of the pattern image in the fragmented image. Because the fragmented image is generated based on clusters of feature points, it is unlikely to include meaningless areas, such as areas that do not include feature points. The information processing device 1 can increase the amount of meaningful areas of the pattern image that are included in the area included in the fragmented image.
[2: Second embodiment]

A second embodiment of an information processing device, an information processing method, and a recording medium will be described. Hereinafter, a second embodiment of an information processing device, an information processing method, and a recording medium will be described using an information processing device 2 according to this disclosure.
[2-1: Configuration of information processing device 2]

FIG. 2 is a block diagram showing the configuration of the information processing device 2. As shown in FIG. 2, the information processing device 2 includes a calculation device 21 and a storage device 22. Furthermore, the information processing device 2 may include a communication device 23, an input device 24, and an output device 25. However, the information processing device 2 does not have to include at least one of the communication device 23, the input device 24, and the output device 25. The calculation device 21, the storage device 22, the communication device 23, the input device 24, and the output device 25 may be connected via a data bus 26.

The arithmetic device 21 includes, for example, at least one of a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), and an FPGA (Field Programmable Gate Array). The arithmetic device 21 reads a computer program. For example, the arithmetic device 21 may read a computer program stored in the storage device 22. For example, the arithmetic device 21 may read a computer program stored in a computer-readable and non-transient recording medium using a recording medium reading device (e.g., an input device 24 described later) not shown in the figure that is provided in the information processing device 2. The arithmetic device 21 may acquire (i.e., download or read) a computer program from a device (not shown) located outside the information processing device 2 via the communication device 23 (or other communication device). The arithmetic device 21 executes the read computer program. As a result, a logical function block for executing the operation to be performed by the information processing device 2 is realized within the calculation device 21. In other words, the calculation device 21 can function as a controller for realizing a logical function block for executing the operation (in other words, processing) to be performed by the information processing device 2. The calculation device 21 may output information to a device (not shown), such as another computer or a cloud server, that is provided outside the information processing device 2, via the communication device 23 (or another communication device).

2 shows an example of a logical functional block realized in the arithmetic device 21 to execute information processing operations. As shown in FIG. 2, the arithmetic device 21 realizes an extraction unit 211, which is a specific example of an "extraction means" described in the appendix described later, a classification unit 212, which is a specific example of a "classification means" described in the appendix described later, a generation unit 213, which is a specific example of a "generation means" described in the appendix described later, an output unit 214, which is a specific example of an "output means" described in the appendix described later, an acquisition unit 215, and a reception unit 216, which is a specific example of a "reception means" described in the appendix described later. However, at least one of the acquisition unit 215 and the reception unit 216 may not be realized in the arithmetic device 21. Details of the operations of the extraction unit 211, classification unit 212, generation unit 213, output unit 214, acquisition unit 215, and reception unit 216 will be described later with reference to FIG. 3.

The storage device 22 can store desired data. For example, the storage device 22 may temporarily store a computer program executed by the arithmetic device 21. The storage device 22 may temporarily store data that is temporarily used by the arithmetic device 21 when the arithmetic device 21 is executing a computer program. The storage device 22 may store data that the information processing device 2 stores for a long period of time. The storage device 22 may include at least one of a RAM (Random Access Memory), a ROM (Read Only Memory), a hard disk device, a magneto-optical disk device, an SSD (Solid State Drive), and a disk array device. In other words, the storage device 22 may include a non-temporary recording medium.

The communication device 23 is capable of communicating with devices external to the information processing device 2 via a communication network (not shown). The communication device 23 may be a communication interface based on standards such as Ethernet (registered trademark), Wi-Fi (registered trademark), Bluetooth (registered trademark), and USB (Universal Serial Bus).

The input device 24 is a device that accepts information input to the information processing device 2 from outside the information processing device 2. For example, the input device 24 may include an operating device (e.g., at least one of a keyboard, a mouse, and a touch panel) that can be operated by an operator of the information processing device 2. For example, the input device 24 may include a reading device that can read information recorded as data on a recording medium that can be attached externally to the information processing device 2.

The output device 25 is a device that outputs information to the outside of the information processing device 2. For example, the output device 25 may output information as an image. That is, the output device 25 may include a display device (so-called a display) capable of displaying an image showing the information to be output. For example, the output device 25 may output information as sound. That is, the output device 25 may include an audio device (so-called a speaker) capable of outputting sound. For example, the output device 25 may output information on paper. That is, the output device 25 may include a printing device (so-called a printer) capable of printing desired information on paper.
[2-2: Information Processing Operation Performed by Information Processing Device 2]

The information processing operation performed by the information processing device 2 will be described with reference to FIG. 3. FIG. 3 is a flowchart showing an example of the flow of the information processing operation performed by the information processing device 2.

As shown in FIG. 3(a), the acquisition unit 215 acquires a pattern image (step S20). The acquisition unit 215 may acquire a pattern image stored in the storage device 22. The acquisition unit 215 may also acquire a pattern image from an external device via the communication device 23. The extraction unit 211 extracts a plurality of feature points from the acquired pattern image (step S21). FIG. 4(a) illustrates an example of a plurality of feature points x extracted from the pattern image PI.

The reception unit 216 receives the designation of the predetermined number (n) (step S22). The reception unit 216 may receive the designation of the predetermined number (n) input by the user to the input device 24. The operation of step S22 may be performed before step S20. Furthermore, the operation of step S22 is not a required operation in one information processing operation, and may not be performed in some cases. For example, when the user checks information related to feature points extracted from the pattern image and recognizes that a designation is necessary, the reception unit 216 may receive the input of the designation of the predetermined number (n).

Each of the feature points contained in the pattern image can form a cohesive group, i.e., a cluster. The classification unit 212 classifies the feature points into a number of clusters (step S23). The feature points classified into a cluster are called feature points belonging to the cluster. Details of the operation of step S23 will be described with reference to FIG. 3(b).

As shown in FIG. 3(b), the classification unit 212 determines the number of clusters (C) to be classified from the number (N) of extracted feature points and a predetermined number such that each of the multiple clusters contains a predetermined number (n) of feature points from the multiple feature points (step S23-1). The classification unit 212 may determine the number of clusters (C) by dividing the number (N) of extracted feature points by the predetermined number (n). In other words, when a pattern image contains a relatively large number of feature points, the classification unit 212 classifies the pattern image into a relatively large number of clusters. Also, when a pattern image contains a relatively small number of feature points, the classification unit 212 classifies the pattern image into a relatively small number of clusters.

The classification unit 212 selects C feature points as initial representative points (step S23-2). The classification unit 212 may select the initial representative points randomly. Alternatively, the classification unit 212 may select feature points that are appropriately separated from each other as the initial representative points.

The classification unit 212 repeats a first operation (step S23-3) of classifying the multiple feature points into C clusters each including n feature points centered on a representative point, and a second operation (step S23-4) of selecting a new representative point from each of the classified C clusters. The classification unit 212 may repeat the first and second operations until the classified clusters become cohesive and meaningful groups (step S23-5: yes). Figure 4(b) illustrates multiple feature points x classified into five clusters C1, C2, C3, C4, and C5.

The generating unit 213 generates fragmented images from the pattern image based on the multiple clusters (step S24). The generating unit 213 generates multiple fragmented images corresponding to each of the multiple clusters. The generating unit 213 generates fragmented images so as to include feature points belonging to the corresponding clusters. The generating unit 213 generates fragmented images according to the distribution of each of the feature points belonging to the corresponding clusters. In other words, the generating unit 213 generates fragmented images corresponding to clusters so that areas where feature points included in the clusters are not distributed are small. Figure 4(c) illustrates fragmented images I1, I2, I3, I4, and I5 generated from the pattern image based on five clusters C1, C2, C3, C4, and C5. The shapes of the fragmented images will be described in other embodiments.

The generation unit 213 may generate a fragmented image based on the distance between the representative point of the cluster and each of the feature points belonging to the cluster. The representative point of the cluster may be a feature point located at the center of the cluster, or may be a feature point located at the center of gravity of the cluster. Alternatively, the representative point of the cluster may be a position where no feature point exists.

The output unit 214 outputs the fragmented image (step 25). The output unit 214 may control a display as the output device 25 to display the fragmented image. The output unit 214 may control the storage device 22 to store the fragmented image in the storage device 22. The output unit 214 may control the communication device 23 to transmit the fragmented image to an external device.
[2-3: Modifications]

The information processing device 2 may be configured to allow the number of necessary fragmented images to be specified. In this case, the receiving unit 216 may receive a specification of the desired number (C) of fragmented images. The receiving unit 216 may receive a specification of the desired number (C) input by the user to the input device 24. When the number of necessary fragmented images is specified, the number of feature points contained in the fragmented images changes depending on the number of feature points contained in the pattern image.
[2-4-1: Necessity of fragmented images 1]

Machine learning is used for image enhancement processing and feature extraction processing in fingerprint matching. Machine learning requires a large amount of training data.

Fingerprint matching may be performed using a fragmentary fingerprint image, such as a latent fingerprint image, which shows only a partial area of the entire fingerprint, rather than a fingerprint image that includes the entire fingerprint, such as an imprint fingerprint image. When machine learning is used for various processes in matching latent fingerprints, a large amount of learning data of fragmentary fingerprint images is required. However, there are not many samples of fragmentary fingerprint images that are useful for machine learning. For example, a fragmentary fingerprint image of an area that does not include minutiae is not useful for matching, and is not useful for machine learning either. Therefore, for example, a fragmentary fingerprint image obtained by randomly dividing an imprint fingerprint is often not useful for machine learning. The information processing device 2 disclosed herein is useful for mass generation of fragmentary fingerprint images that are useful for machine learning.

Furthermore, latent fingerprint images often contain information other than fingerprints. For this reason, the information processing device 2 may prepare a fragmented fingerprint image that resembles a latent fingerprint by compositing a shading or other image as a background, as learning data to be used in machine learning for various processes in matching latent fingerprints.
[2-4-2: Necessity of fragmented images 2]

Compared to fingerprint images, palm print images are larger in size and have more feature points to detect. As the number of feature points increases, the matching cost increases. In addition, palm print images have a wide distribution of feature points, so they are susceptible to image distortion. This makes it difficult, for example, to overlay the entire palm print image. As such, matching the entire palm print image is difficult. Also, the entire palm print image often contains areas that are difficult to use for matching.

By generating a fragmented palm print image, the number of feature points extracted from individual images can be reduced, and matching costs can be reduced. Also, by including an area useful for matching palm print images in the fragmented palm print image, the effects of distortion can be suppressed, important information is not missed, and unnecessary matching can be reduced. The information processing device 2 disclosed herein is useful for generating a fragmented palm print image including an area useful for matching palm print images.
[2-5: Technical Effects of Information Processing Device 2]

The information processing device 2 disclosed herein determines the shape of a fragmented image corresponding to a cluster based on at least one of the distance between the representative point of the cluster and each of the feature points belonging to the cluster and the shape of the distribution of each of the feature points belonging to the cluster, and is therefore able to generate a fragmented image that includes many meaningful areas that include feature points. The information processing device 2 can treat the areas included in each fragmented image as independently meaningful areas.

Since the information processing device 2 generates each fragmented image so as to include a predetermined number of feature points, each fragmented image has an equal amount of information. Since the number of feature points included in a fragmented image or the number of fragmented images can be specified, the information processing device 2 can generate fragmented images having a desired amount of information.
[3: Third embodiment]

A third embodiment of an information processing device, an information processing method, and a recording medium will be described below. Hereinafter, a third embodiment of an information processing device, an information processing method, and a recording medium will be described using an information processing device 3 according to this disclosure. In the third embodiment, the operation of the generating unit 313 is different from that of the second embodiment.
[3-1: Information Processing Operation Performed by Information Processing Device 3]

The generating unit 313 generates an elliptical fragmented image. The generating unit 313 may determine the elliptical shape according to the distribution of each of the feature points belonging to the cluster. The generating unit 313 may determine the elliptical shape based on the shape of the distribution of each of the feature points belonging to the cluster.

The generating unit 313 may determine the shape of an ellipse based on the distance between the representative point of a cluster and each of the feature points belonging to the cluster. For example, the generating unit 313 may determine a major axis based on the distance between the representative point of a cluster and each of the feature points belonging to the cluster corresponding to the representative point, and generate a fragmented image corresponding to the cluster based on an ellipse that can be defined by this major axis. The generating unit 313 may determine the major axis of the ellipse based on the longest distance between the representative point of a cluster and each of the feature points belonging to the cluster corresponding to the representative point.

The generating unit 313 may generate a circular fragmented image. The generating unit 313 may determine the radius of the circle based on the longest distance between the representative point of the cluster and each of the feature points belonging to the cluster corresponding to the representative point.
[3-2: Technical Effects of Information Processing Device 3]
The information processing device 3 disclosed herein generates a fragmented image corresponding to a cluster based on an ellipse whose major axis is determined based on the distance between the representative point of the cluster and each of the feature points belonging to the cluster corresponding to the representative point, and can therefore generate a fragmented image according to the distribution shape of each of the feature points belonging to the cluster.
[4: Fourth embodiment]

A fourth embodiment of an information processing device, an information processing method, and a recording medium will be described below. Hereinafter, a fourth embodiment of an information processing device, an information processing method, and a recording medium will be described using an information processing device 4 according to this disclosure. In the fourth embodiment, the operation of the generating unit 413 is different from the second and third embodiments.
[4-1: Information Processing Operation Performed by Information Processing Device 4]

The generating unit 413 generates a polygonal fragmentation image. The polygon may be a convex polygon. The generating unit 413 may determine the polygonal shape according to the distribution of each of the feature points belonging to the cluster. The generating unit 413 may determine the polygonal shape based on the shape of the distribution of each of the feature points belonging to the cluster. The generating unit 413 may determine the polygonal shape based on the distance between the representative point of the cluster and each of the feature points belonging to the cluster.
[4-2: Technical Effects of Information Processing Device 4]

The information processing device 4 according to this disclosure generates polygonal fragmented images, and therefore can generate fragmented images according to the distribution shape of each of the feature points belonging to a cluster.
[5: Fifth embodiment]

A fourth embodiment of an information processing device, an information processing method, and a recording medium will be described below. A fifth embodiment of an information processing device, an information processing method, and a recording medium will be described below using an information processing device 5 according to this disclosure. The fifth embodiment differs from the second to fourth embodiments in that a determination unit 517 is realized in the calculation device 21.
[5-1: Information Processing Operation Performed by Information Processing Device 5]

The determination unit 517 determines whether the fragmented image should be elliptical or polygonal in shape, depending on the distribution of each of the feature points belonging to the cluster. For each of the multiple clusters, the determination unit 517 determines whether the fragmented image corresponding to the cluster should be elliptical or polygonal.

The determining unit 517 may determine whether the fragmented image should be elliptical or polygonal in shape, depending on the distribution of each of the feature points belonging to the cluster. The determining unit 517 may determine whether the fragmented image should be elliptical or polygonal, based on the shape of the distribution of each of the feature points belonging to the cluster (referred to as the "distribution shape"). The determining unit 517 may determine which of a plurality of predefined shapes the distribution shape is. The predefined shapes include an ellipse and a polygon including at least one of a triangle, a rectangle, a pentagon, and a hexagon. Any of the predefined shapes determined by the determining unit 517 is referred to as the "area shape". Alternatively, the determining unit 517 may determine whether the fragmented image should be elliptical or polygonal, based on the distance between the representative point of the cluster and each of the feature points belonging to the cluster.

Alternatively, the information processing device 5 may accept the area shape specified by the user who has confirmed the distribution shape.

The generating unit 513 generates a fragmented image of the region shape. The generating unit 513 may deform the region shape so that the region shape includes the region of the distribution shape and the region other than the region of the distribution shape is reduced, and generate a fragmented image of the deformed region shape.

The deformation of the region shape by the generation unit 513 may include enlarging or reducing the region shape. The deformation of the region shape by the generation unit 513 may include deformation to make any side of a polygon convex inwardly of the region shape. The deformation of the region shape by the generation unit 513 may include changing the length of at least one of the major axis and minor axis of an elliptical shape.
[5-2: Technical Effects of Information Processing Device 5]

The information processing device 5 disclosed herein determines whether the shape of a fragmented image should be elliptical or polygonal, and can generate a fragmented image according to the distribution shape of each of the feature points belonging to a cluster.
[6: Notes]

The following supplementary notes are further disclosed regarding the above-described embodiment.
[Appendix 1]
An extraction means for extracting a plurality of feature points from a pattern image;
A classification means for classifying the plurality of feature points into a plurality of clusters;
a generating means for generating a fragmented image from the pattern image based on the plurality of clusters;
and an output means for outputting the fragmented image.
[Appendix 2]
The information processing device according to claim 1, wherein each of the plurality of clusters includes a predetermined number of feature points from the plurality of feature points.
[Appendix 3]
The information processing device according to claim 2, further comprising a reception unit configured to receive the designation of the predetermined number.
[Appendix 4]
The generating means generates a plurality of the fragmentation images corresponding to each of the plurality of clusters,
The information processing device according to claim 1, wherein the fragmented image includes feature points that belong to a corresponding cluster.
[Appendix 5]
The information processing device according to claim 1, wherein the generating means generates, for each of the plurality of clusters, the fragmented image corresponding to the cluster in accordance with a distribution of each of the feature points belonging to the cluster.
[Appendix 6]
The information processing device according to claim 1, wherein the generating means generates a plurality of the fragmented images corresponding to each of the plurality of clusters based on a distance between a representative point of each of the plurality of clusters and each of feature points belonging to each of the plurality of clusters.
[Appendix 7]
The information processing device according to claim 6, wherein the generating means generates the fragmented image corresponding to the cluster based on an ellipse whose major axis is determined based on a distance between the representative point and each of the feature points belonging to the cluster corresponding to the representative point.
[Appendix 8]
The information processing device according to claim 1, wherein the generating means determines, for each of the plurality of clusters, a shape of the fragmented image corresponding to the cluster based on a distribution shape of each of the feature points belonging to the cluster.
[Appendix 9]
The information processing device according to claim 8, wherein the generating means generates the fragmented image having a polygonal shape.
[Appendix 10]
The information processing device according to claim 9, wherein the polygonal shape is a convex polygonal shape.
[Appendix 11]
The information processing device according to claim 5, further comprising a determination means for determining whether the shape of the fragmented image is to be an ellipse or a polygon.
[Appendix 12]
The information processing device according to claim 1, further comprising a reception unit configured to receive a designation of a desired number of the fragmented images.
[Appendix 13]
The information processing device according to claim 1, wherein the pattern image includes at least one of a fingerprint image and a palm print image.
[Appendix 14]
Extract multiple feature points from the pattern image,
classifying the feature points into a plurality of clusters;
generating a fragmented image from the pattern image based on the plurality of clusters;
and outputting the fragmented image.
[Appendix 15]
On the computer,
Extract multiple feature points from the pattern image,
classifying the feature points into a plurality of clusters;
generating a fragmented image from the pattern image based on the plurality of clusters;
A recording medium on which a computer program for executing an information processing method for outputting the fragmented image is recorded.

Although this disclosure has been described above with reference to the embodiments, this disclosure is not limited to the above-mentioned embodiments. Various modifications that can be understood by those skilled in the art can be made to the configuration and details of this disclosure within the scope of this disclosure. Furthermore, each embodiment can be combined with other embodiments as appropriate.

1, 2, 3, 4, 5 Information processing device 11, 211 Extraction unit 12, 212 Classification unit 13, 213, 313, 413, 513 Generation unit 14, 214 Output unit 215 Acquisition unit 216 Reception unit 517 Determination unit

Claims (15)

An extraction means for extracting a plurality of feature points from a pattern image;
A classification means for classifying the plurality of feature points into a plurality of clusters;
a generating means for generating a fragmented image from the pattern image based on the plurality of clusters;
and an output means for outputting the fragmented image. The information processing apparatus according to claim 1 , wherein each of a plurality of clusters includes a predetermined number of the plurality of feature points. The information processing apparatus according to claim 2 , further comprising a reception unit for receiving a designation of the predetermined number. The generating means generates a plurality of the fragmentation images corresponding to each of the plurality of clusters,
The information processing apparatus according to claim 1 , wherein the fragmented image includes feature points that belong to a corresponding cluster. The information processing apparatus according to claim 1 , wherein the generating means generates, for each of the plurality of clusters, the fragmented image corresponding to the cluster in accordance with a distribution of each of the feature points belonging to the cluster. The information processing apparatus according to claim 1 , wherein the generating means generates a plurality of the fragmented images corresponding to each of the plurality of clusters based on a distance between a representative point of each of the plurality of clusters and each of feature points belonging to each of the plurality of clusters. The information processing device according to claim 6 , wherein the generating means generates the fragmented image corresponding to the cluster based on an ellipse whose major axis is determined based on a distance between the representative point and each of the feature points belonging to the cluster corresponding to the representative point. The information processing apparatus according to claim 1 , wherein the generating means determines, for each of the plurality of clusters, a shape of the fragmented image corresponding to the cluster based on a distribution shape of each of the feature points belonging to the cluster. The information processing apparatus according to claim 8 , wherein the generating means generates the fragmented image having a polygonal shape. The information processing device according to claim 9 , wherein the polygonal shape is a convex polygonal shape. The information processing apparatus according to claim 5 , further comprising a determination unit that determines whether the shape of the fragmented image is to be an ellipse or a polygon. The information processing apparatus according to claim 1 , further comprising a reception unit that receives a designation of a desired number of the fragmented images. The information processing device according to claim 1 , wherein the pattern image includes at least one of a fingerprint image and a palm print image. Extract multiple feature points from the pattern image,
classifying the feature points into a plurality of clusters;
generating a fragmented image from the pattern image based on the plurality of clusters;
and outputting the fragmented image. On the computer,
Extract multiple feature points from the pattern image,
classifying the feature points into a plurality of clusters;
generating a fragmented image from the pattern image based on the plurality of clusters;
A recording medium on which a computer program for executing an information processing method for outputting the fragmented image is recorded.