Abstract: An information processing device includes an extraction unit that extracts a core line from an image containing dermatoglyphic pattern; an identification unit that identifies an evaluation value indicating possibility of the dermatoglyphic pattern of the core line; and a correction unit that corrects the image based on the evaluation value.

Abstract: A printing apparatus includes a carriage mounted with a printing unit; a first motor configured to move the carriage in a predetermined direction; a second motor configured to move the carriage in the predetermined direction; and a control unit. The control unit executes a first mode in which, when causing the printing unit to perform printing under a first printing condition, the first motor is driven in a first direction and the second motor is driven in the first direction, and a second mode in which, when causing the printing unit to perform printing under a second printing condition different from the first printing condition, the first motor is driven in the first direction and the second motor is driven in the first direction while changing an output ratio between the first motor and the second motor.

Abstract: An image processing apparatus includes a first skeleton extraction means, a second skeleton extraction means, and an integration unit. The first skeleton extraction means discriminates a valid region and an invalid region in a fingerprint image which is input, and extracts skeleton lines in the valid region. The second skeleton extraction means extracts skeleton lines in the invalid region. The integration unit generates a skeleton image by integrating the skeleton lines extracted in the valid region and the skeleton lines extracted in the invalid region.

Abstract: In a model generation apparatus, a training data selection means selects sets of training data from a plurality of fingerprint images. A learning means trains a model that corrects a fingerprint image, by using the sets of training data. An evaluation value calculation means calculates evaluation values of results acquired by inputting the plurality of fingerprint images into the trained model. A model update means updates a model to be trained, based on the evaluation values. Next, the training data selection means determines sets of training data to be selected from the plurality of fingerprint images based on the evaluation values.

Abstract: In a model generation apparatus, a training data selection means selects sets of training data from a plurality of fingerprint images. A learning means trains a model that corrects a fingerprint image, by using the sets of training data. An evaluation value calculation means calculates evaluation values of results acquired by inputting the plurality of fingerprint images into the trained model. A model update means updates a model to be trained, based on the evaluation values. Next, the training data selection means determines sets of training data to be selected from the plurality of fingerprint images based on the evaluation values.

Abstract: An image processing device outputs, by using a result of machine learning, an image including three or more gradations and indicating a central line in response to an input of a fingerprint image, the machine learning being performed by using data including at least a fingerprint image, central line information indicating a central line in the fingerprint image, and zone information indicating a zone that is a portion of the fingerprint image which is effective for fingerprint collation, and generates a binary central line image based on the output image.

Abstract: An information processing device includes: an acquisition unit that acquires a rolled print and a slap print on the same finger; an extraction unit that extracts first feature points from the rolled print and second feature points from the slap print, respectively; and an estimation unit that, based on a ratio occupied by the first feature points having no correspondence with the second feature points out of the first feature points included in a common region that is common to the rolled print and the slap print, estimates a first work amount of correction work performed by a user on a non-common region excluding the common region from the rolled print.

Abstract: A fingerprint image display means displays an input fingerprint image being a fingerprint image which is input and a registered fingerprint image being a fingerprint image which has been registered. A marker acquisition means acquires a position of an input marker which is input by a user with respect to the input fingerprint image being displayed. A marker applying means applies a correspondence marker at a position corresponding to an input marker in the registered fingerprint image. A matching means matches the input fingerprint image to which the input marker is input with the registered fingerprint image to which the correspondence marker has been applied.

Abstract: A fingerprint processing device includes a match processing unit configured to determine, based on a first degree, a plurality of feature points having a large value of the first degree among the feature points of a fingerprint specified in a fingerprint image of a matching source, the first degree representing a first distance to other feature points, as representative feature points used in match processing of the fingerprint.

Abstract: An image processing apparatus includes an image division unit, an image processing unit, and an integration unit. The image division unit divides a palm print image into a plurality of divisional images. The image processing unit determines a valid region in each of the plurality of divisional images by using a fingerprint model, performs image processing that extracts core lines in each valid region, and generates respective processed divisional images corresponding to the divisional images. The integration unit generates an entire processed image corresponding to an entire of the palm print image by integrating the processed divisional images.

Abstract: An image processing apparatus includes a first skeleton extraction means, a second skeleton extraction means, and an integration unit. The first skeleton extraction means discriminates a valid region and an invalid region in a fingerprint image which is input, and extracts skeleton lines in the valid region. The second skeleton extraction means extracts skeleton lines in the invalid region. The integration unit generates a skeleton image by integrating the skeleton lines extracted in the valid region and the skeleton lines extracted in the invalid region.

Abstract: A stripe pattern image collating device according to the example embodiment includes a feature extracting unit that extracts a feature point and a skeleton from a first stripe pattern image and a second stripe pattern image in which a stripe pattern is formed of ridges, and generates feature point data and skeleton data. A skeleton collating unit that collates two sets of pieces of the feature point data and pieces of the skeleton data that are extracted from each of the first stripe pattern image and the second stripe pattern image, and calculates a collation score. An image analyzing unit that analyzes the second stripe pattern image with respect to an area in which an opposite feature point pair of the first stripe pattern image exists, calculates an image analysis score, and corrects the collation score.

Abstract: In a model generation apparatus, a training data selection means selects sets of training data from a plurality of fingerprint images. A learning means trains a model that corrects a fingerprint image, by using the sets of training data. An evaluation value calculation means calculates evaluation values of results acquired by inputting the plurality of fingerprint images into the trained model. A model update means updates a model to be trained, based on the evaluation values. Next, the training data selection means determines sets of training data to be selected from the plurality of fingerprint images based on the evaluation values.

Abstract: A fingerprint processing device includes a match processing unit configured to determine, based on a first degree, a plurality of feature points having a large value of the first degree among the feature points of a fingerprint specified in a fingerprint image of a matching source, the first degree representing a first distance to other feature points, as representative feature points used in match processing of the fingerprint.

Abstract: A fingerprint image display means displays an input fingerprint image being a fingerprint image which is input and a registered fingerprint image being a fingerprint image which has been registered. A marker acquisition means acquires a position of an input marker which is input by a user with respect to the input fingerprint image being displayed. A marker applying means applies a correspondence marker at a position corresponding to an input marker in the registered fingerprint image. A matching means matches the input fingerprint image to which the input marker is input with the registered fingerprint image to which the correspondence marker has been applied.

Abstract: An image processing device outputs, by using a result of machine learning, an image including three or more gradations and indicating a central line in response to an input of a fingerprint image, the machine learning being performed by using data including at least a fingerprint image, central line information indicating a central line in the fingerprint image, and zone information indicating a zone that is a portion of the fingerprint image which is effective for fingerprint collation, and generates a binary central line image based on the output image.

Abstract: A fingerprint processing device includes a match processing unit configured to determine, based on a first degree, a plurality of feature points having a large value of the first degree among the feature points of a fingerprint specified in a fingerprint image of a matching source, the first degree representing a first distance to other feature points, as representative feature points used in match processing of the fingerprint.

Abstract: A fingerprint processing device includes a match processing unit configured to determine, based on a first degree, a plurality of feature points having a large value of the first degree among the feature points of a fingerprint specified in a fingerprint image of a matching source, the first degree representing a first distance to other feature points, as representative feature points used in match processing of the fingerprint.

Abstract: A fingerprint processing device includes a match processing unit configured to determine, based on a first degree, a plurality of feature points having a large value of the first degree among the feature points of a fingerprint specified in a fingerprint image of a matching source, the first degree representing a first distance to other feature points, as representative feature points used in match processing of the fingerprint.

Abstract: A fingerprint minutia display input device includes: an image output controller for displaying a fingerprint image and a pointer for designating a minutia; and a minutia input setting unit for displaying a minutia display mark on the minutia; wherein the fingerprint minutia display input device includes a gray-scale value adjustment unit for setting the gray-scale intensity of the entire fingerprint image on which the pointer is positioned, the setting being performed within a range of a display gray-scale width constituted with upper-limit and lower-limit gray-scale values. The screen output controller includes a gray-scale width update function for displaying an updated output display of the fingerprint image in the set display gray-scale width, and a gray-scale width fixing/holding function for fixedly holding the set display gray-scale width as a set display gray-scale width for the minutia display input when there is no new setting information regarding the display gray-scale width.