Abstract:
A basis image is converted into a more functional image by image synthesis technology using a number of imaging devices arranged independently of each other. A multocular imaging system is provided with a plurality of imaging parts that can be arranged independently of each other, a similar component search part operable to acquire a basis image outputted from at least one imaging part of the plurality of imaging parts and a reference image outputted from another imaging part of the plurality of imaging parts and to search a similar component included in the reference image for each of components included in the basis image, and an image synthesis part operable to perform a synthesis process on at least one component included in the basis image into a desired image with reference to the similar component extracted by the similar component search part and to output the desired image as a synthesis image.
Abstract:
An authentication device includes an image acquisition unit, an identification unit, and an authentication unit. The image acquisition unit acquires an image of an eye of a subject. The identification unit identifies the colored pattern of a colored contact lens worn by the subject by comparing a reference image with the image of the eye. The authentication unit identifies the subject, using a feature in a region other than a colored region of the colored pattern in the iris region of the eye.
Abstract:
The learning device includes a metric space learning unit and a case example storage unit. The metric space learning unit learns a metric space including feature vectors extracted from attributed image data, for each combination of different attributes, using the attributed image data to which attribute information is assigned. The case example storage unit computes the feature vector from the image data for case example to store the computed feature vector as a case example associated with the metric space, and stores additional information associated with the case example.
Abstract:
An information providing device according to one aspect of the present disclosure includes: at least one memory storing a set of instructions; and at least one processor configured to execute the set of 5 instructions to: receive a face image; determine whether a person in the face image is unsuitable for iris data acquisition based on the face image; and output information based on determining that the person is unsuitable for the iris data acquisition when the person is determined to be unsuitable for the iris data acquisition.
Abstract:
The disclosure is detecting an authentication target who is moving in a predetermined direction in a video; inputting a first image in which an entire body of the target; calculating characteristic information from an image of the entire body in the first image, comparing the calculated characteristic information with characteristic information of the entire body stored in first memory that stores characteristic information of entire bodies of targets, and extracting candidate information of the targets from the first memory based on a first authentication result; inputting a second image in which an iris of the target; and comparing characteristic information of irises stored in second memory that stores the characteristic information of the irises of targets with characteristic information of an iris from the second image, calculating a verification score, executing second authentication on the target in the second image based on the verification score, and outputting an authentication result.
Abstract:
The disclosure is inputting a first image obtained by capturing an object of authentication moving in a specific direction; inputting a second image at least for one eye obtained by capturing a right eye or a left eye of the object; determining whether the second image is of the left eye or the right eye of the object, based on information including the first image, and outputting a determination result associated with the second image as left/right information; comparing characteristic information relevant to the left/right information, the characteristic information being acquired from a memory that stores the characteristic information of a right eye and a left eye pertaining to object to be authenticated, with characteristic information associated with the left/right information, and calculating a verification score; and authenticating the object captured in the first image and the second image, based on the verification score, and outputting an authentication result.
Abstract:
In a data augmentation device, a data acquisition means acquires two sets of source domain data of a predetermined class from a data group of a source domain, and acquires one set of target domain data of the predetermined class from a data group of a target domain data. An estimation means estimates a structure of a manifold representing a data distribution of the source domain by using two sets of source domain data. A data generation means generates new data of the target domain by using the one set of target domain data and the structure of the manifold.
Abstract:
An authentication system according to one aspect of the present disclosure includes: at least one memory storing a set of instructions; and at least one processor configured to execute the set of instructions to: track an object included in a video captured by a first capture device; detect a candidate for biometric authentication in the object being tracked; determine whether biometric authentication has been performed for the candidate based on a record of biometric authentication performed for the object being tracked; and perform the biometric authentication for the candidate based on a video of an authentication part of the candidate when the biometric authentication has not been performed for the candidate, the video of the authentication part being captured by a second capture device having a capture range including a part of a capture range of the first capture device.
Abstract:
A learning apparatus includes a metric space learning unit and a case example storage unit. The metric space learning unit learns a metric space including feature vectors extracted from sets of attribute-attached image data for each combination of different attributes by using the sets of attribute-attached image data to which pieces of attribute information are added. The case example storage unit calculates feature vectors from sets of case example image data, and stores the feature vectors as case examples associated with the metric space.
Abstract:
Please delete the Abstract of the Disclosure, and replace it with the following: An input image acquisition unit acquires a plurality of input images in which a specific detection target is captured by a plurality of different modalities. A perturbed image acquisition unit acquires a plurality of perturbed images in which at least one of the plurality of input images is perturbed. A detection processing unit detects a detection target included in the input images using each of the plurality of perturbed images and one of the plurality of input images that has not been perturbed, and acquires, for each of the plurality of perturbed images, a detection position of the detection target and a detection confidence level as detection results. An adjustment unit calculates, based on the detection positions and the confidence levels acquired for the plurality of perturbed images, an adjusted confidence level for each of the perturbed images using integrated parameters.