Fujitsu Corporation

Fujitsu Frontech Limited

Toshio Endoh

Masaki Watanabe

Takahiro Aoki

Mitsuaki Fukuda

Takashi Shinzaki

Atsushi Miki

Kiyoshi Chinzei

Satoshi Fukui

Shuji Kimura

Takayuki Sugiura

Naoyuki Fujimoto

Makoto Goto

Hironori Yokoi

Mieko Nakano

Mitsuhiro Gotoh

Shigeru Sasaki

Takashi Katoh

Soichi Hama

Shinichi Eguchi

Hajime Nada

Kento Uemura

Takumi Kishino

Suguru Yasutomi

Yutaka Katsumata

Shigefumi Yamada

Kazuhiro Matsumoto

Yasuyuki Higashiura

Hideo Kamata

Takashi Toriu

Keizo Kato

Kanata Suzuki

Yuka Sugimura

An adaptability calculation device inputs input data to a learning model and an encoder of an autoencoder that have performed learning with learning data, inputs an output from the learning model and an output from the encoder of the autoencoder to a decoder of the autoencoder, and calculates adaptability of the output from the learning model to the input data based on an output from the decoder and the input data.

Adaptability calculation method, adaptability calculation device, identification method, identification device, and computer-readable recording medium

A non-transitory computer-readable storage medium storing a program that causes a computer to execute a process, the process comprising: calculating parameters of an expression that describes a model that is associated with a traffic system and estimates a traffic volume between a plurality of points, by using a plurality of measurement data measured in the traffic system that connects the plurality of points to each other, by using a method for solving an optimization problem; generating, when an indefinite solution is obtained by the calculating, indefiniteness information related to a range of the indefinite solution in the method for solving the optimization problem; and determining addition of the plurality of measurement data or output of the indefinite solution, depending on the indefiniteness information.

Storage medium, estimation device, and estimation method

A risk evaluation method is disclosed. A machine learning is conducted by using a neural network by inputting training data. A data distance corresponding to a permission level is calculated based on restoration data and the training data, the permission level being among a plurality of layers of the neural network, the restoration data being generated by using at least one weight of a plurality of permission level weights at the permission level, the plurality of permission level weights being among a plurality of weights of synapses at the plurality of layers, the plurality of weights being generated by the machine learning.

Risk evaluation method, computer-readable recording medium, and information processing apparatus

A non-transitory computer-readable storage medium storing a program that causes a computer to execute a process, the process including obtaining a feature quantity of input data by using a feature generator, generating a first output based on the feature quantity by using a supervised learner for labeled data, generating a second output based on the feature quantity by using an unsupervised learning processing for unlabeled data, and changing a contribution ratio between a first error and a second error in a learning by the feature generator, the first error being generated from the labeled data and the first output, the second error being generated from the unlabeled data and the second output.

Non-transitory computer-readable storage medium, learning method, and learning device

A computer-implemented detection method includes, in response to inputting a first image including a region of one or more objects to a learned model, identifying a first entire image corresponding to entirety of a first object as a detection candidate, the learned model being generated by learning training data including an image corresponding to a part of an object and an entire image corresponding to entirety of the object, detecting an existing region of the first target object in the first image in accordance with a comparison between the identified first entire image and the region of the one or more target objects, and determining, based on a specific image obtained by invalidating the existing region in the first image, whether another target object is included in the first image.

Detection method and detection device

A feature model, which calculates a feature value of an input image, is trained on a plurality of first images. First feature values corresponding one-to-one with the first images are calculated using the feature model, and feature distribution information representing a relationship between a plurality of classes and the first feature values is generated. When a detection model which determines, in an input image, each region with an object and a class to which the object belongs is trained on a plurality of second images, second feature values corresponding to regions determined within the second images by the detection model are calculated using the feature model, an evaluation value, which indicates class determination accuracy of the detection model, is modified using the feature distribution information and the second feature values, and the detection model is updated based on the modified evaluation value.

Learning apparatus, detecting apparatus, learning method, and detecting method

A learning apparatus causes a first supervised learning model, which receives feature data generated from input data having data items with which a first label and a second label are associated and outputs a first estimation result, to learn such that the first estimation result is close to the first label. The learning apparatus causes a second supervised learning model, which receives the feature data and outputs a second estimation result, to learn such that the second estimation result is close to the second label. The learning apparatus causes a feature extractor, which generates the feature data from the input data, to learn so as to facilitate recognition of the first label and suppress recognition of the second label.

Non-transitory computer-readable recording medium, learning method, and learning apparatus

An object detection device extracts feature for input data utilizing an encoder, the input data including labeled data and unlabeled data and detects object in each of the input data, utilizing an object detector. The object detection device generates region data for each of the input data, each of the region data corresponding to the detected object and generates restoration data from the region data and meta-information related to the detected object for each of the input data utilizing a decoder corresponding to the encoder. The object detection device executes learning of the encoder and the object detector based on a result detected by the object detector and a label associated with the input data, when the input data is labeled data. The object detection device executes learning of the encoder, the object detector, and the decoder, based on the input data and the restoration data.

Computer-readable recording medium, training method, and object detection device

A biometric information processing apparatus includes a processor; and a memory, wherein the processor is configured to extract auxiliary information representing a part of a body being captured together with biometric information from a plurality of images captured by an imaging unit; to trace the auxiliary information in a time direction; to extract the biometric information from at least one image among the plurality of images; to associate the traced auxiliary information with the extracted biometric information in terms of a positional relationship; and to output the auxiliary information having been associated with the biometric information.

Biometric information processing for providing increased authentication precision by guiding a user to place auxiliary information within an imaging range

A biometric information input apparatus includes an image capturing section configured to obtain a captured image of a biological object; a pliable part detecting section configured to detect whether there is a pliable part on a surface of the biological object by obtaining a distance to the surface of the biological object from the captured image and comparing the obtained distance with a predetermined distance to be compared set beforehand; and an extracting section configured to extract biometric information from the captured image if the pliable part is not detected by the pliable part detecting section.

Biometric information input apparatus and biometric information input method

An authentication device includes: a memory; and a processor coupled to the memory and configured to: acquire a plurality of images with regard to a certain living body, extract first biometric information from each of the plurality of images, perform an alignment between the plurality of images, based on the first biometric information, calculate a coefficient based on feature of the certain living body, the feature being identified by a result of the alignment, and generate a normalized image by normalizing at least one image from among the plurality of images based on the coefficient.

Authentication device and method

A biometric authentication device includes: a memory; and a processor coupled to the memory and configured to: determine, when authentication with first biometric information has been successful, whether second biometric information different from the first biometric information is from a user corresponding to the first biometric information using a determining method corresponding to one of a plurality of security levels, the determining method being selected from among a plurality of determining methods based on the one of the plurality of security levels, and register, when the second biometric information has been determined to be from the user, the second biometric information associated with the user.

Biometric authentication device and method

An obtaining unit of a biological information obtaining apparatus obtains biological information for authentication, and an extracting unit extracts first feature information from biological information for authentication. A generating unit of a biological information collating apparatus generates encrypted position correction information, and encrypted position correction information is transmitted to the biological information obtaining apparatus. A correcting unit decrypts encrypted position correction information to obtain position correction information, and by correcting first feature information, performs alignment between first feature information and second feature information extracted from biological information for registration. A transforming unit transforms corrected first feature information, and transmits transformed first feature information to the biological information collating apparatus. A collating unit collates transformed first feature information and transformed second feature information stored in a storing unit, and transmits a collation result to biological information obtaining apparatus.

Biological information obtaining apparatus and biological information collating apparatus

A biometric authentication device includes: a storage unit configured to store a three-dimensional shape of a posture of a body of a user; a three-dimensional shape calculation unit configured to calculate a three-dimensional shape of a body from biometric information of the user detected by a biometric sensor; a posture calculation unit configured to calculate a posture of the body from the biometric information detected by the biometric sensor; a synthesis unit configured to synthesize a three-dimensional shape from the three-dimensional shape stored in the storage unit in accordance with the posture calculated by the posture calculation unit; and a comparison unit configured to compare the three-dimensional shape calculated by the three-dimensional shape calculation unit with the three-dimensional shape synthesized by the synthesis unit.

Biometric authentication device, biometric authentication method, and non-transitory computer-readable recording medium

A biometric authentication device determining whether or not a user is to be authenticated, by matching biological information of a finger of the user and biological information of a palm thereof against biological information of a finger of a registered user and biological information of a palm thereof, the biometric authentication device includes, a first sensor configured to generate a first biological image in which the biological information of the finger of the user is captured; a second sensor configured to generate a second biological image in which the biological information of the palm of the user is captured; a guiding member configured to regulate a position of the finger of the user; an object area detection unit configured to detect a first object area in which the biological information of the finger is captured on the first biological image; and a position determination unit configured to determine.

Growing community of inventors

Kawasaki, Japan

Toshio Endoh