Nec Corporation

Akinori Ebihara

Taiki Miyagawa

Kapik Lee

An information processing apparatus includes: an acquisition unit that obtains a plurality of elements included in series data; a calculation unit that calculates a likelihood ratio indicating a likelihood that the series data are derived from the same target as that of predetermined data, on the basis of at least two consecutive elements of the plurality of elements; and a determination unit that determines whether or not the series data are derived from the same target as that of the predetermined data, on the basis of the likelihood ratio. The calculation unit calculates the likelihood ratio in view of a degree of similarity or difference between the series data and the predetermined data. According to the information processing apparatus, it is possible to accurately determine whether or not the series data is derived from the same target as that of the predetermined data.

Information processing apparatus, information processing method, and computer program

In order to suitably perform labeling even in a case where there is an unclear event boundary, a classification apparatus () includes: an acquiring means () for acquiring series data; and a class label assigning means () for assigning a class label to each piece of element data contained in the series data, by repeatedly applying quickest change detection to the series data.

Classification apparatus, classification method, and recording medium

A determination device includes an acquisition unit that sequentially acquires a plurality of elements included in series data related to a classification target, a score calculation unit that calculates an integrated score for binary classification of the classification target each time the acquisition unit acquires the elements, a threshold value determining unit that determines an upper limit threshold value and a lower limit threshold value of the integrated score each time the acquisition unit acquires the elements, and a class determination unit that determines a determination result of the binary classification as one of a first class, a second class, and class indetermination, on the basis of comparison between the integrated score and the upper limit threshold value and the lower limit threshold value each time the acquisition unit acquires the elements.

Determination device, determination method, and recording medium

Provided is an information processing device including: an acquisition unit that sequentially acquires a plurality of elements included in sequential data; a first calculation unit that calculates, for each of the plurality of elements, indicators each indicating which of a plurality of classes is appropriate for corresponding element to belong to, in consideration of two or more elements among the plurality of elements; a second calculation unit that calculates, by integrating the indicators of the plurality of elements, an integrated indicator indicating which of the plurality of classes is appropriate for the sequential data to belong to; and a classification unit that classifies the sequential data into one of the plurality of classes based on the integrated indicator.

Classification of sequential data based on integrated likelhood ratio

Classification of sequential data based on integrated likelihood ratio

Information processing device, personal identification device, information processing method, and storage medium

A determination device includes an acquisition unit that sequentially acquires a plurality of elements included in series data related to a classification target, a score calculation unit that calculates an integrated score for binary classification of the classification target in each time the acquisition unit acquires the elements, a class determination unit that determines a determination result of the binary classification as one of a first class, a second class, and class indetermination, on the basis of comparison between the integrated score and the upper limit threshold value and the lower limit threshold value of the integrated score in each time the acquisition unit acquires the elements, and a remaining time estimation unit that calculates an index value of a remaining time required for the class determination unit to determine a final determination result of the binary classification as one of two classes.

A learning device includes a class classification learning unit that learns class classification of a classification target by using a loss function in which a loss is calculated to become smaller as a magnitude of a difference between a function value obtained by inputting a log-likelihood ratio to a function having a finite value range and a constant associated with a correct answer to the class classification of the classification target becomes smaller, the log-likelihood ratio being the logarithm of a ratio between the likelihood that the classification target belongs to a first class and the likelihood that the classification target belongs to a second class.

Learning device, learning method, and recording medium

A determination device includes a partial image acquisition unit that repeatedly acquires a partial image of a target image until a predetermined end condition is established, a score calculation unit that calculates a score related to the presence or absence of processing of the partial image in each time the partial image acquisition unit acquires the partial image, and a processing determination unit that determines the presence or absence of processing of the target image on the basis of the score.

An information processing system () includes: an acquisition unit () configured to sequentially acquire a plurality of elements included in sequential data; a first calculation unit () configured to calculate, for each of the plurality of elements, a first indicator indicating which one of a plurality of classes the element belongs to; a weight calculation unit () configured to calculate, for each of the plurality of elements, a weight according to a confidence related to calculation of the first indicator; a second calculation unit () configured to calculate, based on the first indicators each weighted with the weight, a second indicator indicating which one of the plurality of classes the sequential data belongs to; and a classification unit () configured to classify the sequential data as any one of the plurality of classes, based on the second indicator. According to such an information processing system, sequential data can be appropriately classified.

Information processing system, information processing method, and computer program

A spoofing detection apparatus comprises obtaining, from an image capture apparatus, a first image frame that includes the face of a subject person obtained when a light-emitting apparatus is emitting light and a second image frame that includes the face of the subject person obtained when the light-emitting apparatus is turned off, extracting, from the first image frame, information specifying a face portion of the subject person, and extract, from the second image frame, information specifying a face portion of the subject person, extracting a portion that includes a bright point formed by reflection in an iris region of an eye of the subject person, from the first image frame, extracts a portion corresponding to the portion that includes the bright point, from the second image frame, and calculates a feature that is independent of the position of the bright point, and determining authenticity of subject person based on the feature.

Spoofing detection apparatus, spoofing detection method, and computer-readable recording medium

A spoofing detection apparatusincludes a face image obtaining unitthat obtains a first image frame that includes the face of a subject person obtained with a light-emitting apparatusturned on and a second image frame that includes the face of the subject person obtained with a light-emitting apparatusturned off, a face information extraction unitthat extracts first face information, and extracts second face information, a feature value calculation unitthat obtains a luminance value of the face portion in the first image frame, obtains a luminance value of the face portion in the second image frame, and calculates, based on these values, a feature value that reflects a three-dimensional shape, and that is independent of colors of a face surface, and a spoofing determination unitthat determines authenticity of subject person based on the feature value.

A three-dimensional facial shape estimating device () includes a face image acquiring unit () configured to acquire a plurality of image frames that capture a subject's face; a face information acquiring unit () having, preset therein, a predetermined number of facial feature points, the face information acquiring unit () being configured to acquire, for each of the plurality of image frames, face information that indicates a position of each of the predetermined number of facial feature points of the subject's face within the image frame; and a three-dimensional shape estimating unit () configured to perform mapping of each of the predetermined number of facial feature points of the subject's face between the plurality of image frames based on the face information of each of the plurality of image frames and to estimate the three-dimensional shape of the subject's face based on a result from the mapping.

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Tokyo, Japan

Akinori Ebihara