Abstract: A non-transitory computer-readable recording medium has stored therein a distribution program that causes a computer to execute a process, the process including, extracting a person and a product from a video of an inside of a store, tracking the extracted person, identifying a behavior that is performed by the tracked person with respect to a product in the store, identifying a first behavior type that is led by the behavior that is performed by the tracked person with respect to the product among a plurality of behavior types that define transition of a process of the behavior since entrance into the store until purchase of a product in the store, and distributing information on a product indicating the first behavior type to the tracked person when the identified first behavior type is at a predetermined level or higher and the tracked person has not yet purchased the product.

Abstract: A non-transitory computer-readable recording medium stores therein an information processing program that causes a computer to execute a process including, extracting a person and a commodity product from a video image in which an inside of a store is captured, tracking the extracted person, specifying a behavior exhibited by the tracked person with respect to the commodity product, specifying a first behavior type that is reached by a first behavior exhibited by the tracked person with respect to the commodity product from among a plurality of behavior types in each of which a transition of processes of the behaviors exhibited between a behavior of entering the inside of the store and a behavior of purchasing the commodity product in the inside of the store is defined, and specifying, based on the first behavior type, content of a customer service provided with respect to the tracked person.

Abstract: A non-transitory computer-readable recording medium stores therein an information processing program that causes a computer to execute a process including, acquiring a video image in which an inside of a store in which each commodity product is arranged is captured, specifying a relationship between a plurality of persons who visit the inside of the store by analyzing the acquired video image in which the inside of the store is captured, grouping the plurality of persons when the specified relationship between the plurality of persons satisfies a predetermined condition, specifying, by analyzing the acquired video image in which the inside of the store is captured, a behavior exhibited with respect to the commodity product by each of the plurality of grouped persons, and associating the behavior exhibited with respect to the commodity product with a group to which the person who exhibits the behavior with respect to the commodity product belongs.

Abstract: An information processing apparatus acquires video data that includes target objects including a person and an object, and identifies a relationship between the target objects in the acquired video data, by inputting the acquired video data to a first machine learning model. The information processing apparatus identifies a behavior of the person in the video data by using a feature value of the person included in the acquired video data. The information processing apparatus predicts one of a future behavior and a future state of the person by comparing the identified behavior of the person and the identified relationship with a behavior prediction rule that is set in advance.

Abstract: An information processing device obtains skeletal information which includes a location of joint in each of users who acted on a product. Then, the information processing device generates a feature quantity that characterizes an action of each of the users on a basis of the location of joint included in the skeletal information of each of the users. Then, the information processing device generates a determination index for determining a user interested in a product, by using the feature quantity of each of the users. After that, the information processing device detects a customer service target from visiting users, by using the determination index.

Abstract: An information processing program causes a computer to execute a process including: specifying, from an image that is captured by a camera, a person and a plurality of objects, generating, by inputting the image of the person into a machine learning model, skeleton information on the person, identifying, based on the plurality of objects and the skeleton information, a first feature value associated with one or more first motions of the person who retrieves an object from among the plurality of objects, identifying a second feature value associated with one or more objects registered to a first terminal by the person from among the plurality of object, and generating, based on a difference between the first feature value and the second feature value, an alert indicates that an object retrieved by the person is not registered in the first terminal.

Abstract: A computer-readable recording medium has stored therein a program that causes a computer to execute a process including: learning, for multiple forecasting models that perform demand forecasting based on sales performance data, multiple error forecasting models that estimate forecasting errors of the respective forecasting models based on product information and a result of forecasts by the respective forecasting models based on sales performance data on a first period; generating weight information on the multiple forecasting models from forecasting errors of multiple forecasted values by the multiple forecasting models, which are forecasting errors generated using the multiple error forecasting models and based on the sales performance data on a second period, which is a period after the first period, and the product information; and performing demand forecasting based on the result of forecasts by the multiple forecasting models that are combined according to the weight information.

Abstract: A secret data matching device includes a memory unit for storing a first secret vector obtained by concealing a first data set and a key data using a determination matrix; an obtaining unit for obtaining a second secret vector by concealing a second data set using the determination matrix; a calculating unit for calculating a residue vector which is a residue when the determination matrix is a modulus, from a difference between the first and second secret vectors; a determination unit for determining whether the first and second data sets are approximate based on the residue vector; an extracting unit for extracting the key data from the residue vector when the first data set and the second data set are approximate; a creating unit for generating a third secret vector by combining the key data and the second secret vector; and a storing unit for storing the third secret vector.

Abstract: A concealed data matching method for a computer including: registering a first concealed vector obtained by concealing registered data and key data based on a first random number and a linear combination of row vectors of a determination matrix; acquiring a second concealed vector; calculating a remainder vector indicating a remainder obtained by dividing the difference between the first concealed vector and the second concealed vector; determining the similarity between the registered data and the matching data based on the remainder vector; extracting the key data from the remainder vector if it is determined they are similar; calculating an inter-vector distance between the registered data and the matching data; and determining the similarity between the registered data and the matching data based on the magnitude of the inter-vector distance.

Abstract: A vein authentication device converts pixel values of an image, which captures an authentication site including veins, into frequency components. Moreover, the vein authentication device performs filtering of the frequency components, which are obtained by conversion of the pixel values, using a filter stored in a filter storing unit for reducing frequency components, from among low-frequency components having a lower spatial frequency than the spatial frequency of the veins and high-frequency components having a higher spatial frequency than the spatial frequency of the veins, corresponding to surface reflection of an illumination used for the authentication site. Furthermore, the vein authentication device converts the frequency components, which are subjected to filtering, back into an image. Moreover, the vein authentication device extracts vein data representing a vascular pattern of veins from the image obtained by reverse conversion.

Abstract: An information terminal includes acquiring an image of a first subject that is captured by a first sensor provided on a back surface of a surface on which a screen of the information terminal is disposed, and when displaying the image on the screen, changing a degree of scaling up/down of the image and a position at which the image is displayed in accordance with a position of a second subject that is imaged by a second sensor provided to the information terminal.

Abstract: A concealed data matching method for a computer including: registering a first concealed vector obtained by concealing registered data and key data based on a first random number and a linear combination of row vectors of a determination matrix; acquiring a second concealed vector; calculating a remainder vector indicating a remainder obtained by dividing the difference between the first concealed vector and the second concealed vector; determining the similarity between the registered data and the matching data based on the remainder vector; extracting the key data from the remainder vector if it is determined they are similar; calculating an inter-vector distance between the registered data and the matching data; and determining the similarity between the registered data and the matching data based on the magnitude of the inter-vector distance.

Abstract: A secret data matching device includes: a memory; and a processor coupled to the memory. The processor executes a process including: storing a first secret vector obtained by concealing first data and key data based on a first random number and a first linear combination of row vectors of a determination matrix which is different for each system including the secret data matching device and which is generated by attaching a random number vector as a last column to a matrix including diagonal components including a threshold value to determine whether the first data and second data are approximate to each other and a threshold value related to the key data; acquiring a second secret vector obtained by concealing the second data based on a second linear combination of row vectors of the determination matrix and a second random number.

Abstract: An information terminal includes acquiring an image of a first subject that is captured by a first sensor provided on a back surface of a surface on which a screen of the information terminal is disposed, and when displaying the image on the screen, changing a degree of scaling up/down of the image and a position at which the image is displayed in accordance with a position of a second subject that is imaged by a second sensor provided to the information terminal.

Abstract: A secret data matching device includes a memory unit for storing a first secret vector obtained by concealing a first data set and a key data using a determination matrix; an obtaining unit for obtaining a second secret vector by concealing a second data set using the determination matrix; a calculating unit for calculating a residue vector which is a residue when the determination matrix is a modulus, from a difference between the first and second secret vectors; a determination unit for determining whether the first and second data sets are approximate based on the residue vector; an extracting unit for extracting the key data from the residue vector when the first data set and the second data set are approximate; a creating unit for generating a third secret vector by combining the key data and the second secret vector; and a storing unit for storing the third secret vector.

Abstract: A secret data matching device includes: a memory; and a processor coupled to the memory. The processor executes a process including: storing a first secret vector obtained by concealing first data and key data based on a first random number and a first linear combination of row vectors of a determination matrix which is different for each system including the secret data matching device and which is generated by attaching a random number vector as a last column to a matrix including diagonal components including a threshold value to determine whether the first data and second data are approximate to each other and a threshold value related to the key data; acquiring a second secret vector obtained by concealing the second data based on a second linear combination of row vectors of the determination matrix and a second random number.

Abstract: A biometric authentication device that authenticates a user using biological features of the user, the biometric authentication device includes: an illumination unit configured to illuminate a target which represents the biological features; an image sensor configured to obtain a first captured image by capturing the target illuminated by the illumination unit, and obtain a second captured image by capturing the target not illuminated by the illumination unit; an acquisition unit configured to acquire from a storage unit a mask which has a target area approximating the shape of the target in the first and second captured images obtained by the image sensor; and a detection unit configured to detect light other than illumination light illuminated by the illumination unit based on the mask acquired by the acquisition unit and at least one of the first and second images.

Abstract: A biometric authentication device includes, an acquisition unit configured to repeatedly image a biological part of an authenticated person while changing a relative position with respect to the biological part so as to acquire time-series biological images; a detection unit configured to detect a pixel corresponding among the biological images from the time-series biological images; an extraction unit configured to extract a pixel of which a pixel value includes a surface reflection component from the biological part, from each of the time-series biological images, on the basis of a degree of divergence of temporal variation of a pixel value of the detected pixel in the time-series biological images from an estimation result of temporal variation; a generation unit configured to correct the pixel value of the extracted pixel on the basis of a pixel value; and an authentication unit configured to perform personal authentication of the authenticated person.

Abstract: A vein authentication device converts pixel values of an image, which captures an authentication site including veins, into frequency components. Moreover, the vein authentication device performs filtering of the frequency components, which are obtained by conversion of the pixel values, using a filter stored in a filter storing unit for reducing frequency components, from among low-frequency components having a lower spatial frequency than the spatial frequency of the veins and high-frequency components having a higher spatial frequency than the spatial frequency of the veins, corresponding to surface reflection of an illumination used for the authentication site. Furthermore, the vein authentication device converts the frequency components, which are subjected to filtering, back into an image. Moreover, the vein authentication device extracts vein data representing a vascular pattern of veins from the image obtained by reverse conversion.

Abstract: A biometric-information processing device includes an image acquisition unit configured to acquire an image of a biometric object using light reflected from the biometric object. The biometric-information processing device further includes an extracting unit configured to extract a frequency component having a frequency higher than a predetermined spatial frequency at the image acquired by the image acquisition unit.