Abstract: An image processing method executed by a computer, the method includes detecting a plurality of feature points of a face from an input image, referring to importance information that indicates an importance of a region within an image in a process of detecting a predetermined facial motion from the image, selecting, from the plurality of feature points detected by the detecting, one or more points that correspond to an image region including an importance indicated by the importance information equal to or smaller than a first threshold value, correcting the input image by using the one or more points selected by the selecting, to generate a corrected image; and determining whether or not the predetermined facial motion is occurring in the input image, based on an output obtained by inputting the corrected image to a recognition model.

Abstract: A travelling control system includes a plurality of vehicles each including a controller, a server including a generator configured to generate a travelling control information for the plurality of vehicles, a predictor configured to predict a merging interference between a first vehicle travelling from a merging road toward a main road and a second vehicle travelling on the main road, a sender which is provided in the first vehicle to send an interference suppressing request, and a receiver which is provided in the second vehicle to receive the interference suppressing request. The controller of the second vehicle travelling on the main road is configured to execute interference suppressing control of suppressing an approach of the second vehicle to the first vehicle travelling from the merging road to the main road, in a case that the receiver receives the interference suppressing request from the first vehicle.

Abstract: A recording medium stores a program for causing a computer to execute processing including: acquiring images; classifying the images, based on a combination of whether an action unit related to a motion of a portion occurs and whether occlusion is included in an image in which the action unit occurs; calculating a feature amount of the image by inputting each classified image into a model; and training the model so as to decrease a first distance between feature amounts of an image in which the action unit occurs and an image with an occlusion with respect to the image in which the action unit occurs and to increase a second distance between feature amounts of the image with the occlusion with respect to the image in which the action unit occurs and an image with an occlusion with respect to an image in which the action unit does not occur.

Abstract: A non-transitory computer-readable recording medium stores a program that causes a computer to execute a process, the process includes inputting each of first images that includes a face of a subject to a first machine learning model to obtain a recognition result that includes information indicating first occurrence probability of each of facial expressions in each first image, generating training data that includes the recognition result and second images that are respectively generated based on the first images and in which at least a part of the face of the subject is concealed, and performing training of a second machine learning model, based on the training data, by using a loss function that represents an error that relates to a second occurrence probability of each facial expression in each second image and relates to magnitude relationship in the second occurrence probability among the second images.

Abstract: An image processing method executed by a computer, the method includes detecting a plurality of feature points of a face from an input image, referring to importance information that indicates an importance of a region within an image in a process of detecting a predetermined facial motion from the image, selecting, from the plurality of feature points detected by the detecting, one or more points that correspond to an image region including an importance indicated by the importance information equal to or smaller than a first threshold value, correcting the input image by using the one or more points selected by the selecting, to generate a corrected image; and determining whether or not the predetermined facial motion is occurring in the input image, based on an output obtained by inputting the corrected image to a recognition model.

Abstract: A recording medium on which a user assistance program is recorded which makes a computer perform: based on image information obtained by photographing a plurality of users who use a given service, calculating state quantities of the plurality of respective users corresponding to the image information; counting, for each time period, a number of users whose amounts of change in the calculated respective state quantities are equal to or more than a given threshold value among the plurality of users; and detecting a time period in which the counted number of users satisfies a given condition.

Abstract: A recording medium on which a user assistance program is recorded which makes a computer perform: based on image information obtained by photographing a plurality of users who use a given service, calculating state quantities of the plurality of respective users corresponding to the image information; counting, for each time period, a number of users whose amounts of change in the calculated respective state quantities are equal to or more than a given threshold value among the plurality of users; and detecting a time period in which the counted number of users satisfies a given condition.