A non-transitory computer-readable medium (CRM) storing computer program code executed by a computer processor that executes a process of acquiring a medical image generated based on a signal detected by a catheter inserted to a lumen organ, estimating a position of an object at least included in the acquired medical image by inputting the medical image to a first learning model for estimating a position of an object included in the medical image, extracting from the medical image an image portion by using the estimated position of the object as a reference, and recognizing the object included in the extracted image portion by inputting the image portion to a second learning model for recognizing an object included in the image portion.

An control method executed by a computer, the control method includes acquiring a plurality of captured images captured by cameras in an area; presuming a movement state of a first candidate of candidates for persons who have entered the area based on facial images included in the plurality of acquired captured images; determining whether or not a similarity between a past movement state of the first candidate of candidates and the presumed movement state, meets a criterion by reference to a memory that stores past movement states of the candidates for persons who have entered the area; selecting a candidate list that includes the first candidate of candidates among candidate lists of the persons who have entered the area in the past when the similarity meets the criterion; and reading out biometric information included in the selected candidate list from a biometric information database to the memory.

A head-mounted wearable device includes a frame mountable on a head of a user; an infrared imaging device arranged to image a face of the user when the frame is mounted on the head of the user; and a computing system configured to perform operations including causing the infrared imaging device to capture an image of the face of the user using infrared light received at the infrared camera and initiating a biometric authentication process based on the image. The head-mounted wearable device may include a visible-light imaging device to image the face of the user with the computing system configured to perform operations including causing the visible-light imaging device to capture a second image of the face of the user using visible light received at the visible-light imaging device, with the biometric authentication process being based in part on the second image.

An authentication method including: determining whether biometric information with a degree of similarity to first biometric information that satisfies a criterion is included in a plurality of pieces of biometric information extracted from a first biometric information group; and controlling, when the biometric information is determined not to be included, whether to execute first determination that determines whether biometric information with the degree of similarity to biometric information newly detected by the first sensor that satisfies the criterion is included in the plurality of pieces of biometric information extracted from the first biometric information group on a basis of a degree of divergence of the degree of similarity from the criterion, or to execute second determination that extracts a plurality of pieces of biometric information from a second biometric information group.

An authentication method executed by a computer, the authentication method including: extracting, when a captured image of a living body is acquired, a biometric image included in a region that corresponds to the living body from the captured image; and performing authentication of the living body on the basis of the extracted biometric image and a position of the biometric image in the captured image.

An authentication method executed by a computer, the authentication method including: extracting, when a captured image of a living body is acquired, a biometric image included in a region that corresponds to the living body from the captured image; and performing authentication of the living body on the basis of the extracted biometric image and a position of the biometric image in the captured image.

The disclosure herein relates to systems, methods, and devices for medical image analysis, diagnosis, risk stratification, decision making and/or disease tracking. In some embodiments, the systems, devices, and methods described herein are configured to analyze non-invasive medical images of a subject to automatically and/or dynamically identify one or more features, such as plaque and vessels, and/or derive one or more quantified plaque parameters, such as radiodensity, radiodensity composition, volume, radiodensity heterogeneity, geometry, location, and/or the like. In some embodiments, the systems, devices, and methods described herein are further configured to generate one or more assessments of plaque-based diseases from raw medical images using one or more of the identified features and/or quantified parameters.

The disclosure herein relates to systems, methods, and devices for medical image analysis, diagnosis, risk stratification, decision making and/or disease tracking. In some embodiments, the systems, devices, and methods described herein are configured to analyze non-invasive medical images of a subject to automatically and/or dynamically identify one or more features, such as plaque and vessels, and/or derive one or more quantified plaque parameters, such as radiodensity, radiodensity composition, volume, radiodensity heterogeneity, geometry, location, and/or the like. In some embodiments, the systems, devices, and methods described herein are further configured to generate one or more assessments of plaque-based diseases from raw medical images using one or more of the identified features and/or quantified parameters.

The disclosure relates to systems and methods for evaluating a blood vessel. The method includes receiving image data of the blood vessel acquired by an image acquisition device, and predicting, by a processor, blood vessel condition parameters of the blood vessel by applying a deep learning model to the acquired image data of the blood vessel. The deep learning model maps a sequence of image patches on the blood vessel to blood vessel condition parameters on the blood vessel, where in the mapping the entire sequence of image patches contribute to the blood vessel condition parameters. The method further includes providing the blood vessel condition parameters of the blood vessel for evaluating the blood vessel.

During a training phase, a first machine learning system is trained using actual data, such as multimodal images of a hand, to generate synthetic image data. During training, the first system determines latent vector spaces associated with identity, appearance, and so forth. During a generation phase, latent vectors from the latent vector spaces are generated and used as input to the first machine learning system to generate candidate synthetic image data. The candidate image data is assessed to determine suitability for inclusion into a set of synthetic image data that may be used for subsequent use in training a second machine learning system to recognize an identity of a hand presented by a user. For example, the candidate synthetic image data is compared to previously generated synthetic image data to avoid duplicative synthetic identities. The second machine learning system is then trained using the approved candidate synthetic image data.