The disclosure provides a system that may receive an identification of a first patient; may receive a first template that includes first multiple locations associated with a face of the first patient and associated with the identification of the first patient; may determine second multiple locations associated with a face of a current patient; may determine a second template of the face of the current patient based at least on the second multiple locations associated with the face of the current patient; may determine if the first template matches the second template; if the first template matches the second template, may provide an indication that the current patient has been correctly identified as the first patient; and if the first template does not match the second template, may provide an indication that the current patient has not been identified.

Systems, devices, media and methods are presented for generating biometric image data. In one example, a system accesses a set of images stored on a mobile computing device. The system identifies one or more faces depicted in the set of images and generates a set of face images from the set of images. The system determines a set of positions of a set of facial features depicted within the set of face images and generates a set of biometric reference maps based on the set of positions. The system transmits the set of face images to a reference server and stores the set of biometric reference maps on the mobile computing device.

Systems and methods for demographic determination using image recognition. The method includes analyzing an image with a pre-trained lightweight neural network model, where the lightweight neural network model generates a confidence value, and comparing the confidence value to a threshold value to determine if the pre-trained lightweight neural network model is sufficiently accurate. The method further includes analyzing the image with a pre-trained heavyweight neural network model for the confidence value below the threshold value, wherein the pre-trained heavyweight neural network model has above about one million trainable parameters and the pre-trained lightweight neural network model has a number of trainable parameters below one tenth the heavyweight model, and displaying demographic data to a user on a user interface, wherein the user modifies store inventory based on the demographic data.

Described are methods, systems, and computer-program product embodiments for selecting a face image based on a name. In some embodiments, a method includes receiving the name. Based on the name, a name vector is selected from a plurality of name vectors in a dataset that maps a plurality of names to a plurality of corresponding name vectors in a vector space, where each name vector includes representations associated with a plurality of words associated with each name. A plurality of face vectors corresponding to a plurality of face images is received. A face vector is selected from the plurality of face vectors based on a plurality of similarity scores calculated for the plurality of corresponding face vectors, where for each name vector, a similarity score is calculated based on the name vector and each face vector. The face image is output based on the selected face vector.

This invention can readily, reliably perform identity verification and input of user information when opening an account from a terminal. An information processing apparatus includes a first feature extractor extracting a first feature from a face image of a user included in a moving image, a second feature extractor extracting a second feature from a face image of an identity verification document for authenticating the user included in the moving image, a collator collating whether the first feature and the second feature match each other, a determiner instructing the user to perform a predetermined action, and determining whether an action of the user included in the moving image corresponds to the instructed predetermined action, and a verifier verifying that the user is the person himself or herself, when the first feature and the second feature match each other and the action of the user corresponds to the instructed predetermined action.

A display device (1) includes a screen (3), at least one sensor, and a processing unit (6) configured to manage content displayed on the screen (3) based on information received from the at least one sensor. The device (1) is configured to detect the presence of one or more user in the vicinity of the device (1) and display content, such as advertisements, on the screen based on information received from the sensors. A plurality of display devices may be interconnected to form a composite display system. The device/system may further use sensors to determine information/advertising content relevant to the demographic of the user(s).

An electronic device and a controlling method thereof are provided. A controlling method of an electronic device according to the disclosure includes: performing first learning for a neural network model for acquiring a video sequence including a talking head of a random user based on a plurality of learning video sequences including talking heads of a plurality of users, performing second learning for fine-tuning the neural network model based on at least one image including a talking head of a first user different from the plurality of users and first landmark information included in the at least one image, and acquiring a first video sequence including the talking head of the first user based on the at least one image and pre-stored second landmark information using the neural network model for which the first learning and the second learning were performed.

An information processing apparatus (100) includes a collation unit (102) that collates first feature information extracted from a person included in a first image (10) with first feature information indicating a feature of a retrieval target person, an extraction unit (104) that extracts second feature information from the person included in the first image in a case where a collation result in the collation unit (102) indicates a match, and a registration unit (106) that stores, in a second feature information storage unit (110), the second feature information extracted from the person included in the first image.

Described herein are systems and methods that may autonomously identify a person of a member pool based on pictures of the person, without requiring the person's cooperation. Contextual information of the picture(s) along with the picture(s) are utilized. Contextual information may be the information that is related to current circumstances when the picture was taken. A system may comprise cameras that map appearances to visual data; a mapping function that maps identities and camera information to generate contextual information and a set of a priori probabilities; recognition functions that map the visual data to another set of probabilities, which match each of the visual data with each of the one of the identities; and a decision function that combines the set of a priori probabilities and the another set of probabilities to determine one of the plurality of the identities.

An information processing device includes: an acquisition unit that acquires feature information of a target depicted in images; a storage unit that stores registration information containing feature information of registered targets; and a distinction unit that distinguishes, on a basis of a result of identification of the feature information acquired by the acquisition unit and the feature information contained in the registration information, one registered target of the registered targets, the one registered target corresponding to the target in the images. The registration information contains zip codes of sites relating to the registered targets. The distinction unit identifies a zip code of a site relating to the target in the images and zip codes contained in registration information with each other, and distinguishes one registered target corresponding to the target in the images using the result of identification of the feature information and using the identification of the zip codes.