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.

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.

A method for image-processing is disclosed. The method includes obtaining first image-information of an image that is to be clustered in response to a first preset condition being met; clustering the image according to the first image-information and obtaining a first clustering-result; sending an image-clustering request to a server in response to a second preset condition being met, wherein the image-clustering request is configured to indicate the server to cluster the image which has been uploaded to the server and obtain a second clustering-result; and receiving the second clustering-result returned from the first server and updating at least one of the first clustering-result and the second clustering-result according to a preset rule.

In an exemplary embodiment, user input is received, a selected portion of a digital image is identified based on the user input, a data instance is selected, and a tag is applied to the selected portion of the digital image. The applied tag provides an association between the selected portion of the digital image and the data instance. In certain examples, a visual indicator representative of the tag is provided for display together with the tagged digital image.

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.

Machine learning is performed using synthetic data for neural network training using vectors. Facial images are obtained for a neural network training dataset. Facial elements from the facial images are encoded into vector representations of the facial elements. A generative adversarial network (GAN) generator is trained to provide one or more synthetic vectors based on the one or more vector representations, wherein the one or more synthetic vectors enable avoidance of discriminator detection in the GAN. The training a GAN further comprises determining a generator accuracy using the discriminator. The generator accuracy can enable a classifier, where the classifier comprises a multi-layer perceptron. Additional synthetic vectors are generated in the GAN, wherein the additional synthetic vectors avoid discriminator detection. A machine learning neural network is trained using the additional synthetic vectors. The training a machine learning neural network further includes using the one or more synthetic vectors.

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.

Provided are an image processing device, an image processing method, a program, and a recording medium which are capable of classifying a plurality of persons appearing in an image set into groups. In an image processing device, an image processing method, a program, and a recording medium according to an embodiment of the present invention, a plurality of persons appearing in an image set is determined. In a case where two or more persons among the plurality of persons appear in the image based on a determination result of the plurality of persons for each image included in the image set, co-occurrence relation information indicating that the two or more persons have a co-occurrence relation in the image is stored. A co-occurrence score indicating strength of the co-occurrence relation of two persons in the image set is calculated based on all the co-occurrence relation information items in the image for each permutation of the two persons of the plurality of persons. A part of the plurality of persons is classified into a group based on all the co-occurrence scores of the permutations of the two persons in the image set.

Devices, systems, and methods are provided for enhanced video annotations using image analysis. A method may include identifying, by a first device, first faces of first video frames, and second faces of second video frames. The method may include determining a first score for the first video frames, the first score indicative of a first number of faces to label, the first number of faces represented by the first video frames, and determining a second score for the second video frames, the second score indicative of a second number of faces to label. The method may include selecting the first video frames for face labeling, and receiving a first face label for the first face. The method may include generating a second face label for the second faces. The method may include sending the first face label and the second face label to a second device for presentation.

Some embodiments process facial images to ensure the inmate present for release is the actual inmate to be released. One embodiment includes receiving a facial image and a data identifier of a person. This embodiment further includes processing the facial image of the person in view of facial image data of the person to determine whether the received facial image matches the retrieved facial image data. The facial image data may be retrieved based on the data identifier of the person, facial landmark data extracted from the facial image, or otherwise. When the received facial image and the retrieved facial image data of the person match, an indicator of a match and a successful release identity verification is output. When there is not a match, an indicator of a failed release identity verification is output.