Example implementations are directed to methods and systems for individualized multimedia navigation and control including receiving metadata for a piece of digital content, where the metadata comprises a primary image and text that is used to describes the digital content; analyzing the primary image to detect one or more objects; selecting one or more secondary images corresponding to each detected object; and generating a data structure for the digital content comprising the one or more secondary images, where the digital content is described by a preferred secondary image.

A system and method is disclosed for improving fraud detection in the context of a user submitting, via a client device, a photo of a photo ID and a selfie taken during a step of the verification process. A machine learning model may be trained to generate biometric data signals from the selfie. As examples, the biometric data signals generated from the selfie can include an estimated age, gender, hair color, and eye color. The biometric data generated from the selfie may be compared with biometric data listed on the photo ID as an aid to identifying potential identity fraud. In some implementations, a facial filter corresponding to a map of a set of facial measurements of the photo in the photo ID may be compared with the facial measurements of the selfie.

A system, device, and method for collecting user information. An image and metadata associated with the image are captured utilizing a wireless device. The image and the metadata are associated with a device incapable of communication. User information associated with the device is determined. The user information is characterized in context of historical data. The user information is distributed from the wireless device to one or more devices associated with the user information.

Methods and systems for implementing an object identification system equipped to reduce the number of service calls for remote object identification lookups are disclosed. The object identification management server works to reduce the number of object identification requests, which overall improves the process of quickly and accurately providing object identifications when needed and without unnecessarily wasting computing resources. The object identification server can subsequently manage the number of identification requests by setting a tunable threshold, which allows a system to operate more efficiently and allocate computing resources accordingly.

Tools, strategies, and techniques are provided for evaluating the identities of different entities to protect business enterprises, consumers, and other entities from fraud by combining biometric activity data with facial recognition data for end users. Risks associated with various entities can be analyzed and assessed based on a combination of user liveliness check data, facial image data, social network data, and/or professional network data, among other data sources. In various embodiments, the risk assessment may include calculating an authorization score or authenticity score based on different portions or combinations of the collected and processed data.

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.

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.

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.

An apparatus for face image candidate determination for authentication, including a storage and at least one processor wherein the storage stores a reference face image in association with an attribute of the image for each registered person and a score threshold value for each combination of attributes of face images to be collated. The processor configured to: acquire a face image of a person to be determined as a face image for determination, determine an attribute of the face image, collate the reference face image with the face image and calculates a score indicating a degree of matching, and select a reference face image satisfying the score threshold value as a face image candidate for authentication and selects the face image candidate and the score thereof as authentication information of the face image for determination.

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.