Systems and methods are disclosed for generating, a source image sequence using an image sensor of the computing device, the source image sequence comprising a plurality of source images depicting a head and face, identifying driving image sequence data to modify face image feature data in the source image sequence, generating, using an image transformation neural network, a modified source image sequence comprising a plurality of modified source images depicting modified versions of the head and face, and storing the modified source image sequence on the computing device.

Systems, devices, methods, and computer readable media are provided in various embodiments having regard to authentication using secure tokens, in accordance with various embodiments. An individual's personal information is encapsulated into transformed digitally signed tokens, which can then be stored in a secure data storage (e.g., a “personal information bank”). The digitally signed tokens can include blended characteristics of the individual (e.g., 2D/3D facial representation, speech patterns) that are combined with digital signatures obtained from cryptographic keys (e.g., private keys) associated with corroborating trusted entities (e.g., a government, a bank) or organizations of which the individual purports to be a member of (e.g., a dog-walking service).

In examples, locations of facial landmarks may be applied to one or more machine learning models (MLMs) to generate output data indicating profiles corresponding to facial expressions, such as facial action coding system (FACS) values. The output data may be used to determine geometry of a model. For example, video frames depicting one or more faces may be analyzed to determine the locations. The facial landmarks may be normalized, then be applied to the MLM(s) to infer the profile(s), which may then be used to animate the mode for expression retargeting from the video. The MLM(s) may include sub-networks that each analyze a set of input data corresponding to a region of the face to determine profiles that correspond to the region. The profiles from the sub-networks, along global locations of facial landmarks may be used by a subsequent network to infer the profiles for the overall face.

Provided in the present disclosure are a method and system for facial recognition. The method for facial recognition comprises: acquiring an image of an unshielded area of a face; and utilizing the image of the unshielded area of the face for facial recognition.

A device for temperature measurement, includes a blackbody configured to radiate a specified temperature, an infrared thermal imaging camera configured to measure a temperature of a target surface of the blackbody and a temperature of an object and at least one processor configured to calibrate the temperature of the object based on the specified temperature and the temperature of the target surface.

An imaging system comprising: a pair of cameras (2, 3) adapted to capture images in the near infrared region of the electromagnetic spectrum; a camera (4) adapted to capture images in the visible region of the electromagnetic spectrum; a near-infrared light source (5); and a controller (6). The system configured to obtain a plurality images of the face of an individual and, from a local binary pattern distribution of image pixels, determine the age of the individual in each of the images by applying a linear regression technique and computing an average.

In one embodiment, one or more computing systems may access a plurality of images corresponding to a portion of a face of a user. The plurality of images is captured from different viewpoints by a plurality of cameras coupled to an artificial-reality system worn by the user. The one or more computing systems may synthesize, using a machine-learning model, to generate a synthesized image corresponding to the portion of the face of the user. The one or more computing systems may access a three-dimensional (3D) facial model representative of the face of the user and generate a texture image by projecting the synthesized image onto the 3D facial model from a specific camera pose. The one or more computing systems may cause an output image of a facial representation of the user to be rendered using at least the 3D facial model and the texture image.

A non-transitory computer-readable medium, an information processing device, and an information processing method that causes a computer processor to execute a process that includes: outputting a first question by voice; receiving an answer to the first question from a subject; outputting a second question by text; receiving an answer to the second question from the subject; determining whether or not the answers to the first and second questions are correct; and estimating a possibility of a brain dysfunction of the subject based on correctness or incorrectness of the answers to the first and second questions.

The present invention provides a facial image display apparatus that can display moving images concentrated on the face when images of people's faces are displayed. A facial image display apparatus is provided wherein a facial area detecting unit (21) detects facial areas in which faces are displayed from within a target image for displaying a plurality of faces; a dynamic extraction area creating unit (22) creates, based on the facial areas detected by the facial area detecting means, a dynamic extraction area of which at least one of position and surface area varies over time in the target image; and a moving image output unit (27) sequentially extracts images in the dynamic extraction area and outputs the extracted images as a moving image.

Methods, apparatus, systems, and articles of manufacture to measure facial attention are disclosed. An example apparatus includes at least one memory, machine readable instructions, and processor circuitry to at least one of instantiate or execute the machine readable instructions to identify facial landmarks from input image data, the facial landmarks corresponding to coordinates of landmarks of a face detected in the input image data, determine a first distance between a first facial landmark of the facial landmarks and a second facial landmark of the facial landmarks, determine a second distance between the first facial landmark and a third facial landmark of the facial landmarks, and compare a quotient of the first distance and the second distance to a threshold to determine an attentiveness metric.