A server includes processing circuitry configured to receive one or more images, the one or more images including one or more representations of people. Additionally, the processing circuitry is configured to apply a neural network to the one or more images, wherein the neural network classifies at least one aesthetic component of each image of the one or more images, an aesthetic component score being generated for each image in the one or more images. Further, the processing circuitry is configured to generate a user eyewear equipment profile for a user, the user being matched to a persona from a personae database, each persona in the personae database being linked to one or more persona eyewear equipment profiles, the one or more persona eyewear equipment profiles being based on the aesthetic component score, and select eyewear equipment for the user based on the generated user eyewear equipment profile.

A digital facial image from a user is collected. Digital facial images from a plurality of people are collected from a database. A neural network is used to compare a plurality of pixels of the digital facial image of the user to a plurality of pixels of the digital facial images from a plurality of people to determine a facial shape type of the user. A neural network is used to compare the plurality of pixels from the digital facial image of the user to the digital facial images from a plurality of people to determine an ability of the user to grow hair in an area of the user's face. A facial hair style is recommended to the user based on the facial shape type of the user and the ability of the user to grow hair in an area of the user's face.

An apparatus includes a memory storing one or more instructions; and a processor configured to execute the one or more instructions to: obtain a first image including one or more faces captured at a first time by one or more cameras, each of the one or more faces corresponding to a person, among one or more persons; detect a first person, among the one or more persons, by performing facial recognition operation on the one or more faces in the image obtained from the camera; detect a body region corresponding to a face region of the first person; track the body region in a second image captured by the one or more cameras at a second time subsequent to the first time; and output information to control a bather to open based on a determination that the body region is approaching the barrier.

Handling personally identifiable information (PII) in data streams is provided. Processed sensor data is received, from a plurality of vehicles including sensors capturing raw sensor data, the raw sensor data including captured PII and non-PII. The processed sensor data includes simulated PII created based on the captured PII and one or more layers of the captured PII corresponding to the simulated PII. A request is received from a client device for a portion of the processed sensor data. Access keys corresponding to the request are identified. A result is constructed according to the access keys using the processed sensor data. The constructed result is sent to the client device responsive to the request.

A method with liveness testing may include: acquiring an infrared (IR) image including an object, and a depth image including the object; generating a first preprocessed IR image by performing first edge enhancement preprocessing on the IR image; generating a preprocessed depth image by performing second edge enhancement preprocessing on the depth image; and determining whether the object is a genuine object based on the first preprocessed IR image and the preprocessed depth image.

A system of enhancing biometric analysis matching utilizes an image sensor, such as a digital camera, to capture an image of a face of a person. The system may perform image enhancement, such as edge and contrast enhancement, prior to performing face matching. The enhancement may be localized to a given image region based on determined region illumination. The system may perform image processing and analysis comprising face detection, alignment, feature extraction, and recognition. A biometric recognition confidence indicator may be generated using the results of the image enhancement and analysis. At least partly in response to the biometric recognition confidence indicator falling below a threshold enhancing recognition confidence using an image of visual indicia captured using the image sensor.

The present disclosure relates to methods and devices for wireless communication of an apparatus, e.g., a UE. In one aspect, the apparatus may determine whether a connection of a video call is interrupted, the video call including a plurality of decoded frames. The apparatus may also determine, if the connection of the video call is interrupted, whether one or more decoded frames of the plurality of decoded frames are suitable for artificial frame generation. The apparatus may also generate one or more artificial frames based on the one or more decoded frames and an audio feed from a transmitting device. Additionally, the apparatus may determine whether the one or more artificial frames are suitable for a facial model call. The apparatus may also establish a facial model call based on a combination of the one or more artificial frames and the audio feed from the transmitting device.

A method and apparatus with landmark coordinate prediction are provided. The method includes generating a multi-stage feature map for landmarks of a face image through a staged convolutional network, generating an initial query matrix by fully connecting a last-stage feature map in the multi-stage feature map using a fully connected network, where a total number of feature elements in the initial query matrix is equal to a total number of predicted landmarks of the face image, generating a memory feature matrix by flattening and connecting the multi-stage feature map, generating the predicted landmark coordinates by inputting the memory feature matrix and the initial query matrix to a decoder network of plural cascaded decoder networks.

Systems, methods, and computer-readable storage media for digital fingerprinting of media content. The system does this by normalizing and pre-processing the media content into a standard format, then generating fingerprints for image, audio, and/or video aspects of the media. These fingerprints are mathematical vectors generated using one or more techniques, such as perceptual hashes coupled with machine learning. Once the fingerprints for new content is generated, the system can compare those fingerprints to fingerprints of known content to determine if the new content contains prohibited content.

A method for generating an image includes: obtaining a reference image and an image to be processed; extracting target fusion features from the reference image; determining a plurality of depth feature maps corresponding to the reference image based on the target fusion features; obtaining a target feature map by fusing the plurality of depth feature maps based on the target fusion features; and generating a target image by processing the image to be processed based on the target feature map.