Systems and methods are described to dynamically generate metadata for a live media stream. The system determines that a first user on a social media network has started a live media stream. In response, the system identifies a topic of the live media stream based on a frame of the live media stream and identifies another person featured in the frame of the live media stream based on social connections of the first user in the social media network. The system then generates a title for the live media stream based on the identified topic and the identified person, and transmits a notification to a second user that the first user is streaming live, where the notification includes the generated title.

Embodiments described herein include an expressive icon system to present an animated graphical icon, wherein the animated graphical icon is generated by capture facial tracking data at a client device. In some embodiments, the system may track and capture facial tracking data of a user via a camera associated with a client device (e.g., a front facing camera, or a paired camera), and process the facial tracking data to animate a graphical icon.

An interaction method for an electronic device for skin detection includes recognizing a hand action and a face of a user to determine a target hand action; determining a detection target corresponding to the hand action based on the target hand action; determining from an extended content library based on the detection target and a shape of the detection target, extended content associated with the detection target and the shape of the detection target, and outputting the extended content.

Systems and methods for estimating a random distribution for an overall metric for a composite node, the composite node comprising a plurality of nodes. For each data atom of a plurality of data atoms being input to the composite node, and for each node of the plurality of nodes, at least one value may be generated for a per-node metric with respect to the data atom. A value for the overall metric with respect to the data atom may be generated based on the per-node metric values of the plurality of nodes. At least one parameter of the random distribution for the overall metric for the composite node may be estimated based on the overall metric values with respect to the plurality of data atoms.

Facial recognition-based authentication comprises obtaining a first image of a target object, updating projection information associated with a display by a display device, obtaining a second image of the target object, the second image being an image of the target object after the projection information is updated, obtaining an image difference data based at least in part on the first image and the second image, and determining whether the target object is a virtual object based at least in part on the image difference data.

The present disclosure is directed toward intelligently mixing and matching faces and/or people to generate an enhanced image that reduces or minimize artifacts and other defects. For example, the disclosed systems can selectively apply different alignment models to determine a relative alignment between a references image and a target image having an improved instance of the person. Upon aligning the digital images, the disclosed systems can intelligently identify a replacement region based on a boundary that includes the target instance and the reference instance of the person without intersecting other objects or people in the image. Using the size and shape of the replacement region around the target instance and the reference instance, the systems replace the instance of the person in the reference image with the target instance. The alignment of the images and the intelligent selection of the replacement region minimizes inconsistencies and/or artifacts in the final image.

A non-transitory computer-readable medium encoded with a computer-readable program which, when executed by a processor, will cause a computer to execute a method of authenticating a 3-D object with a 2-D camera, the method including building a pre-determined database. The method additionally includes registering the 3-D object to a storage unit of a device comprising the 2-D camera, thereby creating a registered 3-D model of the 3-D object. Additionally, the method includes authenticating a test 3-D object by comparing the test 3-D object to the registered 3-D model.

A system and method are provided for capturing an image with correct skin tone exposure. In use, one or more faces are detected having threshold skin tone within a scene. Next, based on the detected one or more faces, the scene is segmented into one or more face regions and one or more non-face regions. A model of the one or more faces is constructed based on a depth map and a texture map, the depth map including spatial data of the one or more faces, and the texture map includes surface characteristics of the one or more faces. The one or more images of the scene are captured based on the model. Further, in response to the capture, the one or more face regions are processed to generate a final image.

Embodiments of the present disclosure provide a method, an electronic device, and a computer program product for training a model. The method may include determining image features, audio features, and text features of a reference object based on reference image information, reference audio information, and reference text information associated with the reference object, respectively. The method may also include constructing a feature tensor from the image features, the audio features, and the text features. In addition, the method may further include decomposing the feature tensor into a first feature vector, a second feature vector, and a third feature vector corresponding to the image features, the audio features, and the text features, respectively, to determine a loss function value of the model. The method may also include updating parameters of the model based on the loss function value.

An operation of a facial recognition authentication process may fail to authenticate a user even if the user is an authorized user of the device. In such cases, the facial recognition authentication process may automatically re-initiate to provide another attempt to authenticate the user using additional captured images. For the new attempt (e.g., the retry) to authenticate the user, one or more criteria for the images used in the facial recognition authentication process may be adjusted. For example, criteria for distance between the camera and the user's face and/or occlusion of the user's face in the images may be adjusted before the new attempt to authenticate the user. Adjustment of these criteria may increase the likelihood that the authorized user will be successfully authenticated in the new attempt.