Face image data is acquired and a face image is captured, and a difference between a face image indicated by the face image data and the face image that is captured or a candidate of the difference is detected on the basis of at least one of the face image indicated by the face image data that is acquired, and the face image that is captured. Guidance is acquired on the basis of the difference or the candidate of the difference which is detected, and an output unit is controlled to output the guidance.

An electronic apparatus performs a method of constructing a facial position map from a two-dimensional (2D) facial image of a real-life person that includes: generating a coarse facial position map from the 2D facial image; predicting a first set of keypoints in the 2D facial image based on the coarse facial position map; identifying a second set of keypoints in the 2D facial image based on the user-provided keypoint annotations; and updating the coarse facial position map to get a final facial position map so as to reduce the differences between the first set of keypoints and the second set of key points in the 2D facial image. In some embodiments, a final set of keypoints and/or a three-dimensional (3D) facial model of the real-life person is formed from the final facial position map.

With respect to a subject included in an image, the illuminating condition by an ambient light source in an environment where the image was captured is estimated, and based on the estimation result, the effect of a virtual light source that was non-existent at the time of image capture is computed. More specifically, the effect of the virtual light source is computed using an illumination direction of the virtual light source and the reflective characteristics of the subject illuminated by the virtual light source, which have been determined based on the estimation result, and an image derived from addition of the effect of the virtual light source is output.

A neural network-based image processing method may include receiving, by a trained neural network, a first image including a first object, the first object being partially covered by a second object. The method may also include generating, by the trained neural network, a second image based on the first image. The second image is a representation of the first image with the second object substantially removed, and the first object is a human face.

An occupant monitoring device includes: an acquisition unit that acquires a captured image obtained by imaging a region in which there is a probability that a face of an occupant is present in a vehicle; a determination unit that determines whether the captured image acquired by the acquisition unit corresponds to a first image including the face a part of which is hidden by an accessory or a second image including the face a part of which is hidden by a non-accessory object other than the accessory; and a processing unit that detects face information regarding the face of the occupant based on the captured image in different modes according to a determination result in the determination unit, and monitors a state change of the face of the occupant based on a detection result.

Provided are a mediating apparatus and a mediating method, and a computer-readable recording medium thereof. The mediating method includes: receiving a plurality of images from a first user; generating at least one new image by referring to the plurality of received images; extracting a feature of a face included in the at least one generated new image; searching for a second user corresponding to the feature that has been extracted; and providing the first user with information about the second user.

A videoconferencing device at an endpoint determines whether a person is a real person standing in front of a display device or if the person is instead an image rendered by a display device. In the first instance the real person will be included in a video feed for transmission to a remote endpoint. In the second instance, images of the display device on which the person is rendered will not be included in the video feed.

Systems and methods for detecting illicit activity based on body language features identified during a video visitation session or video communication are described herein. In some embodiments, a system may include a processor and a memory coupled to the processor, the memory having program instructions stored thereon that, upon execution by the processor, cause the system to: analyze a video communication between a first party and a second party, where the analysis is configured to detect a body language feature that indicates an event taking place during the communication; and create an electronic record identifying the communication as containing the event.

A subject feature detection unit (53) (detection unit) of a mobile terminal (80) (information processing device) detects a line-of-sight direction (E) (feature) of a subject (92) displayed simultaneously with a 3D model (90M) in a captured image (I). Then, the display control unit (54) (control unit) changes a line-of-sight direction (F) (display mode) of the 3D model (90M) so that the 3D model (90M) faces the camera (84) in accordance with the line-of-sight direction (E) of the subject (92) detected by the subject feature detection unit (53).

A method for providing real-time three-dimensional facial animation from video is provided. The method includes collecting images of a subject, and forming a three-dimensional mesh for the subject based on a facial expression factor and a head pose of the subject extracted from the images of the subject. The method also includes forming a texture transformation based on an illumination parameter associated with an illumination configuration for the images from the subject, forming a three-dimensional model for the subject based on the three-dimensional mesh and the texture transformation, determining a loss factor based on selected points in a test image from the subject and a rendition of the test image by the three-dimensional model, and updating the three-dimensional model according to the loss factor. A system and a non-transitory, computer-readable medium storing instructions to perform the above method are also provided.