Systems and methods are provided for intelligent driving monitoring systems, advanced driver assistance systems and autonomous driving systems, and providing alerts to the driver of a vehicle, based on anomalies detected between driver behavior and environment captured by the outward facing camera. Various aspects of the driver, which may include his direction of sight, point of focus, posture, gaze, is determined by image processing of the upper visible body of the driver, by a driver facing camera in the vehicle. Other aspects of environment around the vehicle captured by the multitude of cameras in the vehicle are used to correlate driver behavior and actions with what is happening outside to detect and warn on anomalies, prevent accidents, provide feedback to the driver, and in general provide a safer driver experience.

A mobile computing device comprises an AR display, an image capture device that generates image data of a face of a viewer of the AR display, and a processing device. The processing device receives the image data; processes the image data to identify a position of a dental arch in the image data; determines a treatment outcome for the dental arch; generates a post-treatment image of the dental arch that shows the treatment outcome; generates updated image data comprising a superimposition of the post-treatment image of the dental arch over the received image data depicting the face of the viewer; and outputs the updated image data to the AR display, wherein the post-treatment image of the dental arch is superimposed over the dental arch in the received image data such that the post-treatment image is visible in the AR display rather than a true depiction of the dental arch.

A computer-implemented method of processing target footage of a target human face includes training an encoder-decoder network comprising an encoder network, a first decoder network, and a second decoder network. The training includes training a first path through the encoder-decoder network including the encoder network and the first decoder network to reconstruct the target footage of the target human face, and training a second path through the encoder-decoder network including the encoder network and the second decoder network to process renders of a synthetic face model exhibiting a range of poses and expressions to determine parameter values for the synthetic face model corresponding to the range of poses and expressions. The method includes processing, using a trained network path comprising or trained using the encoder network and comprising the first decoder network, source data representing the synthetic face model exhibiting a source sequence of expressions, to generate output video data.

Methods and systems are provided for providing directional assistance to guide a user to position a camera for centering a person's face within the camera's field of view. A neural network system is trained to determine the position of the user's face relative to the center of the field of view as captured by an input image. The neural network system is trained using training input images that are generated by cropping different regions of initial training images. Each initial image is used to create a plurality of different training input images, and directional assistance labels used to train the network may be assigned to each training input image based on how the image is cropped. Once trained, the neural network system determines a position of the user's face, and automatically provides a non-visual prompt indicating how to center the face within the field of view.

An information processing apparatus (100) includes an acquisition unit (122) that acquires a first image from which person region feature information regarding a region including other than a face of a retrieval target person is extracted, a second image in which a collation result with the person region feature information indicates a match, and a facial region is detected, and result information indicating a collation result between face information stored in a storage unit and face information extracted from the facial region, and a display processing unit (130) that displays at least two of the first image, the second image, and the result information on an identical screen.

Computer implemented methods and devices for determining dimensions or distances of head features are provided. The method includes identifying a plurality of features in an image of a head of a person. A real dimension of at least one target feature of the plurality of features or a real distance between at least one target feature of the plurality features and a camera device used for capturing the image is estimated based on probability distributions for real dimensions of at least one feature of the plurality of features and a pixel dimension of the at least one feature of the plurality of features.

An image sensor, electronic device and method thereof that performs on-sensor single object class detection using an on-sensor single object class detection deep neural network (DNN), such as a face detection DNN. The single object class detection DNN includes a pixel array layer configured to capture an image and transfer image data of the captured image, and a logic and single object class detection deep neural network (DNN) layer that receives the image data directly from the pixel array layer and outputs the image data with the single object class detection data to a communication bus of an electronic device.

The present disclosure provides a virtual image generation method and apparatus, an electronic device and a storage medium, and relates to the field of artificial intelligence technologies such as augmented reality, computer vision and deep learning. A specific implementation scheme involves: acquiring base coefficients corresponding to key points of a target face based on a target face picture; generating a structure of a virtual image of the target face based on a mapping relationship of spatial alignment between a preset virtual model and a standard model, a base of the standard model and the base coefficients corresponding to the key points of the target face; and performing texture filling on the structure of the virtual image based on textures of the target face picture, to obtain the virtual image of the target face.

An control method executed by a computer, the control method includes acquiring a plurality of captured images captured by cameras in an area; presuming a movement state of a first candidate of candidates for persons who have entered the area based on facial images included in the plurality of acquired captured images; determining whether or not a similarity between a past movement state of the first candidate of candidates and the presumed movement state, meets a criterion by reference to a memory that stores past movement states of the candidates for persons who have entered the area; selecting a candidate list that includes the first candidate of candidates among candidate lists of the persons who have entered the area in the past when the similarity meets the criterion; and reading out biometric information included in the selected candidate list from a biometric information database to the memory.

An image processing method includes: determining a first face mask image that does not contain hair from a target image, and obtaining a first face region that does not contain hair from the target image according to the first face mask image; filling a preset grayscale color outside the first face region to generate an image to be sampled; performing down-sampling on the image to be sampled to obtain sampling results, and obtaining remaining sampling results by removing one or more sampling results in which a color is the preset grayscale color from the sampling results; obtaining a target color by calculating a mean color value of the remaining sampling results and performing weighted summation on a preset standard face color and the mean color value; rendering pixels in a face region of the target image according to the target color.