A method, apparatus, and non-transitory computer readable medium for image processing are described. Embodiments of the method, apparatus, and non-transitory computer readable medium include identifying an original image including a plurality of semantic attributes, wherein each of the semantic attributes represents a complex set of features of the original image; identifying a target attribute value that indicates a change to a target attribute of the semantic attributes; computing a modified feature vector based on the target attribute value, wherein the modified feature vector incorporates the change to the target attribute while holding at least one preserved attribute of the semantic attributes substantially unchanged; and generating a modified image based on the modified feature vector, wherein the modified image includes the change to the target attribute and retains the at least one preserved attribute from the original image.

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.

Systems and methods for operating a messaging system are provided. An example method includes receiving a first authorization from a user to use a self-image of the user in a personalized video, receiving a second authorization from the user to use a further self-image of a further user in the personalized video, sending, after the first and second authorizations have been received, the personalized video including at least part of the self-image of the user, at least part of the further self-image of the further user, and at least part of a stock video to the further user, and receiving an indication of whether the further user has authorized using the further self-image in the personalized video.

Detection of synthetic content in portrait videos, e.g., deep fakes, is achieved. Detectors blindly utilizing deep learning are not effective in catching fake content, as generative models produce realistic results. However, biological signals hidden in portrait videos which are neither spatially nor temporally preserved in fake content, can be used as implicit descriptors of authenticity. 99.39% accuracy in pairwise separation is achieved. A generalized classifier for fake content is formulated by analyzing signal transformations and corresponding feature sets. Signal maps are generated, and a CNN employed to improve the classifier for detecting synthetic content. Evaluation on several datasets produced superior detection rates against baselines, independent of the source generator, or properties of available fake content. Experiments and evaluations include signals from various facial regions, under image distortions, with varying segment durations, from different generators, against unseen datasets, and under several dimensionality reduction techniques.

An information processor includes: a user information acquiring unit acquiring user information for identifying a user; a display; a viewing time measuring unit measuring a viewing time during which a previously-registered user views contents information on the display; and a controller. The controller determines whether the user is the registered user, based on the user information and registered user information for identifying the registered user. When the user is the registered user, the controller causes the display to display the contents information, causes the viewing time measuring unit to measure the viewing time, compares a cumulative viewing time that is a total of the viewing times cumulated within a period of time with a viewing limit time that is a previously-set upper limit of the cumulative viewing time, and gives the registered user a warning when the cumulative viewing time is equal to or longer than the viewing limit time.

Methods, apparatus, systems are disclosed for altering displayed content on a display device responsive to a user's proximity. In accord with an example, a computing system includes a display, a sensor to output a signal, machine readable instructions, and programmable circuitry to be programmed in accordance with the instructions to intermittingly determine a distance between the compute system and a person based on the signal, and cause a size of at least one object to be presented on the display to be adjusted based on the distance.

Systems, methods, and data storage devices for image grouping in an end user device using trained machine learning group classifiers are described. The end user device may include an image group classifier configured to classify new image data objects using an image classification algorithm and set of machine learning parameters previously trained for a specific image group. The end user device may determine embeddings that quantify features of the target image object and use those embeddings and the image group classifier to selectively associate group identifiers with each new image data object received or generated by the end user device. Calibration, including selection and training, of the image group classifiers and ranking of classified images are also described.

A living body detection method and device are disclosed. Wherein the method comprises the following steps: extracting valid depth data of a target detection object from depth map data containing the target detection object; generating a depth difference histogram based on the valid depth data; and inputting the depth difference histogram into a pre-trained machine learning classifier to obtain a determination result of whether the target detection object is a living body. By adopting this method, the detection accuracy can be improved.

On the basis of voxel data for a plurality of voxels constituting a set of ultrasound volume data, a voxel group identifying unit 50 identifies, in said ultrasound volume data, one or more voxel groups formed by a plurality of voxels in which voxel data satisfy a condition of being linked. On the basis of the voxel data for a plurality of voxels corresponding to each voxel group to be displayed, from among the one or more identified voxel groups, an image forming unit 80 forms an ultrasound image in which the voxel groups to be displayed are indicated clearly in a selective manner. It is thus possible for a three-dimensional image to be formed in such a way that one part of the image, such as floating matter in the amniotic fluid, does not interfere with another part of the image, such as a fetus.

This disclosure enables various computing technologies for predicting various personality traits from various facial and ranial images of persons and then acting accordingly.