Examples of a system for usage control of a personal data are described. The system may obtain an input image including a first face of a person. Further, the system may compute a usage control matrix based on the input image, at least one usage control function, and a predefined criteria. The pre-defined criteria may be associated with at least one of: a data usage policy, a face matching probability related to matching of the face present in the input image, and a face recognition probability related to a recognition of an identity of the person. Furthermore, by using the input image and the usage control matrix, the system may transform the input image to a usage-controlled image. Furthermore, the system may verify a matching of the face present in the input image with a second face present in the usage-controlled image. Furthermore, the system may recognize an identity of the person in the input image and provide a feedback indicative of a failure to verify the identity of the person from the usage-controlled image.

An image pickup apparatus includes an image sensor configured to capture a subject image, a display unit configured to display image data generated with an output of the image sensor, a line-of-sight detector configured to detect a line of sight of a user viewing the display unit, a subject detector configured to detect a state of a subject from the image data, and a controller configured to control imaging according to the state of the subject at a gaze position corresponding to the line of sight in an imaging area of the image sensor.

Systems and methods are disclosed. A digitized human vocal expression of a user and digital images are received over a network from a remote device. The digitized human vocal expression is processed to determine characteristics of the human vocal expression, including: pitch, volume, rapidity, a magnitude spectrum identify, and/or pauses in speech. Digital images are received and processed to detect characteristics of the user face, including detecting if any of the following is present: a sagging lip, a crooked smile, uneven eyebrows, and/or facial droop. Using the human vocal expression characteristics and face characteristics, a determination is made as to what action is to be taken. A cepstrum pitch may be determined using an inverse Fourier transform of a logarithm of a spectrum of a human vocal expression signal. The volume may be determined using peak heights in a power spectrum of the human vocal expression.

A system for non-invasive estimation of dementia progression. The system includes a computer device and a server. The computer device obtains an image of a subject's head from at least one angle. The server and/or computer device includes a plurality of machine learning models configured to: analyze the image for patterns related to dementia symptoms; and estimate progress of said dementia symptoms of said subject based on the analysis. The server and/or computer device pre-processes the image by performing a plurality of pre-processing steps comprising: importing the image; detecting eyes and shape of the head based on a previously trained machine learning model; rotating the image based on detection of the eyes and shape of the head; normalizing the image to one standard.

This application provides an eye bag detection method and apparatus, and relates to the field of facial recognition technologies. The method includes: obtaining a to-be-detected image, where the to-be-detected image includes an eye bag region of interest ROI; detecting the eye bag ROI by using a preset convolutional neural network model, to obtain an eye bag detection score and eye bag position detection information; and when the eye bag detection score is within a preset score range, annotating the to-be-detected image based on the eye bag detection score and the eye bag position detection information to obtain eye bag annotation information. According to the technical solutions provided in this application, a position and a score of an eye bag can be accurately recognized. In this way, accuracy of recognizing the eye bag is significantly improved.

The attention level of participants is measured and then the resulting value is provided on a display of the participants. The participants are presented in a gallery view layout. The frame of each participant is colored to indicate the attention level. The entire window is tinted in colors representing the attention level. The blurriness of the participant indicates attention level. The saturation the participant indicates attention level. The window sizes vary based on attention level. Color bars are added to provide indications of percentages of attention level over differing time periods. Neural networks are used to find the faces of the participants and then develop facial keypoint values which are used to determine gaze direction, which in turn is used to develop an attention score. The attention score is then used to determine the settings of the layout.

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.

Computer-implemented systems and methods for making a custom-fit goggle are described.

A system and method of processing media for facial manipulation. Input frames are downscaled to a lower resolution and facial detection identifies a target face in each input frame. The location of the target face is determined in the downscaled frames and the locations are projected to the input frames based on the pixel differences between the frames. Facial landmark detection is then performed on a cropped image from the original input frame. The facial landmarks are used to adjust the orientation of the input frames so that the target face in each input frame is in a standard orientation. Facial landmark detection is again performed on the target face in each frame while in the standard orientation to produce more accurate landmarks. Facial manipulation can then be executed based on the landmarks in the aligned input images. The orientation of the images with the manipulated faces are then reverted.

A method for image processing includes: receiving a third two-dimensional image and a depth image corresponding to the third two-dimensional image, wherein the third two-dimensional image and the depth image include a face; establishing a three-dimensional model of the face according to the depth image; rotating the three-dimensional model of the face by a first angle; projecting the three-dimensional model of the face rotated by the first angle to an image coordinate system of the third two-dimensional image; and building a three-dimensional model of a background region of the third two-dimensional image, processing a background region of an image projected to the image coordinate system of the third two-dimensional image to obtain a fourth image.