Disclosed herein are a method and apparatus for active identification based on gaze path analysis. The method may include extracting the face image of a user, extracting the gaze path of the user based on the face image, verifying the identity of the user based on the gaze path, and determining whether the face image is authentic.

A method of automatic positioning a seat in an apparatus comprising two cameras located on either side of the seat, each one in a position able to acquire images of a face of a user seated on the seat. The seat comprises at least one motor, each motor acting on a position of the seat along a predefined axis. The method comprises: for each camera: obtaining a position of a predefined image zone in which at least one eye of a user of the apparatus should be located; acquiring an image of a user seated on the seat; detecting at least one eye of the seated user in the image acquired; and obtaining a relative position between each eye detected and the predefined zone. By using each relative position obtained, at least one motor is actuated until each predefined zone contains at least one eye of the seated user.

A shape generation system can generate a three-dimensional (3D) model of an object from a two-dimensional (2D) image of the object by projecting vectors onto light cones created from the 2D image. The projected vectors can be used to more accurately create the 3D model of the object based on image element (e.g., pixel) values of the image.

The present application discloses a face super-resolution realization method and apparatus, an electronic device and a storage medium, and relate to fields of face image processing and deep learning. The specific implementation solution is as follows: a face part in a first image is extracted; the face part is input into a pre-trained face super-resolution model to obtain a super-sharp face image; a semantic segmentation image corresponding to the super-sharp face image is acquired; and the face part in the first image is replaced with the super-sharp face image, by utilizing the semantic segmentation image, to obtain a face super-resolution image.

Some implementations of the disclosure involve, at a device having one or more processors, one or more image sensors, and an illumination source, detecting a first attribute of an eye based on pixel differences associated with different wavelengths of light in a first image of the eye. These implementations next determine a first location associated with the first attribute in a three dimensional (3D) coordinate system based on depth information from a depth sensor. Various implementations detect a second attribute of the eye based on a glint resulting from light of the illumination source reflecting off a cornea of the eye. These implementations next determine a second location associated with the second attribute in the 3D coordinate system based on the depth information from the depth sensor, and determine a gaze direction in the 3D coordinate system based on the first location and the second location.

A method and a system for intelligently controlling children's usage of a screen terminal are provided in this disclosure, which relate to the technical field of intelligent home terminals. When children use the screen terminal, children's age can be automatically and intelligently identified, real-time intelligent supervision on children's sitting posture, distance and other aspects can be conducted according to different children's ages, so as to intelligently control on and off duration of the screen terminal, so as to guide the children to use the screen terminal device healthily. Compared with prior art schemes, in the disclosure, management of the screen terminal device can be realized without manual operation, reducing trouble from manual equipment management, further realizing specific control of children's usage of the screen terminal by age, increasing intelligence degree, and with advantage of being used in multiple scenes.

Methods and systems are described for maintaining hygienic conditions in automatic teller machines by detecting whether or not a user is not in compliance with a hygienic standard. If a user is not in compliance, then the automatic teller machine may execute a hygienic action to cleanse the automatic teller machine. For example, the hygienic action may comprise automatically cleansing the automatic teller machine, disabling the automatic teller machine from service, transmitting a sanitation service request to an automatic teller machine provide, and/or initiating an alternative control scheme (e.g., voice controls, gesture-based controls, etc.) for the automatic teller machine.

The present disclosure provides an object tracking method and an object tracking device. The method includes: acquiring a human-face region of an image frame so as to determine a human-body region; extracting a human-body feature from the human-body region, and determining whether a plurality of historical object trajectories match the human-body feature; in response to that one of the plurality of historical object trajectories matches the human-body feature, updating an age of the human-body feature to a preset value; and in response to that none of the plurality of historical object trajectories matches the human-body feature, adding an object trajectory corresponding to the human-body feature to the plurality of historical object trajectories. Thus, a better tracking effect may be achieved.

Disclosed in some examples are devices, methods, systems, and machine-readable mediums for enhanced meeting room solutions to provide increased inclusiveness for both remote and in-room participants for network-based communication sessions, such as hybrid network-based communication sessions. Content of a first type is placed in a location exclusive of a discontinuity in a display device and content of a second type is placed in a location inclusive of the discontinuity of the display device.

An object detection apparatus according to the present invention includes an object detecting unit configured to perform, for each region in an image set based on a size of a specific object detected in the image, a process of detecting the specific object on a new image.