An image gaze correction method, apparatus, electronic device, computer-readable storage medium, and computer program product related to the field of artificial intelligence technologies are provided. The image gaze correction method includes: acquiring an eye image from an image; performing feature extraction processing on the eye image to obtain feature information of the eye image; performing, based on the feature information and a target gaze direction, gaze correction processing on the eye image to obtain an initially corrected eye image and an eye contour mask; performing, by using the eye contour mask, adjustment processing on the initially corrected eye image to obtain a corrected eye image; and generating a gaze corrected image based on the corrected eye image.

Embodiments of the present invention provide a display panel, including a display region, wherein the display region includes an iris recognition region; the display panel further includes a light conversion layer disposed in the iris recognition region, the light conversion layer is configured to convert visible light incident on the light conversion layer into infrared light, and the infrared light is emitted from a display side of the display panel.

Embodiments of the present invention provide a display panel, including a display region, wherein the display region includes an iris recognition region; the display panel further includes a light conversion layer disposed in the iris recognition region, the light conversion layer is configured to convert visible light incident on the light conversion layer into infrared light, and the infrared light is emitted from a display side of the display panel.

A method and apparatus for waking up a device, an electronic device, and a storage medium are provided, which are related to fields of image processing and deep learning. The method includes: acquiring an environment image of a surrounding environment of a target device in real time, and recognizing a face region of a user in the environment image; acquiring a plurality of facial landmarks in the face region, and acquiring a left eye image and a right eye image according to the facial landmarks; acquiring a left eye sight classification result and a right eye sight classification result according to the left eye image and the right eye image; and waking up the target device in a case of determining that the user is looking at the target device according to the left eye sight classification result and the right eye sight classification result.

A method and apparatus for waking up a device, an electronic device, and a storage medium are provided, which are related to fields of image processing and deep learning. The method includes: acquiring an environment image of a surrounding environment of a target device in real time, and recognizing a face region of a user in the environment image; acquiring a plurality of facial landmarks in the face region, and acquiring a left eye image and a right eye image according to the facial landmarks; acquiring a left eye sight classification result and a right eye sight classification result according to the left eye image and the right eye image; and waking up the target device in a case of determining that the user is looking at the target device according to the left eye sight classification result and the right eye sight classification result.

Systems and methods for eye image set selection, eye image collection, and eye image combination are described. Embodiments of the systems and methods for eye image set selection can include comparing a determined image quality metric with an image quality threshold to identify an eye image passing an image quality threshold, and selecting, from a plurality of eye images, a set of eye images that passes the image quality threshold.

Systems and methods for eye image set selection, eye image collection, and eye image combination are described. Embodiments of the systems and methods for eye image set selection can include comparing a determined image quality metric with an image quality threshold to identify an eye image passing an image quality threshold, and selecting, from a plurality of eye images, a set of eye images that passes the image quality threshold.

An imaging display device includes an imaging unit, a processing unit, a display unit, and a pupil detection unit. The imaging unit includes a plurality of photoelectric conversion elements and is configured to acquire first image information. The processing unit is configured to process a signal from the imaging unit and generate second image information. The display unit is configured to display an image that is based on the signal from the processing unit. The pupil detection unit is configured to detect vector information of a pupil. The processing unit generates the second image information by processing the first image information based on the vector information on the pupil.

An imaging display device includes an imaging unit, a processing unit, a display unit, and a pupil detection unit. The imaging unit includes a plurality of photoelectric conversion elements and is configured to acquire first image information. The processing unit is configured to process a signal from the imaging unit and generate second image information. The display unit is configured to display an image that is based on the signal from the processing unit. The pupil detection unit is configured to detect vector information of a pupil. The processing unit generates the second image information by processing the first image information based on the vector information on the pupil.

The invention relates to a device and a method for performing an eye gaze mapping (M), in which at least one point of vision (B) and/or a viewing direction of at least one person (10) in relation to at least one scene recording (S) of a scene (12) viewed by the at least one person (10) is mapped onto a reference (R). At least a part of an algorithm (A1, A2, A3) for performing the eye gaze mapping (M) is thereby selected from multiple predetermined algorithms (A1, A2, A3) as a function of at least one parameter (P), and the eye gaze mapping (M) is performed on the basis of the at least one part of the algorithm (A1, A2, A3).