Abstract: A method includes, in response to a first operation performed on a first electronic device, obtaining, by the first electronic device, an image using a camera of the first electronic device, where the image includes a hand region and a face region, and the first operation indicates a start of a skin care process; extracting a target image feature from the image, where the target image feature is a feature related to the hand region and the face region; and outputting integrity of the skin care process based on target image features corresponding to a plurality of frames of images, where the frames of images are images generated in a time sequence in the skin care process.

Abstract: This application provides a voice interaction method. For a plurality of electronic devices equipped with a same voice assistant, a voice assistant of only one electronic device is in a working mode, and voice assistants of other electronic devices are all in a silent mode. When a user gives a voice for the voice assistant, the other electronic devices whose voice assistants are in the silent mode do not respond to a first instruction triggered by the voice of the user, only the electronic device whose voice assistant is in the working mode sends, according to the first instruction triggered by the voice of the user, a second instruction to an electronic device that is in the plurality of electronic devices and that has a capability of executing a task requested by the user, and the electronic device that receives the second instruction executes the task requested by the user.

Abstract: This application provides communication methods for a terminal device and a network side device. In an example method, a terminal device establishes a primary communication link with a network side device. The terminal device detects one or more other terminal devices, determines, from the one or more other terminal devices, one or more terminal devices as one or more coordinated communication devices, and notifies the network side device of information indicating that a coordinated communication device is used for coordinated communication. The terminal device establishes a device-to-device (D2D)-based virtual multiple-input-multiple-output (MIMO) communication connection with the coordinated communication device, to form, between the terminal device and the network side device, a coordinated communication link relayed by the coordinated communication device.

Abstract: An appearance analysis method includes an electronic device obtaining a first image associated with a first region of an object and a second image associated with a second region of the object, where the first image is collected by a first camera, where the second image is collected by a second camera, and wherein the first region is different from the second region. The electronic device provides an appearance evaluation of the object, where the appearance evaluation is determined based on the first image and the second image.

Abstract: Embodiments of this application provide a speech recognition method. The speech recognition method includes: obtaining a facial depth image and a to-be-recognized voice of a user, where the facial depth image is an image collected by using a depth camera; recognizing a mouth shape feature from the facial depth image, and recognizing a voice feature from a to-be-recognized audio; and fusing the voice feature and the mouth shape feature into an audio-video feature, and recognizing, based on the audio-video feature, a voice uttered by the user.

Abstract: The technology of this application relates to an artificial intelligence terminal-based image detection method and apparatus, and an electronic device. The method includes obtaining a to-be-detected image, determining a light source region of the to-be-detected image and a foreground region of the to-be-detected image, and determining a blurring degree of the to-be-detected image based on the light source region and the foreground region. Impact of a light source on clarity of the to-be-detected image is determined by performing light source region detection and foreground region detection on the to-be-detected image, to effectively detect whether an image shot in a backlight condition is blurred.

Abstract: An image processing method includes: converting a red yellow blue RYB image into a grid image based on a red green blue RGB format; generating a first brightness layer of the grid image, and determining a reference gain compensation array used to adjust the first brightness layer; obtaining, based on a preset compensation array correspondence, a target gain compensation array that is associated with the reference gain compensation array and based on an RYB format; and adjusting a second brightness layer of the RYB image by using the target gain compensation array, to generate a corrected image.

Abstract: This application provides a skin care check-in method and an electronic device. The method includes: receiving a first operation that is input by a user; in response to the first operation, displaying at least one skin care check-in plan in which the user participates; obtaining first target information, where the first target information includes one or more of time, a location of the electronic device, a movement status of the user, and a schedule of the electronic device; determining, based on the first target information, whether a preset implementation condition for implementing the at least one skin care check-in plan is met; and if a determining result is that the preset implementation condition is met, displaying first reminder information, where the first reminder information is used to prompt the user to implement the at least one skin care check-in plan.

Abstract: A method includes: detecting an abnormal touch event on a display screen of a wearable device; and enabling gesture recognition in response to the abnormal touch event when the wearable device is worn on an arm of a user such that the wearable device can be effectively controlled when there is a touch exception on the wearable device, so that interaction with the wearable device can be conveniently performed.

Abstract: This application provides a voice interaction method. For a plurality of electronic devices equipped with a same voice assistant, a voice assistant of only one electronic device is in a working mode, and voice assistants of other electronic devices are all in a silent mode. When a user gives a voice for the voice assistant, the other electronic devices whose voice assistants are in the silent mode do not respond to a first instruction triggered by the voice of the user, only the electronic device whose voice assistant is in the working mode sends, according to the first instruction triggered by the voice of the user, a second instruction to an electronic device that is in the plurality of electronic devices and that has a capability of executing a task requested by the user, and the electronic device that receives the second instruction executes the task requested by the user.

Abstract: A codec negotiation and switching method and system, an electronic device, a computer-readable storage medium, and a computer program product are disclosed. The system includes an electronic device (100) and an audio play device (200). Before transmitting audio data, the electronic device (100) and the audio play device (200) categorize codecs into a plurality of categories, and determine a common codec category, for example, a first category and a second category, supported by the electronic device (100) and the audio play device (200). The electronic device (100) transmits the audio data to the audio play device (200) based on a codec of the first category. When the codec needs to be switched, the electronic device (100) does not need to renegotiate with the audio play device (200) on a codec type, but directly transmits the audio data based on a codec of the second category.

Abstract: Embodiments of this application provide a speech recognition method. The speech recognition method includes: obtaining a facial depth image and a to-be-recognized voice of a user, where the facial depth image is an image collected by using a depth camera; recognizing a mouth shape feature from the facial depth image, and recognizing a voice feature from a to-be-recognized audio; and fusing the voice feature and the mouth shape feature into an audio-video feature, and recognizing, based on the audio-video feature, a voice uttered by the user.

Abstract: An electronic device includes a display panel, a camera, a fingerprint recognition light source, and a light steering element, where in the display panel, transmittance of a first display area is greater than transmittance of a second display area that includes a fingerprint recognition area, where the camera, the fingerprint recognition light source, and the light steering element are all located on a side of the display panel that is back onto a light outlet surface of the display panel, and where the camera includes an optical-to-electrical conversion chip, the fingerprint recognition light source is configured to emit light to the fingerprint recognition area in a fingerprint recognition mode, and the light steering element is configured to transfer light reflected by a body part that presses the fingerprint recognition area to the optical-to-electrical conversion chip in the fingerprint recognition mode.

Abstract: A method includes, in response to a first operation performed on a first electronic device, obtaining, by the first electronic device, an image using a camera of the first electronic device, where the image includes a hand region and a face region, and the first operation indicates a start of a skin care process; extracting a target image feature from the image, where the target image feature is a feature related to the hand region and the face region; and outputting integrity of the skin care process based on target image features corresponding to a plurality of frames of images, where the frames of images are images generated in a time sequence in the skin care process.

Abstract: This application provides communication methods for a terminal device and a network side device. In an example method, a terminal device establishes a primary communication link with a network side device. The terminal device detects one or more other terminal devices, determines, from the one or more other terminal devices, one or more terminal devices as one or more coordinated communication devices, and notifies the network side device of information indicating that a coordinated communication device is used for coordinated communication. The terminal device establishes a device-to-device (D2D)-based virtual multiple-input-multiple-output (MIMO) communication connection with the coordinated communication device, to form, between the terminal device and the network side device, a coordinated communication link relayed by the coordinated communication device.

Abstract: An appearance analysis method includes an electronic device obtaining a first image associated with a first region of an object and a second image associated with a second region of the object, where the first image is collected by a first camera, where the second image is collected by a second camera, and wherein the first region is different from the second region. The electronic device provides an appearance evaluation of the object, where the appearance evaluation is determined based on the first image and the second image.

Abstract: A photographing method implemented by an electronic device includes receiving a photographing instruction of a user, shooting a primary camera image using a primary camera, shooting a wide-angle image using a wide-angle camera, and generating a three-dimensional (3D) image based on the primary camera image and the wide-angle image, where the 3D image includes a plurality of frames of images that is converted from a 3D viewing angle and corresponds to different viewing angles.

Abstract: The technology of this application relates to an artificial intelligence terminal-based image detection method and apparatus, and an electronic device. The method includes obtaining a to-be-detected image, determining a light source region of the to-be-detected image and a foreground region of the to-be-detected image, and determining a blurring degree of the to-be-detected image based on the light source region and the foreground region. Impact of a light source on clarity of the to-be-detected image is determined by performing light source region detection and foreground region detection on the to-be-detected image, to effectively detect whether an image shot in a backlight condition is blurred.

Abstract: A face image processing method and apparatus, a device, and a computer readable storage medium are provided. The face image processing method includes: obtaining a face image; detecting an eye feature based on the face image, and obtaining eye feature data, where the eye feature includes an eyelid and an iris, and the eye feature data includes an eyelid key point, an iris center, and an iris radius; determining an eye adjustment degree based on the eye feature data; and adjusting the eye feature in the face image based on the eye adjustment degree, to obtain a processed image. According to the face image processing method, real-time correction is performed on a human eye sight line in a face image.

Abstract: This application provides a skin color detection method and apparatus. The skin color detection method includes: obtaining a face image (101); determining a face key point (102) in the face image; determining a skin color estimation region of interest (Region Of Interest, ROI) and an illumination estimation region of interest ROI (103) in the face image based on the face key point; obtaining a detected skin color value (104) corresponding to the skin color estimation region of interest; obtaining a detected illumination color value (105) corresponding to the illumination estimation region of interest; and using the detected skin color value and the detected illumination color value as feature input of a skin color estimation model, and obtaining a corrected skin color value (106) output by the skin color estimation model.