An image processing method includes analyzing a first image including a face image in a plurality of directions, including recognizing photographing background information of the first image and brightness information and makeup information that are of a face, so that skin beautification processing can be purposefully performed on the face image comprehensively based on specific background information of the image, actual brightness distribution of the face, and actual makeup of the face.

Systems and apparatuses for generating surface dimension outputs are provided. The system may collect an image from a mobile device. The system may analyze the image to determine whether they comprise one or more standardized reference objects. Based on analysis of the image and the one or more standardized reference objects, the system may determine a surface dimension output. The system may determine one or more settlement outputs and one or more repair outputs for the driver based on the surface dimension output.

A system and method of anonymization of a face in a set of images by at least one processor, the method including: receiving a first set of images; extracting from the first set of images a first face, depicting a person and having a first set of attributes; and performing a perturbation of the first face to produce a second face having a second set of attributes, wherein the second set of attributes is adapted to be visually perceived by a viewing entity as being substantially equivalent to the first set of attributes, and wherein the second face is adapted to be visually perceived by a viewing entity as not pertaining to the depicted person.

Techniques are described for passive three-dimensional (3D) face imaging based on macro-structure and micro-structure image sizing, such as for biometric facial recognition. A set of images of a user's face is processed to extract authentication deterministic macro-structure (DMAS) measurements. A database includes profile DMAS measurements, profile location definitions for deterministic micro-structure (DMIS) feature regions, and profile DMIS signatures computed for the DMIS feature regions. A first-level authentication determination can be based on comparing the authentication DMAS measurements with the profile DMAS measurements. Authentication DMIS signatures can be computed from sub-images obtained for the DMIS feature regions at the profile location definitions. A second-level authentication determination can be based on comparing the authentication DMIS signatures with the profile DMIS signatures. An authentication result can be output based on both the first-level authentication determination and the second-level authentication that indicates whether authentication of the user is granted or denied.

A skin color identification method comprises: acquiring an image of human face; determining a target color gamut difference of respective pixels in the image of human face, the target color gamut difference of the respective pixels being a difference between intensity values of two designated color components of the respective pixels; and determining a confidence degree of skin color that the skin color of a human face in the image of human face belongs to a target skin color according to the target color gamut differences of all pixels in the image of human face, the confidence degree of skin color reflecting a probability that the skin color of the human face in the image of human face is the target skin color. A skin color identification apparatus and a computer-readable storage medium are further provided.

Technology described herein includes a method that includes receiving, at one or more processing devices, data corresponding to a first image, and determining, by the one or more processing devices based on the received data, that a first set of pixel values of the first image corresponds to illumination of a first representative wavelength, and at least a second set of pixel values of the first image corresponds to illumination of a second representative wavelength. The illuminations of the first and second representative wavelengths constitute at least a portion of a first illumination sequence pattern used in capturing the first image. The method also includes determining that the first illumination sequence pattern matches a second illumination sequence pattern associated with a device from which the first image is expected to be received, and in response, initiating a biometric authentication process for authenticating a subject represented in the first image.

A face image processing method and apparatus, an electronic device, and a storage medium are provided. The method includes: obtaining face key points and a face deflection angle in a face image; determining a submalar triangle center in the face image according to the face key points and the face deflection angle; determining a submalar triangle region in the face image according to the face key points and the submalar triangle center; and performing color filling on the submalar triangle region. By means of the present disclosure, a submalar triangle region can be accurately positioned, and submalar triangle filling is performed based on the accurately positioned submalar triangle region, thereby obtaining a more natural filling effect.

The disclosure provides an image processing method, an image processing apparatus, an electronic device and a storage medium, which belongs to the field of computer technologies, and specifically relates to computing vision, image processing, face recognition, and deep learning technologies in artificial intelligence. The method includes: performing skin color recognition on a face image to be processed to determine a target skin color of a face contained in the face image; obtaining a reference transformation image corresponding to the face image by processing the face image using any style transfer model in response that a style transfer model set does not comprise a style transfer model corresponding to the target skin color; and obtaining a target transformation image matching the target skin color by adjusting a hue value, a saturation value, and a lightness value of each pixel in the target region based on the target skin color.

A computer, including a processor and a memory, the memory including instructions to be executed by the processor to acquire a first image by illuminating a first object with a first light beam, determine a first measure of pixel values in the image and perform a comparison of the first measure of pixel values to a second measure of pixel values determined from a second image of a second object, wherein the second image is previously acquired by illuminating the second object with a second light beam. The instructions include further instructions to, when the comparison determines that the first measure is equal to the second measure of pixel values within a tolerance, determine that the first object and the second object are a same object.

Disclosed is a method and an order generation system for a software interface, the method including: by a UE, collecting operation information about a target object, and determining an operation type and an operation coordinate of the target object on the software interface; acquiring a coordinate set in a price coordinate system of the software interface;

calculating differences between the operation coordinate and the plurality of coordinate values to obtain a plurality of difference values, extracting a minimum difference value from the plurality of difference values, and obtaining a first price corresponding to the minimum difference value; and generating a first straight line matching the first price and the operation type, and sending a transaction request including the first price and the operation type to a background server after superimposing the first straight line onto the software interface.