Abstract: A face modeling method and apparatus, an electronic device and a computer-readable medium. Said method comprises: acquiring multiple depth images, the multiple depth images being obtained by photographing a target face at different irradiation angles; performing alignment processing on the multiple depth images to obtain a target point cloud image; using the target point cloud image to construct a three-dimensional model of the target face. The present disclosure alleviates the technical problems of poor robustness and low precision of the three-dimensional model constructed according to the three-dimensional model constructing method.

Abstract: Disclosed are an image processing method and apparatus, and an image processing device. According to the image processing method and apparatus, and the image processing device, an infrared binocular camera collects images formed when a measured object is illuminated by a speckle pattern projected by a projection assembly to obtain a first image collected by a first camera and a second image collected by a second camera; and three-dimensional data of the measured object is determined according to a pair of images constituted by the first image and the second image. By means of the method, three-dimensional data of a measured object can be relatively accurately obtained through measurement, thereby improving precision and accuracy of measurement, and also precision requirements for a projection assembly are low, so that costs can be lowered.

Abstract: The present disclosure provides a target detection method, target detection system and non-volatile storage medium. The target detection method includes: acquiring an image to be detected and anchor parameters of preset type number, wherein the anchor parameters are parameters of an anchor set on the image to be detected, and each type of anchor parameters include an anchor scale and an anchor aspect ratio; inputting the image to be detected and the anchor parameters into a target detection model; and carrying out target detection on the image to be detected on the basis of the anchor parameters by the target detection model to obtain a detection result, the detection result including a category and/or a position of a target object included in the image to be detected.

Abstract: A living body detection method and system, and a computer-readable storage medium are disclosed. The living body detection method includes: acquiring a video including an object to be detected; extracting at least two images to be detected from the video, and determining optical flow information according to the at least two images to be detected; dividing each image to be detected into a foreground image and a background image according to the optical flow information; using a classifier to perform category judgment on the foreground image and the background image to obtain a category distribution of the foreground image and a category distribution of the background image; and obtaining a probability that the object to be detected is a living body according to the category distribution of the foreground image and the category distribution of the background image.

Abstract: A detection method for an image acquisition device, a detection device for an image acquisition device, an electronic device and a computer readable storage medium are disclosed. The image acquisition device includes a light source and a diffuser, the diffuser is configured to scatter light emitted by the light source and the light emitted by the light source is irradiated on at least partial area of a scene, the image acquisition device includes: acquiring an image of the scene; determining an effective area of the image; and determining whether or not the diffuser is in an abnormal working state according to information of the effective area.

Abstract: There are provided in the present disclosure a behavior prediction method, an apparatus, a system and a non-transitory recording medium. The behavior prediction method includes: obtaining a part of behavior action video frame sequence, which only reflects a part of behavior actions but does not reflect all of the behavior actions; selecting a key information frame from the part of behavior action video frame sequence, wherein the key information frame has a significant differentiation in the part of behavior action video frame sequence; and predicting a type of the behavior action based on the key information frame.

Abstract: The application provides a liveness detection method and a liveness detection system. The liveness detection method includes: obtaining first and second face image data of an object to be detected, and at least one of the first and the second face image data being a depth image; determining a first face region and a second face region, determining whether the first and the second face regions correspond to each other, and extracting, when it is determined that the first and the second face region corresponds to each other, a first and a second face image from the first and the second face region respectively; determining a first classification result for the extracted first face image and a second classification result for the extracted second face image; and determining, based on the first classification result and the second classification result, a detection result for the object to be detected.

Abstract: A method of unlocking an electronic device, an unlocking device and system and a storage medium are provided. The method includes: acquiring at least one image to be authenticated, in which the image to be authenticated is an RGB image or an infrared image; obtaining an RGB image authentication result of the image to be authenticated by RGB object authentication of the image to be authenticated in a case where the image to be authenticated is the RGB image, and obtaining an infrared image authentication result of the image to be authenticated by infrared object authentication of the image to be authenticated in a case where the image to be authenticated is the infrared image; and determining whether to unlock the electronic device according to at least one of the RGB image authentication result and the infrared image authentication result.

Abstract: This disclosure provides an image processing method, an electronic device and a non-transitory computer-readable storage medium, and relates to the field of image processing technique. The image processing method and apparatus and the electronic device provided by this application perform denoising on the to-be-processed image to obtain a denoised image, and then perform detail enhancement on the target area image extracted from the denoised image to obtain a detail enhanced image, thereafter fuse the denoised image with the detail enhanced image to obtain an output image. Since detail enhancement has been performed on the target area image, the obtained output image is sharper and the display effect is better.

Abstract: The disclosure provides method for face-to-unlock, authentication device, and non-volatile storage medium. The method for face-to-unlock includes: obtaining to-be-verified expression data of a to-be-verified expression of a to-be-verified object; comparing the to-be-verified expression data with pre-stored expression data to obtain a comparison result; and in the case where the comparison result indicates that the to-be-verified expression data matches the pre-stored expression data, releasing the lock.

Abstract: The embodiments of this disclosure provide a face spatial positioning method, apparatus and system, and a non-transitory recording medium. The method includes: obtaining a facial image acquired by an image acquisition device; performing facial area detection on the facial image, to determine position information of a facial area in the facial image; and determining spatial position information of the face according to the position information of the facial area. Accordingly, the embodiments of this disclosure can determine spatial position information of the face according to the facial image acquired by only one image acquisition device, no additional image acquisition device is needed, which can reduce the cost and ensure the processing efficiency.

Abstract: An information push method includes: acquiring a monitoring image captured by a camera; identifying a target group contained in the monitoring image; performing a group relationship recognition on the target group according to attribute data of the target group to recognize a group relationship type of the target group; and pushing a recommendation information to at least one designated terminal corresponding to the target group based on the group relationship type. An information push device and an information push system are also provided.

Abstract: The embodiment of the present disclosure relates to an image processing method, which includes: acquiring a first image and a second image captured for a same object; and determining depth information of the object according to the first image and the second image. The embodiment of the present disclosure further provides an image processing device and a computer readable medium.

Abstract: There are provided in embodiments of the present disclosure a living body detection method, system and a non-transitory computer-readable recording medium. The living body detection method includes: acquiring device information of a terminal used to acquire an image of an object to be detected; determining a device risk level of the terminal; determining a living body detection strategy based on the device risk level of the terminal. The above technical solution adjusts the living body detection strategy by utilizing the device information of the terminal, which guarantee a true living body to go through a pass of the detection at a smaller cost, and at the same time makes it difficult for a false and malicious living body to go through a pass of the detection, thereby greatly reducing security risk of the living body detection, enhancing the user experience, and preventing the malicious request from occupying the system resources.

Abstract: The present disclosure provides an object detection method, an object detection device, an object detection system and a storage medium. The object detection method includes: acquiring an image to be processed; and inputting the image to be processed into a neural network to obtain a feature map outputted by the neural network. The feature map includes position channels and attribute channels; the position channels include at least one group of candidate position information respectively corresponding to at least one candidate position of at least one prediction object in the image to be processed; and the attribute channels include at least one group of candidate attribute information respectively corresponding to the at least one candidate position.

Abstract: The application provides a data update method for face-to-unlock authentication, an authentication device and system, and a non-volatile storage medium. The data update method for face-to-unlock authentication includes: acquiring facial image data of a user to be unlock-authenticated; obtaining a plurality of candidate sets from the facial image data; determining whether there is target data satisfying a predetermined condition among the plurality of candidate sets; in the case where it is determined that there is target data satisfying the predetermined condition among the plurality of candidate sets, updating facial image data in a first set by using the target data, wherein a first unlock authentication is performed based on the facial image data in the first set.

Abstract: A facial image generating method, a facial image generating apparatus, and a facial image generating device are proposed. The method comprises: linking an M-dimensional facial feature vector with an N-dimensional demanded feature vector to generate a synthesized feature vector; and generating a synthesized facial image by use of a deep convolutional network for facial image generation and based on the synthesized feature vector. The method further comprises: generating a demand satisfaction score based on the synthesized facial image and the demanded feature vector by use of a deep convolutional network for demand determination; and updating parameters of the deep convolutional network for facial image generation and the deep convolutional network for demand determination based on the demand satisfaction score. A facial image is generated based on a facial feature vector and a demanded feature vector, a facial image with a specific feature can be generated without using the three-dimensional model.

Abstract: A facial expression detection method includes: obtaining an image to be processed; inputting the image to be processed into a neural network model to obtain a facial expression parameter of a target face in the image to be processed; and outputting the facial expression parameter of the target face for driving an object to be driven.

Abstract: The embodiments of this application provide a facial recognition method and system, and a non-transitory recording medium. The method includes: obtaining a first feature vector of a first face in a first facial image and a first confidence vector having the same dimension as the first feature vector, wherein elements in the first confidence vector are used to indicate credibility of features represented by the corresponding elements in the first feature vector; obtaining a second feature vector of a second face in a second facial image and a second confidence vector having the same dimension as the second feature vector, wherein elements in the second confidence vector are used to indicate credibility of features represented by the corresponding elements in the second feature vector; and determining the first confidence vector, the second feature vector and the second confidence vector, whether the first and second face belong to the same person.

Abstract: The present disclosure provides a living body detection method, an electronic device and a computer readable medium. The living body detection method includes: obtaining video images of a target object; extracting a first image and a second image from the video images, wherein the first image is used to represent static information in the video images, and the second image is used to represent position change information of all pixel points in the video images; making a living body analysis on the first image to obtain a first analysis result, making a second body analysis on the second image to obtain a second analysis result, and determining whether the target object is a living body according to the first analysis result and the second analysis result.