9 results about "Face registration" patented technology

The invention discloses a face quality label generation method based on cooperation of a multi-modal large model and a computer vision algorithm, and relates to the technical field of artificial intelligence and computer vision crossing, in particular to a face quality label generation method based on collaborative decision making of the multi-modal large model and the computer vision algorithm. The method is especially suitable for a high-security-level identity verification system, an automatic identification photo auditing platform, a large-scale face registration pre-screening process, a living body detection front level and other scenes, and is used for carrying out fine-grained, interpretable and high-robustness quality evaluation and defect positioning on an input face image. The core problems of weak semantic understanding ability, unstable discrimination precision, rigid fusion strategy, poor scene adaptability and the like in the prior art are solved by constructing a four-level technical architecture of dimension decoupling, double-channel parallelism, dynamic confidence fusion and scene adaptive evaluation.

The invention discloses an image point cloud extraction method for face registration, which comprises the following steps: S1, acquiring an electronic computed tomography image of the head of a patient to obtain a skin surface model of the patient, and converting the skin surface model into a point cloud; s2, transforming the point cloud obtained in the step S1 to a standard coordinate system; s3, roughly extracting the point clouds, clustering the point clouds obtained in the step S2 to obtain four clustering centers, and filtering out the point clouds meeting a first preset condition according to the four clustering centers; and S4, finely extracting the point clouds, clustering the point clouds obtained in the step S3 to obtain three clustering centers, and filtering out the point clouds meeting a second preset condition according to the three clustering centers. Using a clustering method to reversely determine an area which needs to be extracted and has unobvious feature points from a plurality of clustering centers of the obtained point clouds through the plurality of clustering centers; clustering and extraction are carried out step by step, the number of clustering points each time is controlled, and the influence of noise features is eliminated.

By utilizing facial recognition, visitors can enter a private space even when the owner is not present, while also enhancing security. [Solution] An authentication method using a system equipped with a facial recognition function and a visitor reception function, comprising: a visitor registration step in which a first user, who is the manager or owner of the occupied space, configures the system to allow a second user, who is a visitor to the occupied space, to enter the occupied space by facial recognition; and a facial registration step in which the second user registers their own face in the system using a second user terminal, which is an information terminal owned by the second user. The problem is solved by this authentication method and authentication system.

The present application provides a processing circuit applied to a face recognition system, which includes a feature value calculation module, a judgment circuit and a threshold value calculation module. The feature value calculation module is used to receive an image and process the image to generate a specific feature value; when the face recognition system operates in a face recognition stage, the judgment circuit calculates a plurality of differences between the specific feature value and a plurality of reference feature values respectively, and judges whether at least one difference is lower than a threshold value in the plurality of differences to generate a judgment result; and when the face recognition system operates in a face registration stage, the threshold value calculation module determines a new threshold value according to the differences between the specific feature value and the plurality of reference values to update the threshold value used by the judgment circuit.

The application provides a bearing double-end-face image registration and difference comparison defect detection method and system, relates to the technical field of machine vision detection, and the method comprises the following steps: extracting bearing outer circle contours and inner hole edges in a first end face standard image and a second end face standard image, and constructing an annular feature band; determining first and second microtexture anchor points on the annular feature band; establishing a local rectangular coordinate system with the first microtexture anchor point as the origin; and when it is detected that the vector azimuth angle of the second microtexture anchor point relative to the first microtexture anchor point is offset, a rotation compensation matrix is calculated. The application gradually completes image preprocessing, global coarse registration, accurate fine registration and defect identification, effectively improves the bearing double-end-face registration accuracy, accurately filters out interference and extracts defect features, realizes the determination of bearing end face defect types and positions, and improves the detection efficiency and accuracy.

A face registration method and device based on a 3D structured light camera, the method captures video stream data of a user's face through the 3D structured light camera; pushes the captured video stream data of the user's face into a server of an industrial computer system through an RTMP service; the host computer system acquires the video stream data of the user's face through the server of the industrial computer system, and according to the video stream data of the user's face, performs scanning and recognition processing on the user's face to complete user face registration; the industrial computer system performs live detection and picture quality detection on the registered user's face, and performs screenshot processing on the user's face picture that has passed the detection; the host computer system performs preliminary processing on the received screenshot and sends it to a cloud server; the cloud server registers the user's face according to the screenshot to complete user face registration and cloud backup. The application can adapt the 3D structured light camera to the Kirin system and realize fast and accurate face registration.

A vehicle includes an authentication camera provided on an exterior of a body of the vehicle, a vehicle terminal provided on an interior of the body and receiving a user input, an auxiliary display provided on the interior of the body and displaying an review view image, and a processor controlling, upon receipt of a face registration start command for a user, the vehicle terminal to display guide information for a face registration procedure, controlling the auxiliary display to display a guideline and image information acquired by the authentication camera, acquiring outline information of a face image based on the acquired image information, recognizing face information based on the acquired outline information of the face image and location information of the guideline, and registering the recognized face information as the face information of the user.

The application discloses a transcranial magnetic navigation face registration device, and relates to the technical field of computer vision, which collects a depth map and a color map of a target region and records camera intrinsic parameters. Face detection and face segmentation processing are performed on the depth map and the color map to generate a trusted area mask. Based on the camera intrinsic parameters, the depth map and the trusted area mask, a face trusted point cloud is generated; the face trusted point cloud is registered with a head surface model based on an image space to obtain a spatial transformation matrix. When the spatial transformation matrix meets registration requirements, target point navigation of a transcranial magnetic navigation system is performed according to the spatial transformation matrix. Under the condition of not contacting the face of a user, the invasiveness is reduced by analyzing the depth map and the color map collected by the camera. The spatial transformation matrix can be obtained through automatic registration, and registration requirements are set, so that a non-contact, automatic and quantifiable evaluation spatial registration scheme of a TMS navigation system is realized.

1. Name of the product in this design: Graphical User Interface for Face Image Entry in Electronic Device. 2. Purpose of this design: An electronic device. 3. The key design feature of this product is its graphical user interface. 4. Images or photos that best illustrate the design points: Design 1 Interface Change State Diagram 5. 5. Design 1 is designated as the basic design. 6. Purpose of the graphical user interface: The interface is used to guide the user to place the captured facial image in a specific position and prompt the user to make corresponding facial movements, guiding the user to complete the process of facial image registration. 7. Human-computer interaction method of graphical user interface: In Design 1 and Design 2, the main view is the main interface displayed by the electronic device. The user takes a picture of their face in the gray area of ​​the main view, starts the face registration function and rotates their face. The interface switches sequentially to interface change state diagram 1, interface change state diagram 2, interface change state diagram 3, interface change state diagram 4 and interface change state diagram 5. After the user completes the face registration, the interface automatically jumps from interface change state diagram 5 to interface change state diagram 6, interface change state diagram 7 and interface change state diagram 8 in sequence. The gray area in the middle of the interface is used to display the captured face image. In Designs 3 and 4, the main view is the main interface displayed by the electronic device. The user takes a picture of their face in the gray area of ​​the main view, turns on the face recognition function, and rotates their face. The interface then switches to the following states in sequence: interface change state diagram 1, interface change state diagram 2, interface change state diagram 3, interface change state diagram 4, interface change state diagram 5, interface change state diagram 6, and interface change state diagram 7. The gray area in the middle of the interface is used to display the captured facial image. In Designs 5 and 6, the main view is the main interface displayed by the electronic device. The user takes a face image in the main view and places it in the middle area of ​​the interface. The face registration function is activated and the user rotates their face. The interface switches sequentially to interface change state diagram 1, interface change state diagram 2, interface change state diagram 3, interface change state diagram 4, interface change state diagram 5 and interface change state diagram 6. The black area in the interface is used to display the captured face image. In Designs 7 and 9, the main view is the main interface displayed by the electronic device. When the user takes a picture of their face in the main view, it is placed in the gray area. When the face registration function is enabled, the interface automatically switches to interface change state diagram 1, interface change state diagram 2, interface change state diagram 3 and interface change state diagram 4 in sequence, prompting the user to turn their head to the right. The gray area in the interface is used to display the captured facial image. In Design 8, the main view is the main interface displayed by the electronic device. When the user takes a picture of their face in the main view, it is placed in the gray area. When the user turns their head to the right, the interface automatically switches to interface change state diagram 1, interface change state diagram 2 and interface change state diagram 3 in sequence. The gray area in the interface is used to display the captured facial image. In Design 10, the main view is the main interface displayed by the electronic device. The user takes a picture of their face in the main view and places it in the gray area. The interface switches sequentially to Interface Change State Diagram 1, Interface Change State Diagram 2 and Interface Change State Diagram 3. The gray area in the interface is used to display the captured facial image.