4 results about "Three-dimensional face recognition" patented technology

The invention discloses a 3D face cluster classification system based on a twin point cloud network, and relates to the field of three-dimensional face recognition, and the system comprises a point cloud obtaining and preprocessing module which is used for obtaining original three-dimensional face point cloud data, and carrying out the spatial normalization and enhancement processing of the data; the twin feature extraction module is used for hierarchically extracting multi-scale local features from the preprocessed point cloud and aggregating the multi-scale local features into global feature representation; the geometric consistency constraint module is used for generating a geometric constraint signal based on the similarity measurement between the input point cloud pairs in the original geometric space in the feature learning process; and the collaborative optimization judgment module is used for fusing the geometric constraint signal and classification loss based on depth features, jointly guiding parameter updating, and finally outputting a human face group classification result. According to the scheme of the invention, the instability of feature characterization under a complex acquisition condition can be reduced, and the discrimination capability of fine geometric differences of different ethnic groups is enhanced.

The present application relates to the technical field of optical dot array projector, and especially relates to a regular dot array pattern projector device based on VCSEL, which receives configuration instructions through a parameter configuration module and outputs parameters such as lateral period, longitudinal period and offset; a light source generation module integrates a vertical cavity surface emitting laser chip, and generates laser beams with corresponding divergence angles according to arrangement parameters; an optical processing module includes a microlens array, a diffractive optical element or a mask element, the unit arrangement parameters of which are consistent with the configuration parameters, and the optical processing module performs beam splitting, diffraction or screening processing on the light beams; a distance control module calculates and maintains the working distance between the chip and the optical element based on the generalized Talbot principle; and a pattern generation module converts the processed light beams into regular dot array patterns and projects the regular dot array patterns to a target area. The present application does not need physical cutting and splicing of the microlens array, reduces the customization cost and detection complexity, improves the production yield and pattern accuracy, and is suitable for three-dimensional face recognition and industrial vision scenes.

The present application relates to a kind of three-dimensional face recognition methods for coal mine underground, belong to multidimensional stereoscopic face recognition technical field.The method is converted into three-dimensional point under camera coordinate system by two-dimensional face image, and rotation matrix is combined to carry out rotation operation, generates the rotated face side face data including multiple angles, provides rich data basis for three-dimensional reconstruction.PointNet++ and its extended structure are used in conjunction with auto-encoder framework to establish three-dimensional reconstruction model, can extract compact feature representation from three-dimensional point cloud, and utilize these features to reconstruct three-dimensional facial image with details and geometric structure, three-dimensional facial image is divided into several recognition regions, and feature recognition matching is carried out respectively;Identification, only need to filter clear area to carry out image reconstruction and feature matching, can directly give face recognition result.The present application improves the accuracy and fidelity of reconstruction.

The application relates to a three-dimensional face recognition method, system, medium and equipment based on deep learning, which comprises the following steps: acquiring a first face depth image and a second face depth image; recognizing the first face depth image to obtain a first focusing area; recognizing the first face depth image to obtain a second focusing area; comparing the first focusing area with the second focusing area to determine whether the first focusing area and the second focusing area are completely coincident; and inputting the first face depth image and the second face depth image into a pre-established face recognition model to complete face recognition in the case that the first focusing area and the second focusing area are not completely coincident. The three-dimensional face recognition based on deep learning is realized by focusing on different parts of the face through twice face image collection with different focal lengths according to the depth information of the face, and by comparing the focusing areas of the two images.