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

A mobile device can generate a three-dimensional model of a person's face by capturing and processing a plurality of two-dimensional images. During operation, the mobile device uses an image-capture device to capture a set of images of the person from various orientations as the person or any other user sweeps the mobile device in front of the person's face from one side of his / her face to the opposing side. The device determines orientation information for the captured images, and detects a plurality of features of the person's face from the captured images. The device then generates a three-dimensional model of the person's face from the detected features and their orientation information. The three-dimensional model of the person's face facilitates identifying and / or authenticating the person's identity.

The invention discloses a fast 3D face identifying method based on double-eye passive solid sight, which includes the following steps: 1) a non-contact short shaft parallel binocular stereo vision system is built by applying two high-definition digital cameras; 2) after system calibration is finished, face detection and collection based on a haar-AdaBoost sorting machine is carried out on a preview frame image for obtaining corresponding upper and lower stereoscopic vision graph pairs and estimating a sight difference; image correction is carried out on a face area for obtaining the upper and lower stereoscopic vision graph pairs vertical to the polar lines inside and outside the area; 3) the accurate location on the eyes and a spex nasi is captured by applying a Bayesian and the haar-AdaBoost sorting machines as well as point cloud 3D information for building a benchmark triangle; 4) the corresponding sub pixels in the middle and small areas are matched by applying the pyramidal parallel search solid graph of a phase relevant arithmetic based on a complex wavelet; 5) pose normalizing and hole filling are carried out on the faces under different poses by applying the built benchmark triangle; 6) expression normalization is carried out on different faces based on the suppose that the surface geodesic distance of the face is invariable; 7) the 3D faces after normalization are identified by utilizing the arithmetic. The method has the beneficial effects of: mainly solving the problems of being hard to fast and automatically obtain the passive stereoscopic vision and identifying the 3D point cloud information of the dense and accurate face under different poses and expressions, thus leading the 3D face identifying process to be faster, more hidden, safer and more reliable.

The invention relates to a three-dimensional facial reconstruction method, which can automatically reconstruct a three-dimensional facial model from a single front face image and puts forward two schemes. The first scheme is as follows: a deformable face model is generated off line; Adaboost is utilized to automatically detect face positions in the inputted image; an active appearance model is utilized to automatically locate key points on the face in the inputted image; based on the shape components of the deformable face model and the key points of the face on the image, the geometry of a three-dimensional face is reconstructed; with a shape-free texture as a target image, the texture components of the deformable face model are utilized to fit face textures, so that a whole face texture is obtained; and after texture mapping, a reconstructed result is obtained. The second scheme has the following differences from the first scheme: after the geometry of the three-dimensional face is reconstructed, face texture fitting is not carried out, but the inputted image is directly used as a texture image as a reconstructed result. The first scheme is applicable to fields such as film and television making and three-dimensional face recognition, and the reconstruction speed of the second scheme is high.

The present invention discloses a three-dimensional human face identifying method based on human face full-automatic positioning and belongs to the computer vision and mode identifying field. A human face virtual image generating method comprises the steps as follows: a two-dimensional human face shape model and a partial veins model are established; a two-dimensional human face image is positioned exactly; the two-dimensional human face image is processed for three-dimensional reconstruction according to the positioning result to obtain a three-dimensional human face image; the three-dimensional human face image is processed for illumination model treatment to obtain a virtual image with changeable gestures and illumination. The method comprises the steps as follows: characteristics are picked up from the human face image to be identified and compressed; the human face is identified according to the compressed and processed characteristics. The present invention embodiment generates the virtual image by the three-dimension reconstructing of the two-dimensional human face image and by processing the illumination model, thereby increasing the sample space of the gesture and the illumination change of the image; at the same time, the three-dimension reconstructing speed is improved greatly, thereby ensuring that human face image identification has higher efficiency and recognition rate.

The invention discloses a three-dimensional face recognition method and system. The method includes: image data of a to-be-identified target face are collected and a face key point is calibrated, wherein the image data of the face include a depth image of the face and a color image of the face; according to the calibrated depth image of the face, three-dimensional face reconstruction is carried out to obtain a three-dimensional face reconstruction model; surface distances between a preset number of key points in the three-dimensional face reconstruction model are calculated and a surface distance matrix is generated; the surface distance matrix is transformed into a face standard model; a to-be-identified feature vector is extracted from the face standard model; and the to-be-identified feature vector is compared with existing feature vectors in the preset face feature database to realize three-dimensional face recognition. The three-dimensional face recognition method has advantages of high recognition accuracy and fast recognition speed.

Disclosed is a three-dimensional face recognition method based on feature points. The method comprises the following steps that firstly, a three-dimensional face model is preprocessed, and point cloud data are mapped into a depth image through linear interpolation; then, Gabor filtering is applied to the depth image to roughly locate the face feature points, and then the feature points are precisely located on a face point cloud according to ShapeIndex features; then, a series of equally-measured contour lines with the nose bridge as the center is extracted for representing the shape of the face, and Procrustean vector features (distance and angles) with posture invariance are extracted as recognition features; finally, weighted fusion is carried out on all equally-measured contour line features for final recognition. The three-dimensional face recognition method has the good locating and recognition performance, and is good in robustness in expression and posture.

The invention discloses a local descriptor-based three-dimensional face recognition method, which comprises the following steps of: (1) preprocessing a face library model and a testing face model by face cutting, gesture normalization and dilution, and finally establishing a face principle axis coordinate system by taking the tip of nose as a center; (2) extracting equidistant contour lines from the face library model and the testing face model, extracting sixteen contour lines equidistant to a point of the tip of nose by taking the point of tip of nose as a center, and resampling to acquire the same number of sampling points; (3) latticing the face library model and the testing face model, and extracting a local projection area on the acquired sampling points in the step (3) as a local characteristic; (4) establishing one-to-one correspondence between the sampling points in the same sequence on the face library model and the testing face model, and comparing local characteristics of corresponding points; and (5) taking Euclidean distance of the local characteristics between the corresponding points as similarity, and selecting a face which is most similar to the testing face from the face library as a recognition result.

Apparatus for obtaining 3-Dimensional data of a geometric body for matching, and particularly for use in facial matching, comprises a three dimensional scanner for obtaining three-dimensional topographical data of the body, a triangulator for receiving or forming said data into a triangulated manifold, a geodesic converter, for converting the triangulated manifold into a series of geodesic distances between pairs of points of the manifold, and a multi-dimensional scaler, for forming a low dimensional Euclidean representation of the series of geodesic distances, to give a bending invariant representation of the geometric body. In one variant, matching is carried out by taking the principle eigenvalues from the representation and plotting as co-ordinates in a feature space. Tilted or different expression versions of the same face tend to form clusters in the feature space allowing for matching. The apparatus preferably uses the fast marching method for the triangulated domain to obtain the geodesic distances.

The invention discloses a method for adjusting a universal three-dimensional human face model, belongs to the technical field of computer image processing, and mainly relates to three-dimensional human face reconstruction technology in three-dimensional human face recognition technology in biological feature recognition. Based on the universal three-dimensional human face model, the method adjusts the three-dimensional human face model by estimating three-dimensional coordinates of human face feature points in two orthogonal two-dimensional human face images, and finally obtains a three-dimensional specific human face model. The method applies an active shape model, Procrustes analysis and symmetry structural feature of a human face to the process of adjusting the universal three-dimensional human face model. The method estimates the human face feature points through the active shape model, and adjusts the three-dimensional human face model through the Procrustes analysis and the symmetry structural feature of the human face. Compared with common methods based on images, the method of the invention not only improves the accuracy of adjusting the model, but also improves the speed of adjusting the model in certain degree, and has strong commonality in three-dimensional human face reconstruction based on the images.

The invention relates to a method for rapidly identifying a three-dimension face, which takes side contour line matching as exclusion algorithm, carries out exact matching by utilizing the rigid area of the face and reduces expression influence; the method comprises the steps that: firstly, characteristic points such as the tip of the nose and the inner canthus of a three-dimension face model are extracted, the symmetric face of the face is determined according to the characteristic points, and the side contour line is formed by the crossing of the symmetric face of the face and the point cloud of the three-dimension face. The valuable side contour line is intercepted in the side contour line of the tested face model, the valuable side contour line of the tested face model is matched with the side contour line of the face model in a stored collection, which is used for matching, by utilizing the iterative closest point algorithm, and part dissimilar models in the stored collection are excluded according to matching results. Then, the first rigid area and the second rigid area of the tested face are extracted and respectively matched with the remaining models of the stored collection, the matching adopts the improved iterative closest point algorithm based on projection and neighborhood searching, and the marching results are blended by adopting an addition rule. Finally, the identification result is obtained by a nearest neighbor classifier.

The present disclosure provides a three-dimensional facial recognition method and system. The method includes: performing pose estimation on an input binocular vision image pair by using a three-dimensional facial reference model, to obtain a pose parameter and a virtual image pair of the three-dimensional facial reference model with respect to the binocular vision image pair; reconstructing a facial depth image of the binocular vision image pair by using the virtual image pair as prior information; detecting, according to the pose parameter, a local grid scale-invariant feature descriptor corresponding to an interest point in the facial depth image; and generating a recognition result of the binocular vision image pair according to the detected local grid scale-invariant feature descriptor and training data having attached category annotations. The present disclosure can reduce computational costs and required storage space.

The invention discloses a three-dimensional human face recognition method and system, and a data processing device applying the same. According to the embodiment of the invention, a series of key points having relatively high generic relevance are detected on a human face, and obvious difference exists when the key points are distributed on different human faces; and on the basis, fusion recognition of the three-dimensional human face is achieved through local characteristic matching and point cloud registration. Firstly, the unfavorable influence of expression change on human face recognition can be effectively reduced by using local characteristics; and secondly, the fusion recognition is achieved through characteristic matching and point cloud registration by comprehensively using local and global information of the human face, and the accuracy of the recognition method is further improved. Compared with the traditional method, the recognition method has the advantages that the recognition accuracy and the robustness are improved greatly.

The invention discloses a three-dimensional gait recognition based on shielding recovery and partial similarity matching. Moving human body detection, deep data filtering recovery, point cloud data extraction, reduction and three-dimensional human body surface modeling are carried out through a gait depth image collected by a single depth vidicon. Partial public gait feature extraction and fusion at various vision angles is carried out on all three-dimensional surface gait models through a partial similarity matching method; recovery of gait shielding area data is finished through a shielding conversion recovery model based on prior knowledge and singular value decomposition, and therefore multi-vision-angle three-dimensional gait classification recognition under a shielding condition is finished in a classification mode. The three-dimensional gait recognition based on shielding recovery and partial similarity matching resolves the problems that an existing gait recognition method can not well achieve gait surface model point cloud reduction rebuilding with the single vidicon and multi-vision-angle gait recognition under a shielding condition, provides a new measure for vision angle changeable gait recognition under a shielding condition, and has good economical and social benefits.

The invention discloses DPM encoding equipment, an automatic two-dimensional code recognition platform, and a method for recognizing a DPM two-dimensional code. A series of links such as image acquisition, automatic recognition, error correction and decoding of the DPM two-dimensional code are realized by fusing interdisciplinary algorithms such as computer vision, pattern recognition and image processing, and disadvantages in an image, such as nonuniform illumination, low contrast, blurring, rough material and two-dimensional code stain are overcome, so that a purpose of accurately and rapidly recognizing and reading the DPM two-dimensional code is achieved.

The invention describes a three-dimensional face recognition device based on three-dimensional point cloud and a three-dimensional face recognition method based on three-dimensional point cloud. The device includes a feature region detection unit used for locating a feature region of the three-dimensional point cloud, a mapping unit used for mapping the three-dimensional point cloud to a depth image space in a normalizing mode, a statistics calculation unit used for conducting response calculating on three-dimensional face data in different scales and directions through Gabor filters having different scales and directions, a storage unit obtained by training used for storing a visual dictionary of the three-dimensional face data, a map calculation unit used for conducting histogram mapping on the visual dictionary and a Gabor response vector of each pixel, a classification calculation unit used for roughly classifying the three-dimensional face data, a recognition calculation unit used for recognizing the three-dimensional face data.

A 3D face recognition method based on intermediate frequency information in a geometric image as follows: (1) preprocessing a library and test models of 3D faces, including 3D face area cutting, smoothing processing and point cloud thinning, and discarding the lower portion of the face; (2) mapping the remainder of the face to a 2D grid using grid parameters, and performing linear interpolation on the 3D coordinates of the grid top to acquire the 3D coordinate attributes and generating a geometric image of a 3D face model; (3) performing multi-scale filtering with a multi-scale Haar wavelet filter to extract horizontal, vertical, and diagonal intermediate frequency information image images as invariable facial features; (4) calculating the similarity between the test model and the library set model with a wavelet domain structuring similarity algorithm; and (5) judging the test and library set model models with the maximum similarity belong to the same person.

The invention provides an access control system based on three-dimensional face identification. High-quality face data is obtained by a three-dimensional scanner. A two-dimensional camera is used for capturing a two-dimensional face image. The obtained three-dimensional face data and the two-dimensional face image are transferred through a third-party interface module to a computing unit to rebuild a three-dimensional face model. The three-dimensional face model is compared with registered models in a three-dimensional face model bank and recognized. The results are output to a host computer to run a software program. Behavior information of a visiting person is recorded and stored and access control is performed. At the same time, data communication with a liquid crystal display is performed and the needed information is displayed on the liquid crystal display. According to the access control system, three types of information, namely three-dimensional face model information, identification card information and two-dimensional face picture information, are matched, bound and stored in a backend database, thus facilitating recognition and looking up and largely enhancing the safety and stability of the access control system.

The invention discloses an anti-deception three-dimensional face recognition method based on information fusion. The anti-deception three-dimensional face recognition method comprises: 1, collecting and processing color images and depth images of a plurality of real faces; 2, establishing Gaussian distribution models of a plurality of real faces according to the depth information of the key pointsin each depth map, and determining a threshold range of Gaussian distribution parameters of the real faces; 3, establishing a Gaussian distribution model of the to-be-recognized face, comparing and judging the Gaussian distribution model parameters of the to-be-recognized face with the threshold range of the Gaussian distribution parameters of the real faces, if the to-be-recognized face is judged to be the real face, executing the step 4, and otherwise, not performing face recognition; 4, constructing a deep convolutional neural network and training the deep convolutional neural network; and5, inputting the to-be-recognized face image into the trained deep convolutional neural network for recognition, and outputting a recognition result. By analyzing and modeling the face depth information and fusing the face depth information at the data end, a lightweight network is constructed, and the performance of the whole face recognition system is improved.

The invention discloses a key point and local characteristic-based three-dimensional human face identification method. The method comprises the steps of preprocessing a three-dimensional human face model by human face region cutting, smoothing processing and pose normalization, and arranging all human faces in a pose coordinate system; detecting key points according to valuable contour lines and an average curvature; constructing a spatial structure of local characteristics in the form of a DAISY descriptor; adopting a shape index histogram, a slant angle histogram and a directional angle histogram as the local characteristics; and performing key point matching, and measuring the similarity of curved surfaces of two human faces by using the number of successfully matched key points. The method is relatively good in identification performance and has the robustness for expression change to a certain extent.

The invention relates to an electronic name card exchange method based on mobile terminal two-dimensional code recognition. The method is characterized by comprising the following steps that: 1, when a user registers or a new electronic name card is added, a server stores the ID of the user or information of the newly added electronic name card and generates a two-dimensional code corresponding to the ID of the user or the newly added electronic name card; 2, during exchange of electronic name cards, a mobile terminal of the user allows the other side to scan the two-dimensional code generated in the step 1; 3, according to the two-dimensional code information scanned in the step 2, the information of the electronic name card corresponding to the two-dimensional code information is called from the server and is transmitted to a mobile terminal of the other side. The method provided by the invention can overcome the defects that the current paper name card is inconvenient to carry and can be lost easily, resources are wasted, and only a mobile terminal (mobile phone) is needed to realize short-range point-to-point accurate exchange and management of electronic name cards, so commercial activities and the like of individuals or enterprise personnel can be carried out more conveniently.

The invention discloses a three-dimensional face recognition method based on expression invariant regions. Firstly a two-dimensional face region is obtained from two-dimensional face images corresponding to three-dimensional face data through detection; an initial three-dimensional face region is extracted in the three-dimensional face data according to the two-dimensional face region, transverse slicing is performed on the initial three-dimensional face region, and a nose tip point is detected; a relatively accurate three-dimensional face region is extracted according to the nose tip point, and then statistical characteristic vectors and the expression invariant regions are extracted in the three-dimensional face region; the statistical characteristic vectors of a check sample act as the check samples of a reject classifier; and the candidate check samples are obtained according to the statistical characteristic vectors of samples to be recognized, then the set of points of the expression invariant regions of the samples to be recognized are matched with the set of points of the expression invariant regions of the candidate check samples, and a recognition result is obtained according to the matching error. Accuracy of the three-dimensional face region can be enhanced, and accuracy of three-dimensional face recognition is enhanced via combination of the statistical characteristic vectors and the expression invariant regions.

Disclosed is a three-dimensional face recognition method based on face contour lines of semi-rigid areas. The method comprises steps of (1) preprocessing three-dimensional face models, including face area segmentation, smoothing and pose normalization, and placing all faces under a pose coordinate system; (2) extracting multiple vertical contour lines of the faces from the semi-rigid areas of the three-dimensional models to represent the facial curved surfaces of the faces; (3) utilizing an elastic curve matching algorithm to compute the geodesic distances among the corresponding contour lines of different three-dimensional face models in a pre-shape space, taking the geodesic distances as similarity measures, weight-fusing the similarity vectors of all the contour lines to obtain a total similarity for recognition. The three-dimensional face recognition method based on the face contour lines of the semi-rigid areas has high recognition performance, achieves high robustness on change in expression and increases the face matching speed.

The invention provides a three-dimensional portrait comprehensive recognition system, which includes an industrial personal computer, an identity card reader module, a two-dimensional facial image acquisition module, a three-dimensional image recognition module, a metal item detection module, a mobile terminal information acquisition module, an alarm module, a center end database, and a rear end comprehensive control platform. The identity card reader module, the two-dimensional facial image acquisition module, the three-dimensional image recognition module, the metal item detection module, the mobile terminal information acquisition module and the alarm module are connected with the industrial personal computer separately. According to the invention, the speed and accuracy of recognition are improved by the use of three-dimensional facial image recognition and comparison technology, and meanwhile, the data storage and analysis can be performed through the background to conduct alarm processing for key roles.

The invention discloses a three-dimensional face detection and identification technology. The major technical process comprises: first of all, carrying out three-dimensional face detection to obtain data which is obtained by use of a 3D imaging technology such as structured light or stereo visual sense and the like, secondly, carrying out three-dimensional face cutting and data preprocessing which comprise face detection, cutting, denoising and the like, then, carrying out posture correction on a three-dimensional face and performing characteristic extraction on the data, and finally, carrying out three-dimensional face identification to construct a proper classifier for determining the face data. 3D face identification research can be traced back to as early as early 90s of last century, due to restrictions of various factors, such as computing level restrictions of computers, immature 3D acquisition devices and the like, no great breakthroughs are made in correlation research. During the last decade, along with rapid development of a computer technology and the 3D imaging technology and development bottlenecks of a 2D face identification technology as well, the academic society gives more attention to the research on a 3D face identification technology.

The invention discloses a method, a device, and an apparatus for three-dimensional face reconstruction, and a compute readable storage medium. The method comprises: marking feature points of a face reference image in a face reference image set, performing global contour deformation on a reference model based on pixel coordinates of the feature points, and performing photometric normal reconstruction on a deformed primary face model by a photometric stereo technology based on a normal, based on a surface normal vector of the reconstructed target face model, using a mesh deforming technology toiterate to generate a target face model by the optimized normal. The invention provides a robust three-dimensional face reconstruction technology based on the photometric normal, with help of unrestrained image sets and reference models, and combined with the photometric stereo technology and a mesh deformation technology, restriction on face reference images is reduced, and advantages of the twotechnologies are used. The method prevents defects of using one technology alone to reconstruct faces, improves quality of three-dimensional face reconstruction, and realizes robust three-dimensionalface reconstruction with high accuracy.

The invention provides a face recognition system and method for an ATM. The face recognition system comprises a user information database, an image collection unit, a face testing unit and a face recognition unit. The user information database is used for storing identity information, face images and characteristic vector sets of users; the image collection unit is used for collecting the face images of the users; the face testing unit is used for testing the face images and filtering non-face regions away automatically to obtain face regions; the face recognition unit is used for analyzing the face regions, obtaining face characteristic vector and comparing the face characteristic vector with the characteristic vector sets in the user information database and obtaining corresponding identity information. The face recognition system is a three-dimensional face recognition system which is based on PCA global features and SIFT partial description. With the face recognition method, the recognition accuracy rate is high, environment influencing factors are removed, and the problems that a credit card which is left in the ATM is used by a follower is solved due to the fact that an infrared induction unit is arranged and the system recognizes the face as long as one person comes close to the ATM.