1284 results about "Face model" patented technology

A face feature analysis which begins by generating multiple face feature candidates, e.g., eyes and nose positions, using an isolated frame face analysis. Then, a nostril tracking window is defined around a nose candidate and tests are applied to the pixels therein based on percentages of skin color area pixels and nostril area pixels to determine whether the nose candidate represents an actual nose. Once actual nostrils are identified, size, separation and contiguity of the actual nostrils is determined by projecting the nostril pixels within the nostril tracking window. A mouth window is defined around the mouth region and mouth detail analysis is then applied to the pixels within the mouth window to identify inner mouth and teeth pixels and therefrom generate an inner mouth contour. The nostril position and inner mouth contour are used to generate a synthetic model head. A direct comparison is made between the inner mouth contour generated and that of a synthetic model head and the synthetic model head is adjusted accordingly. Vector quantization algorithms may be used to develop a codebook of face model parameters to improve processing efficiency. The face feature analysis is suitable regardless of noise, illumination variations, head tilt, scale variations and nostril shape.

A face avatar generating apparatus includes: a face feature information extraction unit for receiving a face photo and extracting face feature information from the face photo and a two-dimensional (2D) avatar generation unit for selecting at least one region from the face photo based on the face feature information, and exaggerating or beautifying the selected region to create a 2D avatar image. The apparatus further includes a 3D avatar generation unit for modifying a standard 3D face model through a comparison with the standard 3D model based on the face feature information and pre-stored standard information to create a 3D avatar image.

An apparatus and method for image-based 3D photorealistic head modeling are provided. The method for creating a 3D photorealistic head model includes: detecting frontal and profile features in input frontal and profile images; generating a 3D head model by fitting a 3D genetic model using the detected facial features; generating a realistic texture from the input frontal and profile images; and mapping the texture onto the 3D head model. In the apparatus and method, data obtained using a relatively cheap device, such as a digital camera, can be processed in an automated manner, and satisfactory results can be obtained even from imperfect input data. In other words, facial features can be extracted in an automated manner, and a robust “human-quality” face analysis algorithm is used.

Face recognition of a face, to determine whether the face correlates with an enrolled face, may include generating a personalized three-dimensional (3D) face model based on a two-dimensional (2D) input image of the face, acquiring 3D shape information and a normalized 2D input image of the face based on the personalized 3D face model, generating feature information based on the 3D shape information and pixel color values of the normalized 2D input image, and comparing the feature information with feature information associated with the enrolled face. The feature information may include first and second feature information generated based on applying first and second deep neural network models to the pixel color values of the normalized 2D input image and the 3D shape information, respectively. The personalized 3D face model may be generated based on transforming a generic 3D face model based on landmarks detected in the 2D input image.

The invention discloses a binocular stereo vision based intelligent three-dimensional human face rebuilding method and a system; the method comprises: preprocessing operations including image normalization, brightness normalization and image correction are carried out to a human face image; a human face area in the human face image which is preprocessed is obtained and human face characteristic points are extracted; the object is rebuilt by projection matrix, so as to obtain internal and external parameters of a vidicon; based on the human face characteristic points, gray level cross-correlation matching operators are expanded to color information, and a parallax image generated by stereo matching is calculated according to information including polar line restraining, human face area restraining and human face geometric conditions; a three-dimensional coordinate of a human face spatial hashing point cloud is calculated according to the vidicon calibration result and the parallax image generated by stereo matching, so as to generate a three-dimensional human face model. By adopting the steps, more smooth and vivid three-dimensional human face model is rebuilt in the invention.

A novel method and system for 3d-aided-2D face recognition under large pose and illumination variations is disclosed. The method and system includes enrolling a face of a subject into a gallery database using raw 3D data. The method also includes verifying and / or identifying a target face form data produced by a 2D imagining or scanning device. A statistically derived annotated face model is fitted using a subdivision-based deformable model framework to the raw 3D data. The annotated face model is capable of being smoothly deformed into any face so it acts as a universal facial template. During authentication or identification, only a single 2D image is required. The subject specific fitted annotated face model from the gallery is used to lift a texture of a face from a 2D probe image, and a bidirectional relighting algorithm is employed to change the illumination of the gallery texture to match that of the probe. Then, the relit texture is compared to the gallery texture using a view-dependent complex wavelet structural similarity index metric.

The present invention is a system and method for modeling faces from images captured from a single or a plurality of image capturing systems at different times. The method first determines the demographics of the person being imaged. This demographic classification is then used to select an approximate three dimensional face model from a set of models. Using this initial model and properties of camera projection, the model is adjusted leading to a more accurate face model.

The invention discloses a three-dimensional virtual human head image generation method, and a method and a device of human head image motion tracking, and belongs to the field of image processing. The three-dimensional virtual human head image generation method comprises collecting a human face picture, obtaining feature points on the human face picture, wherein the feature points are used for showing a left eyebrow, a right eyebrow, a left eye, a right eye, a nose, the edge contour of a human face and a forehead portion of the human face; obtaining a personalized three-dimensional human face model according to the obtained feature points and a preset standard human face model; and carrying out texture mapping to the personalized three-dimensional human face model, and generating a three-dimensional virtual human head image. Due to the facts that the feature points which are used for characterizing feature information of the human face on a single human face picture are obtained, and are applied in the three-dimensional human face model, and the personalized three-dimensional human face model which includes the forehead portion and is good in third dimension is generated, the three-dimensional virtual human head image generation method, and the method and the device of the human head image motion tracking have the advantages of being in no need of manual marking of the feature points, greatly reducing manual processing amount, being applicable to modeling occasions which require for simpleness and speediness, and being capable of enabling the third dimension of the generated three-dimensional human head image to be greatly improved and to be high in accuracy due to the fact that the feature points include feature points of the forehead portion of the human face.

The present invention aims at realizing displaying of a realistic image of a conversation scene in which a speaker can be visually recognized by people watching this image regardless of the content of the image. To this end, according to the present invention, a two-dimensional or three-dimensional face model is deformed, and animations A1i through A3i which express a state in which a person is speaking are consequently created and displayed as auxiliary images.

A 3D face model construction method is disclosed herein, which includes a training step and a face model reconstruction step. In the training step, a neutral shape model is built from multiple training faces, and a manifold-based approach is proposed for processing 3D expression deformation data of training faces in 2D manifold space. In the face model reconstruction step, first, a 2D face image is entered and a 3D face model is initialized. Then, texture, illumination and shape of the model are optimized until error converges. The present invention enables reconstruction of a 3D face model from a single face image, reducing the complexity for building the 3D face model by processing high dimensional 3D expression deformation data in a low dimensional manifold space, and removal or substituting an expression by a learned expression for the reconstructed 3D model built from the 2D image.

The invention discloses an image communication method and system based on facial expression / action recognition. In the image communication method based on facial expression recognition, firstly, an image is collected at the sending end, and the facial area in the image is determined; The facial expression feature parameters in the region are extracted; then the extracted facial expression feature parameters are sent to the receiving end; finally, the receiving end uses the facial expression feature parameters to control the local facial model of the receiving end to pass the The facial model reproduces the facial expression. The application of the present invention can not only enhance user experience, but also greatly reduce the requirement on the information transmission rate of the video bearer network, realize more effective video communication, and is especially suitable for wireless communication networks with limited network bandwidth and capacity.

A method for providing name-face / voice-role association includes determining whether a closed captioned text accompanies a video sequence, providing one of text recognition and speech to text conversion to the video sequence to generate a role-name versus actor-name list from the video sequence, extracting face boxes from the video sequence and generating face models, searching a predetermined portion of text for an entry on the role-name versus actor-name list, searching video frames for face models / voice models that correspond to the text searched by using a time code so that the video frames correspond to portions of the text where role-names are detected, assigning an equal level of certainty for each of the face models found, using lip reading to eliminate face models found that pronounce a role-name corresponding to said entry on the role-name versus actor-name list, scanning a remaining portion of text provided and updating a level of certainty for said each of the face models previously found. Once a particular face model / voice model and role-name association has reached a threshold the role-name, actor name, and particular face model / voice model is stored in a database and can be displayed by a user when the threshold for the particular face model has been reached. Thus the user can query information by entry of role-name, actor name, face model, or even words spoken by the role-name as a basis for the association. A system provides hardware and software to perform these functions.

Methods and systems for automated annotation of persons in video content are disclosed. In one embodiment, a method of identifying faces in a video includes the stages of: generating face tracks from input video streams; selecting key face images for each face track; clustering the face tracks to generate face clusters; creating face models from the face clusters; and correlating face models with a face model database. In another embodiment, a system for identifying faces in a video includes a face model database having face entries with face models and corresponding names, and a video face identifier module. In yet another embodiment, the system for identifying faces in a video can also have a face model generator.

The invention discloses a method and a device for reconstructing a three-dimensional face based on a single face image. The method comprises the following steps of: performing posture recognition on a face image by utilizing face structure priori knowledge, and estimating rotating direction and angle of a face plane by combining face measurement and projective geometry so as to set a rotating angle of the three-dimensional face; estimating the depth of two-dimensional characteristic points on the face image by adopting an artificial neural network, and acquiring three-dimensional coordinates of the characteristic points; converting a common three-dimensional face model into a specific model by adopting a Dirichlet free deformation algorithm; and mapping the three-dimensional face model by adopting characteristic points extracted from the two-dimensional image. Therefore, the true three-dimensional model is constructed through the single face image, the three-dimensional modeling accuracy is effectively improved, the three-dimensional modeling time is shortened and the three-dimensional modeling cost is reduced.

Methods and systems for automated annotation of persons in video content are disclosed. In one embodiment, a method of identifying faces in a video includes the stages of: generating face tracks from input video streams; selecting key face images for each face track; clustering the face tracks to generate face clusters; creating face models from the face clusters; and correlating face models with a face model database. In another embodiment, a system for identifying faces in a video includes a face model database having face entries with face models and corresponding names, and a video face identifier module. In yet another embodiment, the system for identifying faces in a video can also have a face model generator.

An image analysis device includes circuitry that receives one or more input images and detects a plurality of anatomical landmarks on the one or more input images using a pre-determined face model. The circuitry extracts a plurality of geometric and local texture features based on the plurality of anatomical landmarks. The circuitry selects one or more condition-specific features from the plurality of geometric and local texture features. The circuitry classifies the one or more input images into one or more conditions based on the one or more condition-specific features.

Techniques and systems that provide interactions between a 3D representation of a selected pair of glasses and a fully-textured 3D face model are disclosed. According to one embodiment, an interactive platform is displayed to allow a user to select a pair of glasses and try the selected glasses on a user-provided 3D face model. The interactions provided in the platform include spatial adjustments of the glasses around the face model, various perspective views of the 3D face with the glasses on and other cosmetic alternations to the selected glasses. According to one application, when the user finishes the try-on process, the information about the glasses can be transmitted to a business that can subsequently produce a pair of customized glasses for the user.

The invention provides an expression interaction method based on face tracking and analysis, belonging to the field of figure and image processing and computer vision. The method comprises the following steps of: collecting an expression image of a face by a camera; carrying out analysis processing on a captured face image in time according to a face tracking and expression analysis technology, so as to track the face and extract expression parameters; and then driving a target three-dimensional face model according to the extracted expression parameters to do same expression animations. The expression interaction method provided by the invention has the advantages of strong automation, robustness and interactivity and is applicable to the fields of film production, three-dimensional games, interactive multimedia and the like.

A method (100) and apparatus (700) are disclosed for detecting and tracking human faces across a sequence of video frames. Spatiotemporal segmentation is used to segment (115) the sequence of video frames into 3D segments. 2D segments are then formed from the 3D segments, with each 2D segment being associated with one 3D segment. Features are extracted (140) from the 2D segments and grouped into groups of features. For each group of features, a probability that the group of features includes human facial features is calculated (145) based on the similarity of the geometry of the group of features with the geometry of a human face model. Each group of features is also matched with a group of features in a previous 2D segment and an accumulated probability that said group of features includes human facial features is calculated (150). Each 2D segment is classified (155) as a face segment or a non-face segment based on the accumulated probability. Human faces are then tracked by finding 2D segments in subsequent frames associated with 3D segments associated with face segments.

The present invention relates to a three-dimensional face model-based face key point detection method. The method includes the following steps that: step 01, a face image and the initial parameter of a three-dimensional face model are obtained from a face training sample; step 02, a posture adaptive feature and a normalized coordinate code are generated according to the face image and the initial parameter; step 03, transformation and fusion are performed on the posture adaptive feature and the normalized coordinate code through using a convolutional neural network, so that a real residual and the parameter residual of the initial parameter are obtained; step 04, the initial parameter is updated according to the parameter residual, and the method shifts to the step 02 until the parameter residual reaches a preset threshold; and step 05, the three-dimensional face model is updated through using the parameter residual which reaches the preset threshold, and key points on the three-dimensional face model are acquired. With the method of the present invention adopted, face key point detection under a full-posture condition is realized.

The invention discloses a method for carrying out the three-dimensional human face modeling by a single human face photo to obtain a realistic human face, and the method belongs to the field of graphics and images. The method utilizes a priori knowledge of a human face structure to estimate the depths of two-dimensional feature points on the photo, thereby obtaining similar three-dimensional coordinates; the similar three-dimensional feature points are taken as control points, and the dirichlet free deformation algorithm is adopted to realize the fitting of the neutral human face to the specific human face. Then, a method which is based on the regional expansion is adopted to realize the texture synthesis and the mapping, and the region which is not covered is repaired by adopting the interpolation or the block filling method. The method has simple calculation, high modeling accuracy and can rapidly utilize a small number of feature points to realize the three-dimensional modeling of the human face on the single photo, and the generated three-dimensional human face model has very strong realistic feeling under various postures due to the accurate estimation of the depths of the feature points. The method can also be used for the three-dimensional reconstruction of other objects.

The invention discloses a high-performance human face recognition method and system. The high-performance human face recognition method includes the steps that human face detection is carried out based on multiple visual angles, and after a human face area is determined according to a human face feature point positioning algorithm, image normalization is carried to obtain a human face image at multiple visual angles after normalization; the extracted HOG feature of the normalized image is connected with a LBP feature, and the final feature is obtained; the HOG feature and the LBP feature of the human image in a sample base are extracted, trained and matched with the final feature, so that high-performance human face recognition is realized; the human face image in the sample base is a multi-visual-angle projection image of a three-dimensional human face model with illumination. By means of the high-performance human face recognition method and system, the human recognition performance is not affected by changes of the illuamition condition and the human face posture factor, the human recognition speed is increased, and calculation complexity is reduced.

The invention proposes a full-automatic modeling approach of three dimensional faces based on phase deviation scanning. The approach firstly obtains depth point cloud data of the face part of a scanned person by phase deviation scanning technology, and then registers the point cloud data by using a new self-adaptation topology non-rigid registration method to acquire a smooth face triangle mesh model with continuous and complete topology. The experimental result proves that the modeling process provided by the approach makes all of the general users obtain their own individualized three dimensional face model, and the modeling is significant to the popularization of three-dimensional facial animation in the application of computer graphics.

A system method of advertising using a multi-media application system is disclosed. The multi-media application relates to the delivery of multi-media messages using animated entities that audibly deliver messages created by a sender using text-to-speech technologies. The method provides targeted advertising based on information learned about both the sender of a multi-media message and the recipient of the multi-media message. The information may relate to an analysis of a text message created by the sender, emoticons chosen by the sender and inserted into the text of the message, the choice by the sender of an animated entity, or other parameters such as background music chosen for which template is chosen by the sender. Advertising messages may be delivered before the recipient receives the multi-media message, during the reception by the recipient of the multi-media message or following the reception of the multi-media message. A decision regarding whether to include an advertising message may be based on a text analysis or an analysis of the emoticons or other tags inserted into the text by the sender. Further, animated entities such as professionally designed face models, templates, additional emoticons, animation or sound effects may also be purchased by the sender for a limited number of multi-media messages, for limited amount of time or longer for use in creating multi-media messages. The system comprises a server to handle the reception and processing of sender multi-media messages and client software for both creating multi-media messages and receiving multi-media messages.

The present invention discloses a face replacement method and device. The face replacement method includes the following steps that: the face characteristic parameters of a first person are extracted from an image to be processed; the original face image of a second person is selected; a face model is constructed for the second person according to the original face image of the second person; and the face characteristic parameters of the first person are fitted into the face model of the second person, so that the target face image of the second person is obtained; and the face image of the first person in the image to be processed is replaced with the target face image of the second person. With the face replacement method and device provided by the embodiments of the present invention adopted, a problem that a replacement target person is not matched with an original image can be solved, and therefore, a replacement effect can be enhanced.

A system and method that includes a virtual human face generation technique which synthesizes images of a human face at a variety of poses. This is preferably accomplished using just a frontal and profile image of a specific subject. An automatic deformation technique is used to align the features of a generic 3-D graphic face model with the corresponding features of these pre-provided images of the subject. Specifically, a generic frontal face model is aligned with the frontal image and a generic profile face model is aligned with the profile image. The deformation procedure results in a single 3-D face model of the specific human face. It precisely reflects the geometric features of the specific subject. After that, subdivision spline surface construction and multi-direction texture mapping techniques are used to smooth the model and endow photometric detail to the specific 3-D geometric face model. This smoothed and texturized specific 3-D face model is then used to generate 2-D images of the subject at a variety of face poses. These synthesized face images can be used to build a set of training images that may be used to train a recognition classifier.

A system and method for automatically modeling a three dimensional object, such as a face, from a single image. The system and method according to the invention can construct one or more three dimensional (3D) face models using a single image. Although use of a frontal image simplifies the processing, the system and method according to the invention can also generate a 3D model from a non-frontal image (e.g., an image where the object to be modeled has an out of plane rotation, such a face that is turned to one side to some extent). The system is fully automatic. It is fast compared to the other modeling systems. Furthermore, it is very robust. It can be used to construct personalized models for games, on-line chat, and so on. It can also be used as a tool to generate a database of faces with various poses which are needed to train most face recognition systems.