108 results about "Target expression" patented technology

The embodiment of the invention discloses a transaction risk identification method and device, a storage medium and a program product, and relates to the field of information security and artificial intelligence. The method comprises the following steps: acquiring a face image sequence and a first voice sequence of a user within a preset time length and a behavior data sequence in a target application; performing expression recognition on each frame of facial image in the facial image sequence to obtain a target expression type sequence; performing emotion recognition on the first voice sequence to obtain a target emotion type, and performing behavior recognition on the behavior data sequence to obtain a target behavior type; and performing risk analysis on the target expression type sequence, the target emotion type and the target behavior type to obtain a transaction risk value, and determining a transaction risk level of the transaction performed by the user through the target application within the preset duration based on the transaction risk value, so that risk transactions can be identified from multiple dimensions, and the user experience is improved. Especially for risk transactions of special crowds in special risk scenes, the accuracy of transaction risk identification is improved.

The invention discloses an advertisement material intelligent optimization system and method based on knowledge extraction and knowledge fusion, and the method comprises the following steps: S1, obtaining and preprocessing a to-be-optimized advertisement material, and collecting an external knowledge source; s2, combined semantic analysis is executed, and cross-modal semantic alignment is carried out; s3, extracting entity units and corresponding semantic attributes, performing type labeling on the entity units, and establishing a semantic relation structure; s4, performing semantic fusion with an external knowledge source, calculating similarity between semantic vectors, performing entity mapping, performing normalization, and correcting conflict boundaries and attribute value divergence; s5, extracting a target expression fragment, and performing optimization according to a preset optimization rule set; and S6, verifying and detecting the optimized advertisement material, and outputting an optimization result. According to the method, deep understanding and cross-modal fusion optimization of advertisement materials can be realized, and the accuracy, attraction and propagation effect of advertisement semantic expression are improved.

The invention discloses a bionic robot facial expression synchronization method and device based on visual perception, equipment and a medium, and relates to the technical field of man-machine interaction, and the method comprises the steps: controlling a target bionic robot to execute a target expression control instruction used for achieving a preset expected robot facial expression, and capturing a robot visual facial expression of the robot; based on the robot visual facial expression and a preset expected robot facial expression, carrying out calibration on the target bionic robot so as to construct a hardware compensation parameter vector; executing a facial expression synchronization operation based on the hardware compensation parameter vector and a pre-trained target expression mapping model by asynchronously running a preset sensing thread and a preset driving thread; the preset sensing thread is used for collecting a target face image in real time and outputting a target steering engine control vector through a target expression mapping model; and the preset driving thread is used for outputting a steering engine control instruction at a preset output frequency based on a preset frame compensation algorithm, the hardware compensation parameter vector and the target steering engine control vector.

The invention discloses an expression recognition method and device, electronic equipment and a storage medium, and belongs to the technical field of artificial intelligence. The method comprises the following steps: acquiring an image pair comprising a target user face; the image pair comprises a natural expression image of the target user and a to-be-recognized image; performing fusion processing on the image pair, and inputting the image pair after fusion processing into a trained expression recognition model to obtain an expression recognition result of the to-be-recognized image generated by the expression recognition model; wherein the expression recognition model comprises an expression change extraction module and a decoder; the expression change extraction module is used for extracting target expression change features of the fused image pair; and the decoder is used for outputting an expression recognition result based on the target expression change feature. According to the embodiment of the invention, inherent differences of different individuals in emotional expression are fully considered, and the problem of identification deviation caused by neglecting individual expression characteristics in a traditional expression identification method is reduced, so that the accuracy of expression identification is improved.

The application provides a high-efficiency expression migration method using base expression space transformation, comprising a preprocessing stage and a facial expression migration stage; the preprocessing stage comprises: obtaining a source model O, the source model O being a base expression model comprising expression meshes under multiple different expressions; performing facial expression reconstruction on the source model O and a target model T to obtain expression description parameters of the two; performing similarity estimation according to the expression description parameters to obtain an expression parameter conversion matrix and a similarity matrix; the facial expression migration stage comprises: inputting a character picture into the base expression model, calculating expression description parameters of a target expression model according to the expression description parameters in the base expression model and the similarity matrix, and completing expression migration.

The present disclosure relates to an expression generation method and device, and a training method and device of an expression generation model, and relates to the technical field of computers. The method of the present disclosure comprises: acquiring feature information of each frame of image in an original video, feature information of a face key point, and classification information of an original expression; fusing the feature information of each frame of image, the feature information of the face key point, and the classification information of the original expression with preset classification information corresponding to a target expression to obtain feature information of a fused image corresponding to each frame of image; and generating a fused image corresponding to each frame of image according to the feature information of the fused image corresponding to each frame of image to obtain a target video in which a face expression formed by the fused images corresponding to all images is the target expression.

The present disclosure provides a landmark feature determination method and device, electronic equipment and storage medium. The landmark feature determination method comprises: obtaining target expression data, wherein the target expression data is used to represent the expression amount of each candidate feature in different cells; the average expression amount and expression proportion of each candidate feature in different cell types are counted; for each candidate feature, the first inter-class comparison data is determined according to the average expression amount of the candidate feature in the target cell type and the average expression amount of the candidate feature in the non-target cell type; for each candidate feature, the second inter-class comparison data is determined according to the expression proportion of the candidate feature in the target cell type and the expression proportion of the candidate feature in the non-target cell type; and the landmark feature of the target cell type is determined according to the first inter-class comparison data and the second inter-class comparison data. The embodiment of the present application can solve the problem of landmark feature specificity.

This invention relates to an automatic base expression selection method based on local hybrid optimization, belonging to the field of computer and automation technology. The method includes: acquiring a sequence of facial expression models with the same topology; segmenting each facial expression model into multiple sub-regions; fitting each sub-region of the base expression in the base expression group to the corresponding sub-region of each target expression model; obtaining a local facial expression model corresponding to each target expression model; fusing the obtained local facial expression models to obtain a complete facial expression model corresponding to each target expression model; comparing each complete facial expression model with its corresponding target expression model, identifying the target expression model with the largest error compared to the corresponding complete facial expression model, and adding this target expression model as the base expression to the base expression group; obtaining the base expression group when the rate of change of the largest error is less than 0.1.

The invention provides a manufacturing decision system-oriented modular functional architecture and a collaborative mechanism thereof, and the structural organization and reusable collaboration of a manufacturing decision process are realized by constructing a multi-layer capability system, a functional module system and a standardized behavior interface. The architecture comprises a data interface module, a decision-making target module, a disturbance response module and a fusion decision-making module, and defines a data flow, a control flow and a trigger relationship among the modules. The system completes target expression, constraint analysis, disturbance identification and collaborative decision generation through standardized access of multi-source data such as orders, productivity, materials and processes, and forms an interpretable and executable decision link. According to the invention, modularization, interpretability and intelligent cooperation of the manufacturing decision-making system are realized, and multi-scene and multi-industry manufacturing decision-making application is supported.

Embodiments of the present application provide a face expression migration method and an expression migration device, an electronic device and a storage medium, and belong to the technical field of artificial intelligence. The method comprises: obtaining an original face image of a target object and a target expression category; determining a first training set according to the target object, the first training set comprising a plurality of first face sample images; determining a second training set according to the target expression category, the second training set comprising a plurality of second face sample images; inputting the second face sample images into a preset initial recurrent probability model for pre-training to obtain a pre-training model; inputting the first face sample images and the second face sample images into the pre-training model to obtain a first face processing image, and then obtaining a face expression migration model; and inputting the original face image into the face expression migration model to convert the original face image into a target face image matching the target expression category. The embodiments of the present application can efficiently and accurately realize migration of a face expression in an image.

The invention discloses a kinematics semi-analytical modeling method and terminal for a human wrist-simulated parallel mechanism, and the method comprises the steps: constructing a global coordinate system, a first coordinate system and a second coordinate system when the wrist parallel mechanism is subjected to kinematics positive solution, and then obtaining a target D-H expression corresponding to the tail end pose of the human wrist-simulated parallel mechanism, a target D-H expression is obtained, the target D-H expression shows the relation between the tail end pose and a first joint angle and the relation between the tail end pose and a second joint angle, then a target modular length expression is obtained, the target modular length expression shows the relation between a first modular length and the first joint angle and the relation between a second modular length and the second joint angle, and finally, the target D-H expression shows the relation between the tail end pose and the second joint angle. And obtaining numerical values of a first modulus length and a second modulus length, and substituting the numerical values of the first modulus length and the second modulus length into the target D-H expression and the target modulus length expression to obtain a tail end pose. The invention provides a solution capable of completing operation on a low-computing-power platform for a parallel mechanism simulating a human wrist.

The application discloses a three-dimensional face generation method and device, a three-dimensional face reenactment method and device, wherein the generation method comprises the following steps: acquiring an expression vector of a first image containing a first identity object based on a target expression coding network model; the first image is an image with a face having an occlusion; inputting the expression vector and a second image containing a second identity object into a target face generation network model to determine a target image of migrating the expression vector of the first image to a face of the second identity object in the second image; and enabling the face region of the first identity object in the first image to be identified as the expression vector even in the case of having the occlusion and to be migrated to the face of the second identity object in the second image, so that the accuracy of face expression migration is improved, and the robustness of face expression migration in the case of having the occlusion is improved.

The invention discloses an information recommendation method, electronic equipment and a computer readable storage medium. The method comprises the following steps: in response to a received first data request, based on environmental parameters and position transformation information of a target object, controlling expression acquisition equipment to acquire the target object to view a target expression image of current recommendation information; performing feature extraction on the target expression image to obtain expression features of multiple dimensions; analyzing the expression of the target object for viewing the current recommendation information based on the expression features of the multiple dimensions to obtain an expression analysis result; in response to a received second data request sent by the target client, obtaining object behavior data of the target object for the current recommendation information; and adjusting the current recommendation information based on the expression analysis result and the object behavior data to obtain target recommendation information. According to the invention, the technical problem of poor information recommendation effect in related technologies is solved.

The application discloses a face expression recognition method and device, equipment and storage medium, relates to the artificial intelligence technical field, and includes: in the process of utilizing a target expression recognition model to perform face expression recognition on video frames of a target face video, for any video frame at any time, extracting each face key point, and generating a spatial heat map based on each face key point, and generating a spatial feature vector based on the spatial heat map and the image features of the video frame; determining the spatial feature vector difference between the spatial feature vector of the video frame at the current time and the spatial feature vector of the video frame at the previous time, and determining the first hidden state corresponding to the spatial feature vector of the video frame at the current time and the second hidden state corresponding to the spatial feature vector difference; determining the fusion features based on the first hidden state and the second hidden state, and determining the face expression recognition result of the video frame at the current time based on the fusion features. The application can improve the stability and accuracy of dynamic face expression recognition.

Embodiments of the present application provide a method and device for generating a dynamic image based on audio, an apparatus, and a storage medium, relating to the field of natural human-computer interaction. The method comprises: first obtaining a reference image and a reference audio input by a user; then, based on the reference image and a trained generation network model, determining a target head action feature and a target expression coefficient feature, and adjusting the trained generation network model based on the target head action feature and the target expression coefficient feature to obtain a target generation network model; finally, based on the reference audio, the reference image, and the target generation network model, processing a to-be-processed image to obtain a target dynamic image; wherein the to-be-processed image is the same as an image object in the reference image; in this way, a corresponding digital person can be obtained based on a single picture of a target person; in this way, video acquisition work and data cleaning work are not required, the production cost of the digital person can be reduced, and the production cycle of the digital person is shortened.

The invention discloses an object-driven model reconstruction method and device, a storage medium and electronic equipment. The method comprises the following steps: acquiring a first driving information set matched with a first object driving model; a first driving node set and a second driving node set are obtained from the first driving information set, the first driving node set is used for indicating a preset driving relation provided by the first object driving tool, and the second driving node set is used for indicating a self-defined driving relation included in the first object driving model; generating a driving code set according to the first driving node set, and generating a target expression set according to the second driving node set; and determining a second driving information set matched with a second object driving model according to the driving code set and the target expression set, wherein the second object driving model is used for driving the target virtual object according to a control instruction received in a second object driving tool. The method solves the technical problem of low efficiency of an object-driven model creation method.

The application discloses a kind of kinematics semi-analytical modeling method and terminal of humanoid wrist parallel mechanism, method includes: when kinematics positive solution is carried out to wrist parallel mechanism, global coordinate system, first coordinate system and second coordinate system are constructed, then the end pose of the parallel mechanism of the humanoid wrist corresponding to target D H Expression, the target D H Expression shows the relationship between the end pose and first joint angle, second joint angle, then target module length expression is acquired, the target module length expression shows the relationship between first module length and second module length and the first joint angle and the second joint angle, finally, the numerical value of first module length and second module length is acquired, the numerical value of the first module length and the second module length is brought into the target D H Expression and the target module length expression, to acquire end pose.The application provides a solution for humanoid wrist parallel mechanism, which can be operated on a low-power platform.​​​

The application discloses a few-shot remote sensing image semantic segmentation method based on a double-branch reinforcement network. First, a backbone network is built to extract middle and high-level support features and query features from support images and query images respectively; second, a prototype reinforcement module based on feature customization and activation reconstruction is constructed, the middle-level support features and the query features are input into the module to obtain reinforced prototype features; then, a reinforcement module based on multi-level guidance is proposed, the high-level support features and the query features are input into the module to obtain a multi-level guidance map; finally, an encoder is designed to fuse the reinforced prototype features, the high-level query features and the multi-level guidance map to obtain a final segmentation result. The prototype reinforcement module designed by the application can deeply depict semantic features of target categories and improve target expression capability; meanwhile, the multi-level guidance reinforcement module proposed by the application can capture correlations between different levels of images, enhance the overall framework through unbiased knowledge and obtain good segmentation performance.

The present disclosure relates to a user expression-based information pushing method, device and electronic equipment, and belongs to the technical field of big data. The user expression-based information pushing method comprises: sending a pushing message to a target client; obtaining target user corresponding to the target client, expression information and operation data of the target user when the pushing message is displayed; identifying a target emotion type of the target user according to the expression information, and generating a first feedback quantization value of the pushing information based on the target expression type and the operation data; filtering candidate pushing information based on the first feedback quantization value to update a pushing information set corresponding to the target user; and selecting new pushing information for the target user from the pushing information set and returning to execute the step of sending the pushing message to the target client and subsequent steps. Thus, the present disclosure can adjust the pushing message in real time and continuously improve the mechanism of the pushing information by obtaining the user expression information and operation data in real time and analyzing the real-time user expression information and operation data, so that the pushing information is no longer limited to the user's behavior habits, the information content that can make the user happy can be pushed to the greatest extent, the accuracy and efficiency of sending the pushing message are improved, and the user experience is further improved.

The embodiment of the invention provides a special effect image generation method and device, electronic equipment and a storage medium, and the method comprises the steps: obtaining fusion deformation data and an initial face image, and enabling the fusion deformation data to comprise at least one deformation element forming a target expression; the initial face image comprises a first face element of which the element type is the same as that of the deformation element; the initial face image is processed based on the fusion deformation data, a corrected face image is generated, the corrected face image comprises second face elements, and the second face elements are matched with element contours of corresponding deformation elements in the fusion deformation data; and generating a special effect expression image with the target expression based on the corrected facial image and the fused deformation data. Before the deformation data and the face image are merged and fused, the initial face image is optimized into the corrected face image, so that the element contours of the second face element and the deformation element are matched, the face element is prevented from being covered, and the visual expressive force of the expression special effect is improved.

The invention discloses a robot facial expression generation control method based on a multi-particle swarm optimization algorithm, and relates to the technical field of robot control. According to the method, motor parameters corresponding to facial expressions of a robot are abstracted into particles, populations and parameters are initialized, expressions are generated through physical mapping, and an RGB camera captures and processes images; the method comprises the following steps: firstly, obtaining a Pareto solution set, then respectively calculating expression accuracy, key motion unit activation degree and motion unit coordination degree by using three objective functions to obtain particle fitness, then updating the Pareto solution set and particle information, finally judging whether an end condition is met, and carrying out loop iteration until a natural and vivid target expression is generated. Manual calibration is not needed, the automation degree is high, the universality is good, computing resources can be saved, and the generated expressions conform to the human face movement rule and are vivid and real.

This application provides a model training method, apparatus, electronic device, and storage medium based on balanced samples. The method calculates accumulated coefficients based on the same type of expression coefficients from multiple training samples, and constructs an accumulated vector from these coefficients. A target accumulated coefficient is determined based on the distribution of the accumulated coefficients in the accumulated vector. A portion of the target accumulated coefficient representing the target expression coefficient is assigned a first weight, and the remaining expression coefficients are assigned a second weight, resulting in a weight matrix composed of the first and second weights. The model is adjusted and trained further under the guidance of a loss function based on the weight matrix until preset requirements are met. In this scheme, the distribution of expression coefficients in the training samples is used to assign different weights to the expression coefficients, and training is performed under the guidance of a weighted loss function. This ensures that the training is based on information from uniform expression coefficients in the samples, optimizing the model training effect based on samples that conform to real-world scenarios.

The present invention discloses a real-time anime avatar driving method, belonging to the field of image processing technology. The method comprises: obtaining a real-time user image; performing a 3DMM model solution on the real-time user image to obtain a target expression vector and a target head posture vector; semantically mapping the target expression vector to generate an expression change partial graph; and identically mapping the target head posture vector to the expression change partial graph to generate a three-dimensional reconstructed image, thereby driving the avatar. The present invention also discloses a real-time anime avatar driving system. The present invention can mass-produce high-quality datasets for anime avatar driving. Generating datasets using this method can implement a data-driven anime character avatar driving method. The present invention can also achieve lightweight mobile avatar driving.

The invention discloses a graphical interface program design method capable of dynamically simulating, which comprises the following steps: designing a set of data flow grammar normal forms, the data flow is an expression for expressing an attribute logic relationship between controls, and the expression comprises a source expression and a target expression; based on the grammar normal form, data streams are designed according to requirements in the interface design process, the updating sequence of the data streams is calculated according to the interdependence relation, and a design result is stored in a design file; and carrying out dynamic simulation based on a design result, and sequentially and circularly executing the data streams or updating controls according to an updating sequence of the data streams, so as to realize simulation operation. According to the method, the problem that the design effect cannot be visually and quickly checked when a complex interface is designed is solved.

The embodiment of the invention provides an image editing method and electronic equipment. In the scheme, the electronic equipment can edit the expression of the face object in the target image through the expression of the face object in the real-time preview image collected by the camera, or edit the expression of the face object in the target image according to the expression in the target expression template selected by the user. According to the scheme, the expression of the face object in the image can be edited through simple operation, so that the expression of the face object in the edited image is more natural, user requirements are met, the editing time is shortened, the operation threshold is reduced, the expression editing efficiency of the image is improved, and finally the user experience is improved.

The invention provides a text processing method and device, an interaction method and device, equipment, a medium and a program product. The method can be applied to the technical fields of artificial intelligence and data processing. The method comprises the following steps: determining a plurality of to-be-processed texts from a plurality of pieces of dialogue information related to the same service; based on a field identifier included in the target expression, performing field extraction on the to-be-processed text to obtain a target field matched with field identifier semantics; filling the target field into the target expression according to the field identifier to obtain a structured text of the to-be-processed text; based on respective feature vectors of the plurality of structured texts, the plurality of to-be-processed texts are clustered to obtain a clustered text set, and the feature vectors are obtained by encoding the structured texts; and rewriting the clustered text set to obtain a target text.

The invention discloses a logical operation processing method and device for SQL statements, equipment, a medium and a product. The method comprises the steps of obtaining an original expression corresponding to an SQL statement; the original expression comprises logic and AND operators and AND connection items connected with the AND operators; the AND connection items are stored in an AND connection item chain table; determining public sub-expressions in the AND connection item chain table, storing the public sub-expressions in a public sub-expression chain table after determining each public sub-expression, and removing the sub-expressions in the AND connection item chain table according to the expression of the AND connection item and the public sub-expressions; the target sub-expression is formed according to the remaining sub-expressions in the AND connection necklace chain table, the common sub-expressions in the common sub-expression chain table and the target sub-expression are connected through logic or OR operators, the target expression corresponding to the SQL statement is obtained, screening conditions can be simplified, and the SQL statement query efficiency is improved.

The application discloses a face image generation method, device and equipment and a storage medium, and belongs to the technical field of computers. The method comprises the following steps: acquiring a face image of a first face, acquiring a face image of a second face, and acquiring face vertex data of multiple faces with different face shapes and different expressions; taking the first face as a fitting target, fitting the face vertex data of the multiple faces to determine target face shape features and target expression features of the first face; taking the second face as a fitting target, fitting the face vertex data of the multiple faces to determine target face shape features and target expression features of the second face; and generating a target face image of the second face based on the target expression features of the first face, the target face shape features of the second face, and target face texture features of the second face. The method reduces the time cost and labor cost.

The present application relates to the field of image data processing, and particularly relates to an expression generation method and device, electronic equipment and storage medium, the generation method comprising: acquiring first expression image samples and second expression image samples containing the same object and different object expressions, obtaining transfer data between the first expression image samples and the second expression image samples through the constructed expression generation model, obtaining first driving expression images and second driving expression images of the first expression image samples and the second expression image samples respectively according to the transfer data, completing the training of the expression generation model, and obtaining a target expression image according to a target basic image and a target expression driving image by using the trained expression production model. Compared with the prior art, the present application makes the first expression image samples and the second expression image samples serve as references for each other, enables the expression generation model to learn sufficiently, and further enables the expression information in the images to be effectively utilized to generate a target expression image corresponding to the expression.

This application relates to the field of data processing and provides a data processing method. The method ensures the comparability of multiple AI-generated content variants by screening candidate content containing the same technical feature words and performing correlation analysis. Through semantic alignment and extraction of target expressions, it unifies the expression forms of the same technical meaning in different AI-generated content variants. By classifying and quantifying the target expressions, it improves the accuracy of tendency type identification. By extracting statistical features and comparing them with knowledge constraints to generate candidate restoration values, the restoration results conform to statistical laws and do not violate domain common sense. Finally, by comprehensively considering the characteristics of propagation nodes and cross-difference consistency, it performs credibility assessment and sorts the restoration results for output. The processing provided by this method can reverse-engineer the original true information from multiple AI-generated content variants distributed across different propagation nodes through statistical laws and domain knowledge constraints.