70 results about "Emotion intensity" patented technology

The invention discloses a psychological accompanying method based on multi-modal emotion recognition, and belongs to the technical field of psychological health services, and the method comprises the steps: S1, synchronously collecting physiological signals, voice features, facial expressions and interactive behavior data of a user through a multi-modal sensor; s2, performing fusion analysis on the multi-modal data by using a deep learning model, and identifying a current emotional state and an emotional intensity level of the user; and S3, dynamically generating an adaptive psychological accompanying intervention scheme based on a preset emotion-intervention strategy mapping rule in combination with historical emotion data and personalized preferences of the user. According to the method, the physiological signals, the voice features, the facial expressions and the interactive behavior data are synchronously collected through the multi-modal sensor, the deep learning model is used for fusion analysis, and compared with single-modal recognition, the emotion state and the intensity level of the user can be judged more comprehensively and accurately, the emotion misjudgment risk is reduced, and a reliable basis is provided for subsequent intervention.

The invention belongs to the technical field of brain-computer interfaces, and particularly relates to a brain-computer interface driven emotion recognition and self-adaptive regulation and control system which comprises a multichannel nerve-peripheral coupling module, an emotion intensity probability mapping module and a closed-loop nerve regulation and control current module. The multi-channel nerve-peripheral coupling module is used for realizing overall quantification of central and peripheral emotional physiology; the emotion intensity probability mapping module is used for generating continuous emotion probabilities ranging from 0 to 1 through normalization and nonlinear mapping by utilizing emotion energy and combining eye movement fatigue and electroencephalogram entropy; and the closed-loop nerve regulation and control current module is used for dynamically adjusting the transcranial electrical stimulation intensity within the safety current upper limit according to the difference value between the emotion probability and the expected target. According to the invention, the recognition precision, the response speed and the use comfort are obviously improved.

The invention relates to the technical field of robot control, and discloses a spiking neural network-based robot control method, which comprises the following steps of: preprocessing collected multi-modal data to obtain an emotion pulse signal; inputting the emotion pulse signal into a pre-constructed emotion pulse neural network, and outputting a comprehensive emotion pulse; inputting the comprehensive emotion pulse into a central pattern generator, and outputting a behavior rhythm; acquiring an environment feedback signal generated by executing the behavior rhythm, and adjusting a connection weight according to the environment feedback signal; optimizing the comprehensive emotion pulse according to the connection weight, and converting the optimized comprehensive emotion pulse into emotion interaction voice information; and adjusting the emotion intensity according to the optimized comprehensive emotion pulse and the multi-modal data. According to the method, data are collected through the multi-mode sensor to generate emotion pulses, the emotion pulses are input into the central mode generator to generate behavior rhythms after SNN processing, weight optimization, emotional speech generation and emotional steady-state control setting are combined with the STDP algorithm, and the efficiency of a robot service scene is improved.

The invention, which relates to the field of the intelligent accompanying system, discloses an interactive digital person accompanying system for the old with cognitive impairment, comprising a data acquisition module, an emotion recognition module, a first matching module, a second matching module, a personalized interaction generation module and a reinforcement learning adaptive module. The data acquisition module acquires multi-modal sensing data of a target old person; the emotion recognition module outputs an emotion label and an intensity value through feature extraction and Transform fusion; a first matching module screens corpus fragments according to emotion intensity triggering conditions; the second matching module performs secondary screening based on the retrieval weight; the personalized interaction generation module generates digital twin interaction data through probability distribution sampling; the reinforcement learning adaptive module dynamically adjusts the corpus weight according to the interaction reward value; according to the method, the dual guarantee of emotion guarding and physiological safety is realized, the anxiety emotion of the old with cognitive impairment is remarkably relieved, and the novelty of interaction and high emotion relieving efficiency can be kept.

The invention discloses a campus psychological assessment multi-mode emotion recognition and privacy protection method and system, and the method comprises the steps: collecting physiological signal data, voice signal data and facial expression data in a non-contact manner, carrying out the preprocessing of the collected data, extracting the feature vector of the signal data, and carrying out the recognition of the feature vector of the signal data; the method comprises the following steps of: inputting a modal-invariant basic model to carry out multi-modal fusion, then inputting the modal-invariant basic model into a lightweight multi-modal emotion recognition model to carry out emotion recognition, generating a visual report of a user emotion state and emotion intensity according to an emotion recognition result, calculating a DASS-21 index according to the visual report, generating a standard evaluation scale, and providing emotion guidance. According to the method, the physiological signals, the voice signals and the facial expression data are fused, the emotion features are captured from multiple angles, the emotion recognition accuracy is improved, the psychological state can be more comprehensively described through the multi-modal fusion mode, and the root of the psychological problem can be more accurately positioned in an auxiliary mode.

The invention relates to an emotional infection method based on parameterized reinforcement learning, and the method comprises the steps: obtaining a real crowd video with a social identity attribute, and carrying out the recognition processing of the real crowd video, so as to obtain all groups forming a real crowd and the social identity of each individual; calculating the emotion intensity of each individual and behavior weights of behaviors corresponding to the individual under different emotion intensities on the basis of the influence of an emergency event source in the surrounding environment and the emotion influence of surrounding neighbor individuals on the individual; and obtaining evacuation behaviors of each individual under different emotion intensities and environment conditions based on the same reward function and each behavior weight of all individuals. Thus, the reality that individual emotions are affected by emergency event sources in the environment where the individual emotions are located and surrounding neighbor individual emotions is considered, the dynamic adaptability of the individual to the crowd environment where the individual emotions are located is achieved, complex behavior decisions of the individual under emotion driving are fully captured, and the method adapts to complex scene changes of high-density crowds.

The invention discloses an emotion recognition model training method, an emotion recognition method, equipment and a storage medium. The emotion recognition model training method comprises the following steps: acquiring a predicted emotion category output by an emotion category component in an emotion recognition model for a physiological signal sample; comparing the predicted emotion category with an emotion category label to obtain a category label comparison result; when it is detected that the category label comparison result is that categories are matched, obtaining predicted emotion intensity output by an emotion intensity component in the emotion recognition model for the physiological signal sample, and comparing the predicted emotion intensity with an emotion intensity label to obtain an intensity label matching result; and training the emotion intensity component according to an intensity label matching result. Therefore, interference of wrong emotion classification samples on emotion intensity learning can be effectively avoided, it is ensured that intensity estimation is carried out in an accurate category context, and the precision and reliability of intensity recognition are further improved.

The invention discloses an interactive humanoid robot expression simulation method based on artificial intelligence, and relates to the technical field of expression simulation, and the method comprises the steps: collecting a brain wave signal of a user, carrying out the preprocessing, and generating a power spectrum density feature; inputting the power spectral density features into a pre-trained emotion classification model, and outputting an emotion category and an emotion intensity value of the user; performing emotional expression mapping on the emotional category and the emotional intensity value, and outputting a bionic muscle contraction instruction; performing facial adjustment on facial muscle fibers of the humanoid robot according to the bionic muscle contraction instruction, dynamically adjusting surface texture details of the humanoid robot, and generating a simulated micro expression; and capturing user face feedback data through camera shooting eyeballs, evaluating the acceptance degree of the user on the simulated micro-expression based on the user face feedback data, and outputting an expression optimization strategy. According to the method, through deformation matching and expression optimization double-closed-loop modeling driven by user feedback, biological simulation of micro-expressions of the humanoid robot is realized.

The invention provides a group psychological data fluctuation early warning method based on VR emotion data processing, which comprises the following steps: acquiring multi-modal physiological signals such as electroencephalogram, heart rate variability and skin conductance of a user and behavior data such as voice, expression and virtual trajectory in real time, performing sliding window processing and feature extraction, fusing into multi-dimensional features, inputting the multi-dimensional features into an emotion recognition model, and performing emotion recognition on the emotion recognition model; generating an individual emotion dynamic sequence; a dynamic heterogeneous graph is constructed by combining the social relation and the emotion similarity, the emotion influence intensity between nodes is judged by using a dual-channel graph attention mechanism, and a propagation path is traced through an integral gradient method, so that interpretable group emotion diffusion mode recognition is realized; according to the method, a typical propagation mode and a time sequence convolutional network are combined, a group emotion intensity evolution trend is predicted, when fluctuation exceeds a threshold value, an emotion propagation path thermodynamic diagram is automatically generated and early warning is pushed, and the interpretability of an emotion propagation path and prediction and response efficiency of group psychological fluctuation are improved.

The invention discloses a personalized electroencephalogram emotion recognition method and system based on dynamic brain region division, and belongs to the technical field of electroencephalogram emotion recognition. The method comprises the steps that electroencephalogram signals are preprocessed, and an electroencephalogram signal matrix is constructed; on the basis of the preprocessed electroencephalogram signals, five-frequency-band differential entropy feature values are extracted, multi-head self-attention calculation is carried out in combination with rotation position coding, and channel feature representation with strong emotion intensity is output; on the basis of the constructed electroencephalogram signal matrix, dynamically dividing a brain function region by using a CNM algorithm, optimizing the structure of the brain function region by using a modularity value, and outputting brain region characteristics; and fusing the channel feature representation and the brain region features, predicting an emotion tag through a graph neural network, and outputting an emotion recognition result. According to the method, the individual brain function region can be dynamically divided, the function integration information of the brain region is effectively captured, and deep fusion of brain region function connection and channel emotion information is realized.

The invention provides an employee emotion index evaluation method and device based on multi-modal data. The method comprises the following steps: acquiring multi-modal related data of an employee; the related data comprises voice data of the employees, text data in an enterprise communication tool, video data captured by visual behaviors and physiological signals of the employees; carrying out feature extraction on the related data and carrying out cross-modal alignment to obtain multi-modal technical features; calculating a data quality coefficient and a scene correlation coefficient of each mode so as to calculate a final dynamic weight of each mode; fusing the multi-modal technical features based on the final dynamic weight of each modal, and performing analysis to obtain a comprehensive emotion index of the employee; the comprehensive emotional index comprises emotional tendency, emotional intensity and a pressure index. According to the method and the device, the comprehensive emotion index of the employee is obtained by comprehensively analyzing the data of the multiple modes, so that the defects that the current emotion evaluation of the employee is not comprehensive enough and low in accuracy are overcome.

The invention discloses a multi-modal driving system based on streaming big language model output, which belongs to the technical field of multi-modal interaction and comprises the following steps: a streaming big language model module receives input information and then generates a text unit sequence in a streaming manner; when each text unit is generated, a multi-dimensional emotion label sequence containing emotion types, emotion intensity values and semantic association degrees is synchronously output; the speech generation module maps the text unit sequence and the corresponding multi-dimensional emotion label sequence into speech synthesis parameters, and drives a streaming text-to-speech model to generate an emotional speech stream; the label generation module generates an expression label sequence and an action label sequence of the virtual image in real time; the emotion synchronous control module performs synchronous interpolation and coordination control by taking the time step of the text unit as a reference to generate a synchronous multi-mode driving signal; and the rendering and driving module drives the virtual image based on the signal to realize collaborative output of voice, mouth shape, expression and limb movement, and realizes synchronous presentation of multi-modal emotion expression.

The invention relates to the technical field of voice semantics, can be applied to business system platforms of financial science and technology, medical health and the like, and discloses a business complaint report generation method, device, equipment and medium, and the method comprises the following steps: obtaining business data and customer complaint voice, carrying out text conversion on the customer complaint voice to obtain a customer complaint text, and sending the customer complaint text to a server; performing character recognition on the business data and the customer complaint text to obtain business data fields and text data fields; performing template filling on the customer complaint report template according to the business data field and the text data field to obtain an initial customer complaint report; performing voice coding on the customer complaint voice to obtain coding features, and performing emotion node recognition on the coding features to obtain emotion fluctuation nodes; performing emotion intensity analysis on the emotion fluctuation node to obtain an emotion intensity score; and performing content optimization on the initial customer complaint report according to the emotion fluctuation node and the emotion intensity score to obtain a target customer complaint report. The report generation accuracy and the report generation efficiency can be improved.

The invention discloses a multimedia memoir generation method and device based on multi-dimensional emotion perception, relates to the technical field of artificial intelligence, can be applied to financial science and technology and medical health business scenarios, and comprises the steps that in the AI interaction process, multi-dimensional emotion related data expressed by user recall is collected in real time; comprehensively analyzing the multi-dimensional emotion related data, and determining a real-time emotion type and emotion intensity of the user; according to the real-time emotion type and the emotion intensity, corresponding visual feedback materials and auditory feedback materials are dynamically and adaptively generated, and an immersive interaction scene is constructed based on the visual feedback materials and the auditory feedback materials; in the immersive interaction scene, intercepting a plurality of audio and video material slices when the emotion of the user reaches a peak value; and integrating the plurality of audio and video material slices and the core content expressed by the user memory to obtain the multimedia memoir of the user. The emotion information of the user in the memory expression process can be effectively captured and fused.

The invention provides an intelligent secret state psychological care method, device and system, and the method comprises the steps: obtaining natural language text data and user behavior data of a target user, and obtaining occupational scene data from an external management system; determining a text feature vector corresponding to the natural language text data, a behavior feature vector corresponding to the user behavior data and an occupational scene feature vector corresponding to the occupational scene data; performing feature fusion on the text feature vector, the behavior feature vector and the occupational scene feature vector to obtain a target fusion vector; performing emotion recognition according to the target fusion vector, and determining a target emotion of the target user; determining an emotion intensity value of the target emotion, and performing risk grade division and intervention suggestion according to the emotion intensity value; and encrypting the privacy data according to the emotion intensity value, and storing the privacy data in a ciphertext. Accuracy and reliability of emotion recognition can be guaranteed, compliance and controllability of data use are guaranteed, and privacy background lines of students are not touched.

The application discloses an artificial intelligence customer service system based on natural language processing and concretely relates to the technical field of natural language processing, and comprises an input processing module, an intention cooperation module, an intention optimization module and an intention dynamic decision module.The input processing module is used for accepting user voice and text input and performing pretreatment, calculating context entropy values based on historical N-round conversation feature distribution and time attenuation weight;the intention cooperation module is used for outputting emotion intensity signals through a multi-modal emotion recognition model according to input information, and generating an intention correction vector in combination with a preset intention-emotion mapping matrix;the intention optimization module is used for constructing and optimizing a cooperative loss function, performing intention classification on the input of the user based on optimized model parameters, and dynamically adjusting according to context entropy and emotion analysis; and the intention dynamic decision module is used for adjusting and deciding the response strategy of the system based on the output of the input processing module, the intention cooperation module and the optimization module and according to the context of the current conversation.

In a system that provides psychological support based on dialogue with children, this technology offers support that enables information sharing (advocacy) that respects the child's wishes while appropriately understanding changes in the child's emotional state and the urgency of the situation. [Solution] The child support AI system according to the present invention comprises: dialogue history holding means for holding a history of conversations with a user; emotion analysis means for analyzing the user's emotional state based on input information included in the dialogue history; and state mark generation means for determining psychological urgency and generating a state mark based on two evaluation indicators: (i) the frequency (temporal density) of negative emotions appearing within a predetermined time period, and (ii) the emotional intensity in a single conversation. Furthermore, this system presents supporters with status markers corresponding to the determined psychological urgency, and also includes visualization means that, based on user permission to share, presents supporters with summarized information of the conversation content that has been permitted to be shared.

This invention discloses a method, device, and electronic device for controlling in-vehicle aromatherapy based on occupant emotion recognition. The method includes: acquiring time-series data of the target occupant's voice, physiological state, and behavior; inputting these data into a pre-trained occupant emotion recognition model to obtain the target occupant's current emotion type and corresponding emotion intensity; determining the corresponding target aromatherapy type based on the current emotion type, and determining the corresponding aromatherapy release concentration and release rate based on the emotion intensity; and adjusting and controlling the in-vehicle aromatherapy device according to the target aromatherapy type, release concentration, and release rate. This invention improves the accuracy of in-vehicle aromatherapy control and enhances user comfort, and can be applied to the field of vehicle control technology.

The invention relates to the field of artificial intelligence and man-machine conversation, and particularly discloses an emotion-enhanced historical session representation and drive response generation method, which comprises the following steps: S1, outputting a normalized historical session text sequence; s2, encoding a historical session text, and generating a text vector representation; s3, emotion recognition and coding are carried out, and emotion vector representation is converted; s4, identifying and coding a vertical field, and converting vertical field vector representation; s5, fusing the text vector, the emotion vector and the standing vector by adopting a splicing fusion mode, and outputting historical session vector representation of emotion enhancement; and S6, generating the response of the intelligent customer service, and adjusting a generation strategy according to the emotion intensity and the standing trend. According to the emotion-enhanced historical session representation and drive response generation method provided by the invention, the LLM structure does not need to be modified, the compatibility and expansibility are high, the deployment is convenient, and the pertinence of customer service response and the user satisfaction can be remarkably improved.

The invention relates to the technical field of artificial intelligence, and provides an emotion recognition method and device based on audio data and electronic equipment, and the method comprises the steps: inputting the audio data in a target service scene into an emotion recognition model, and obtaining an emotion type recognition result and an emotion intensity prediction result; the emotion recognition model is obtained by training a latest historical version model corresponding to incremental training data and an emotion label and an intensity label corresponding to the incremental training data, and the incremental training data is obtained based on a newly added target service scene; and performing emotion intensity prediction on the audio service data corresponding to the newly added target service scene by using the target number of historical version models, and screening and constructing the audio service data according to the predicted emotion intensity. The historical version model of the next version in the target number of historical version models is obtained based on incremental training of the historical version model of the previous version. According to the invention, subtle emotion changes can be caught acutely, and accurate recognition of audio emotion is realized.

The invention relates to an emotional state dynamic monitoring system and method, in particular to an emotional state dynamic monitoring system and method based on video image spatio-temporal characteristics, and solves the problems that an existing emotional state monitoring system and method are single in recognition dimension, poor in environmental adaptability, short in time sequence modeling and low in recognition efficiency. The emotion recognition precision is low, and the long-term evolution trend of the emotional state is difficult to effectively capture. The emotional state recognition module is adopted to recognize and quantify the emotional state to obtain the emotional intensity, then the time sequence modeling and trend analysis module is combined to obtain the emotional duration score and the emotional intensity trend, and the real emotional state can be comprehensively reflected; meanwhile, a spatial-temporal feature extraction module is adopted to extract spatial features and time sequence features of the face video data, and time sequence evolution characteristics of an emotional state are reflected through time sequence modeling; in addition, the weight of each frame of image in the face video data is adaptively adjusted by evaluating the environmental factors, so that the emotion recognition precision and stability can be ensured.

The invention relates to the technical field of digital data processing, in particular to a seat side customer emotion recognition method based on a large model. The recognition method comprises the following steps: for online consultation of a customer, judging whether incoming call consultation is performed within a past period of time and an incoming call text is matched with a sensitive word, and if not, continuously performing online monitoring; if yes, switching to manual service; the manual service converts the voice text into a character text; the input of the client is an input text at the t moment; based on a bright electric power large model and in combination with a cue word project, customer emotions are classified according to satisfactory, neutral and negative emotions, the satisfactory and neutral emotions are not distinguished in emotion intensity, and the negative emotions are distinguished into mild, moderate and high emotions. Through the emotion recognition result, negative emotion grade scoring is performed on the negative emotion customer, the seat personnel are assisted to quickly determine a solution, the pressure of the seat personnel is greatly relieved, and the customer experience is improved.

The invention provides a demand priority determination method and device, equipment, a storage medium and a program product, can be applied to the technical field of artificial intelligence, relates to business architecture, and can be applied to the field of financial science and technology. The method comprises the following steps: acquiring demand investigation information; on the basis of a natural language processing technology, a demand matrix is generated according to demand investigation information, and the demand matrix comprises m demand points, the initial weight of each demand point, the mention frequency corresponding to each demand point, the number of related users and semantic criticality obtained through calculation according to the initial weight, the mention frequency and the number of the related users; m is an integer greater than or equal to 1; according to the demand investigation information and m demand points in the demand matrix, utilizing an emotion analysis algorithm to calculate the emotion intensity of each demand point; calculating the comprehensive weight of each demand point according to the semantic criticality and emotion intensity of each demand point; and determining the priorities of the m demand points according to the sorting of the comprehensive weights of the m demand points. The invention further provides a method.

The invention relates to the technical field of advertisement putting, and discloses an advertisement putting method and system for a network novel platform, and the method comprises the steps: extracting chapter texts, and generating a labeling sequence and a boundary point set through recalling marking words, tenses and visual angle conversion features, recognizing a main line and recalling narrative levels; according to the narrative direction, marking entry or exit memory type labels for the boundary points; intercepting text segments before and after the boundary, calculating an emotion intensity value, generating an emotion feature pair and obtaining an emotion fall value; screening boundary points of which the fall exceeds a threshold value and of which the types are entering recall, and forming a preferable set; according to the memory narrative emotion characteristics, matching advertisements with coordinated colors and lower intensity from an advertisement library, and formulating a boundary-advertisement scheme; monitoring the reading position of the user, and triggering advertisement display when entering the target interval; according to the invention, the collaborative unification of the narrative structure breakpoint, the emotion recovery opportunity and the advertisement content emotion is realized.

This invention discloses an artificial intelligence-based method for simulating facial expressions in an interactive humanoid robot, belonging to the field of facial expression simulation technology. The method includes: acquiring and preprocessing user brainwave signals to generate power spectral density features; inputting the power spectral density features into a pre-trained emotion classification model to output the user's emotion category and intensity value; mapping the emotion category and intensity value to an emotional expression and outputting biomimetic muscle contraction commands; adjusting the facial muscle fibers of the humanoid robot according to the biomimetic muscle contraction commands to dynamically adjust the surface texture details of the humanoid robot and generate simulated micro-expressions; capturing user facial feedback data through camera eyes, evaluating the user's acceptance of the simulated micro-expressions based on the user's facial feedback data, and outputting an expression optimization strategy. This invention achieves bio-realistic micro-expressions in a humanoid robot through a dual closed-loop modeling approach of deformation matching and user feedback-driven expression optimization.

The invention discloses an information display method and device, a storage medium and electronic equipment, and relates to the technical field of information display, and the method comprises the steps: obtaining the evaluation content of a user in response to an evaluation instruction of the user for target content; determining emotion type information and emotion intensity information corresponding to the evaluation content; generating a target character image corresponding to the emotion type information and the emotion intensity information; and displaying the target character image in an evaluation area corresponding to the target content. Compared with the prior art, the method has the advantages that the evaluation content of the user is converted into the target character image of the corresponding emotion and is displayed in the evaluation area, so that other users can intuitively perceive the emotion attitude of the user to the target content, and the display fineness of the social evaluation information is improved; the emotion type and intensity are determined in combination with the user evaluation content to generate the target character image, personalized expression of emotion feedback is achieved, and personalized experience of the user in social interaction is improved.

The invention provides a voice data set determination method and device and electronic equipment, and relates to the technical field of artificial intelligence, in particular to the technical fields of deep learning, natural language processing, voice technologies, large models and the like. According to the specific implementation scheme, a voice fragment set and text content corresponding to voice fragments in the voice fragment set are obtained; determining a first emotion type of the voice segment, an emotion intensity of the first emotion type, a second emotion type of the text content and an emotion intensity of the second emotion type, and further determining whether to retain the voice segment; determining a voice data set according to the voice segments reserved in the voice segment set and the text content corresponding to the reserved voice segments; whether the voice segments are reserved or not is determined by combining the emotion intensity of the emotion type, and the emotion intensity of the emotion type of the voice segments in the voice data set can be ensured. The speech synthesis model obtained based on the speech data set training can synthesize the speech representing the real emotion.

The invention discloses a player emotion perception and experience optimization system based on artificial intelligence, and the system comprises an emotion data collection module which is used for collecting multi-modal emotion data of a player in a game process and carrying out the preprocessing of the multi-modal emotion data; the emotion recognition module is used for acquiring the current emotion category and emotion intensity of the player according to the multi-modal data; the experience optimization decision module is used for generating game experience optimization actions matched with the current emotion category and emotion intensity of the player; and the feedback adjustment module is used for adjusting the game environment parameters according to the optimization action. According to the method, the multi-modal emotion recognition and reinforcement learning decision technology is combined, fusion perception of the face image and the voice signal of the player is achieved, the optimal experience adjustment strategy is generated, the method has the advantages of being high in accuracy and high in personalized adaptation ability, and the immersion, interactivity and stability of a game system are remarkably improved.

The invention relates to the technical field of artificial intelligence, is applied to business scenes of intelligent medical treatment, finance and the like, and discloses an intelligent customer service interaction optimization method and device, computer equipment and a medium, and the method comprises the steps: analyzing and obtaining multi-dimensional voiceprint features according to received user voice, and carrying out the emotion classification and emotion intensity quantification of the obtained multi-dimensional voiceprint features; according to the emotion category obtained through emotion classification and the emotion intensity obtained through emotion intensity quantification, a response strategy is matched in combination with a preset response strategy rule; and labeling the user according to the obtained emotion category and emotion intensity, updating a user portrait, and generating a visual report. The user emotion is recognized, the user experience is improved, and the complaint rate is reduced.

An image processing device according to one aspect of the present invention is equipped with an image acquisition unit, a feature point recognition unit, an emotion estimation unit, a movement amount storage unit, a cognitive tendency storage unit, a conversion intensity acquisition unit, a movement amount adjustment unit, and a facial expression conversion unit. The image acquisition unit acquires image data of an image including a person's face region. The feature point recognition unit recognizes feature points in the face region from the image data. The emotion estimation unit estimates the type and intensity of the person's emotion from the image data. The movement amount storage unit stores a movement amount set for each feature point according to each emotion type. The cognitive tendency storage unit stores each individual's cognitive tendency of facial expressions. The conversion intensity acquisition unit obtains a conversion intensity corresponding to the emotion intensity on the basis of a target individual's cognitive tendency. The movement amount adjustment unit adjusts the movement amounts corresponding to the estimated emotion type using the obtained conversion intensity. The facial expression conversion unit converts the image data by moving the feature points by the adjusted movement amounts to generate a face image for the target individual.