118results about "Acquiring/recognising facial features" patented technology

Techniques described herein relate to generating and issuing digital credentials within a credentialing environment, including storing digital credentials with associated sensor data collected via a sensor-monitored environment. Digital credential generation systems may include sensor-based monitoring or detection systems, along with digital credential generation and issuing components. During an evaluation of a credential receiver, and generation / issuance of digital credentials to the receiver, the receiver may be monitored using various sensors. The digital credential generation system may determine and store the relevant sensor data in a digital credential storage repository associated with the particular credential receiver. The associated sensor data may serve as authentication data and / or evidence of the completion of the credential criteria by the receiver. Additionally, the stored sensor data may be automatically applied to additional digital credential criteria, to allow the system to automatically generate and issue updated and / or additional digital credentials to receivers.

The application provides an intelligent propaganda system and method fusing multi-modal data analysis, and belongs to the technical field of robots, and specifically comprises: an image recognition module responsible for acquiring and processing facial images and action images of propaganda objects.A speech recognition module is responsible for acquiring and processing speech data of the propaganda objects.An emotion recognition module determines a fusion processing object according to the deviation of emotion recognition results in each propaganda object and the propaganda duration, and performs emotion analysis processing on the fusion processing object by using multi-modal data.An emotion recognition result output module outputs the results of emotion analysis processing of each propaganda object to nursing staff, thereby improving the reliability of propaganda processing.

In an action control system, an action of an avatar includes creating a picture diary, and in a case where the action determination unit determines creating the picture diary as the action of the avatar, the action determination unit selects the picture or the moving image from the history data, generates an explanatory sentence of a clip of the picture or the moving image on the basis of the emotion value when the selected picture or the moving image is acquired, and outputs a combination of the clip of the picture or the moving image and the explanatory sentence as the picture diary.

This disclosure describes techniques for creating real-time interactive videos. A source image is generated by a first machine learning model based on capturing an image of a user. The image includes a face of the user. One or more facial images of the user are captured. The one or more facial images depict one or more facial expressions. The source image and information extracted from the one or more facial images are input into a second machine learning model. The second machine learning model is configured and trained to transfer the creator’s facial expressions to the machine-generated images in real-time. A real-time interactive video is created by dynamically driving the source image based on the one or more facial expressions.

Various embodiments of the present disclosure provide a gesture translation pipeline that improves the functionality of a computer in various aspects. The techniques comprise receiving an image that depicts a facial expression and a hand position of a user, generating, using a parallel feature extraction model of a multi-stage machine learning architecture, a set of facial features and a set of hand features from the image, generating, using an aggregation model of the multi-stage machine learning architecture, a text prediction corresponding to the image based on the set of facial features, the set of hand features, and a set of defined terms associated with the multi-stage machine learning architecture, and initiating a prediction-based action based on the text prediction.

A computer-implemented method of determining whether a user providing feedback in a feedback collection process is a real person is described. The method comprises: collecting a facial image of the user while they interact with an electronic device to provide feedback without interrupting the user's interaction with the electronic device; and determining, using a personhood-recognition model whether the collected facial image depicts a real person. The personhood-recognition model is trained using a database of previously collected facial images to determine whether a given facial image depicts a real person.

The application discloses a kind of based on multi-modal feature fusion sentiment analysis method and system, comprising: through Bi-GRU capture context relationship between text modal, speech modal and image modal each other, while based on cross-modal attention mechanism, text modal, speech modal and image modal are combined two by two, obtain the interactive sentiment representation between text-image, text-speech and image-speech modal, through the multi-head attention mechanism of regular term, text modal, speech modal and image modal are carried out joint sentiment representation, obtain the interactive sentiment representation of three kinds of modal, finally single modal, double modal and three modal emotion feature cascade are classified finally emotion.The application solves the problem that feature information is not enough rich due to the modeling of context information in the existing multi-modal sentiment analysis algorithm, also solves the information limited problem when using single-head attention mechanism for feature learning and the feature information redundancy problem existing in multi-head attention mechanism.

Provided in the present disclosure are an image processing method and apparatus, and an electronic device and a storage medium. The method comprises: acquiring relative positional relationship information between an intraoral model and a neutral facial model of a target user; using base data of basic expressions to form a plurality of pieces of facial data corresponding to expression states; using the facial data to update the expression state of the neutral facial model; and on the basis of the relative positional relationship information, superimposing a portion, for which an aesthetic design has not been generated, in the intraoral model and a pre-constructed dental model onto the neutral facial model, the expression state of which has been updated, so as to obtain an expression image showing an aesthetic design effect. In the present solution, aesthetic restoration effects under different expressions are dynamically displayed to a target user by means of expression images showing aesthetic design effects, thereby improving the display effect of the aesthetic restoration effects, and the user experience.

The present application relates to a kind of based on two-way optical flow to realize the method for fast micro-expression recognition processing, comprising the following steps: according to visual system acquisition tester face micro-expression video segment information;Extract the emotional video segment in emotional memory library, and the facial muscle movement situation of micro-expression in emotional video segment is captured by positive and negative two-way optical flow;Extract method extracts key frame in emotional video segment, and the redundant frame in continuous sequence image is eliminated;Call the optical flow information between key frame in optical flow information memory library.The present application also relates to a kind of two-way optical flow to realize fast micro-expression recognition device, processor and storage medium.The method for fast micro-expression recognition processing based on two-way optical flow of the present application, device, processor and its computer readable storage medium, the facial muscle movement situation of micro-expression is captured by positive and negative two-way optical flow, the micro-expression of tester is identified using muscle movement trend, and the micro-expression recognition accuracy is improved.

A method for generating a synthesis voice is provided, which is performed by one or more processors, and includes acquiring a text-to-speech synthesis model trained to generate a synthesis voice for a training text, based on reference voice data and a training style tag represented by natural language, receiving a target text, acquiring a style tag represented by natural language, and inputting the style tag and the target text into the text-to-speech synthesis model and acquiring a synthesis voice for the target text reflecting voice style features related to the style tag.

Facial expression recognition method and apparatus via label distribution learning are disclosed. The facial expression recognition method through label distribution learning, comprising: (a) in the training process, preprocessing an input sample to generate a plurality of augmented samples, creating a target label distribution for the input sample using the plurality of augmented samples, and training a model using supervised learning; and (b) after the training is completed, during the inference process, outputting a facial expression recognition result by inputting a single facial sample into the trained model without using the augmented samples.

The application provides a humanoid robot facial expression mapping and calibration method, the facial expression mapping method constructs a self-supervised discrete expression data set, trains a multilayer perceptron model based on the data set, realizes preliminary mapping from expression parameters to rudder control signals, extracts expression parameters in a real person expression data set, generates robot rudder control signals through the model, constructs a self-supervised time sequence expression data set based on a redirection method, trains a long short-term memory network model, and realizes time sequence mapping of continuous expressions. The facial expression calibration method uses a visual capture device to obtain robot facial expression parameters in real time, gradually optimizes the rudder control signal, and makes the expression parameter response approach the target value. Through data-driven modeling and visual feedback optimization, the application realizes high-fidelity mapping from semantic expression parameters to multi-rudder collaborative control and systematic calibration, effectively improving the naturalness, accuracy and long-term stability of the robot facial expression.

A computing system providing an enhanced user experience with a voice assistant (VA) that may be part of an automotive or vehicle infotainment system. A speech recognition module 10 converts spoken in

A computer device such as a smartphone includes at least one processor programmed to monitor a video feed from at least one camera to detect a facial profile of an operating user when a messaging interface is being used, match the detected facial profile against a known facial profile utilizing a facial recognition process to verify an identity of the operating user, determine a plurality of human expressions of the operating user based on the video feed and an expression recognition process, associate the plurality of human expressions with a plurality of contextual tags, generate a user profile in a user profile database based on the plurality of human expressions and the plurality of contextual tags, determine a current activity for the smartphone, associate the determined activity for the smartphone with one of the contextual tags, and update the user profile in the user profile database based on the association.

The application provides a lamp adaptive adjustment method and system based on driver interaction, and relates to the technical field of vehicle driving. The method comprises the following steps: constructing display schemes of a lamp group and display atmospheres corresponding to each display scheme based on sample pictures uploaded by a user; monitoring facial expressions and gesture changes of a driver in real time through a visual acquisition subsystem; matching a target display scheme and a target display atmosphere from the display schemes according to the facial expressions and gesture changes, and determining a corresponding target lamp group control instruction; and adjusting the display color of each LED lamp in the vehicle lamp group according to the target lamp group control instruction, so that the lamp group display matches the target display scheme and the display atmosphere. By implementing the method, the display scheme and the display atmosphere of the lamp group can be automatically adjusted to match the mood and environmental requirements of the driver, the individualization and comfort of the vehicle interior environment are increased, and the interactivity and intelligent degree of the driving experience are improved.

The present disclosure provides a facial expression simulation method and apparatus, a device, and a storage medium. The method comprises: collecting a local facial image to be processed of a target object, and generating an expression coefficient corresponding to the local facial image to be processed, wherein the local facial image to be processed belongs to an expression image sequence, and the expression coefficient is determined on the basis of the position of the local facial image to be processed in the expression image sequence; and simulating a facial expression of the target object according to the expression coefficient.

The subject technology captures first image data by a computing device, the first image data comprising a target face of a target actor and facial expressions of the target actor, the facial expressions including lip movements. The subject technology generates, based at least in part on frames of a source media content, sets of source pose parameters. The subject technology receives a selection of a particular facial expression from a set of facial expressions. The subject technology generates, based at least in part on sets of source pose parameters and the selection of the particular facial expression, an output media content. The subject technology provides augmented reality content based at least in part on the output media content for display on the computing device.

An electronic device with a fatigue detection function is provided and includes a keyboard, a key detection circuit, a first processing circuit, a control circuit, and a display screen. The keyboard includes at least one key. The key detection circuit detects the pressing state of the key to generate a first detection signal. The first processing circuit converts the first detection signal to a first processing signal. The control circuit determines whether a specific event occurs according to the first processing signal. In response to the specific event, the control circuit sends a reminder signal. The display screen displays a reminder image according to the reminder signal.

Systems and methods are disclosed for training and using a digital human foundational model (DHFM) comprising a generative adversarial network (GAN) generator. For instance, the method may include obtaining one or more inputs comprising pose information indicating a three-dimensional (3D) pose representation of a human and processing the one or more inputs using a mapping network to generate intermediate latent code. The method may further include processing the intermediate latent code using the trained generator to generate texel-aligned Gaussian maps that align Gaussian attributes to a coarse mesh template of the human and performing linear blend skinning and deformation on the texel-aligned Gaussian maps to obtain modified texel-aligned Gaussian maps. The method may also include processing the modified texel-aligned Gaussian maps using a multi-part renderer to generate a synthetic human representation of the human indicating facial and hand features of the human.

Techniques are disclosed for providing an avatar personalized for a specific person based on known data from a relatively large population of individuals and a relatively small data sample of the specific person. Auto-encoder neural networks are used in a novel manner to capture latent-variable representations of facial models. Once such models are developed, a very limited data sample of a specific person may be used in combination with convolutional-neural-networks or statistical filters, and driven by audio / visual input during real-time operations, to generate a realistic avatar of the specific individual's face. In some embodiments, conditional variables may be encoded (e.g. gender, age, body-mass-index, ethnicity, emotional state). In other embodiments, different portions of a face may be modeled separately and combined at run-time (e.g., face, tongue and lips). Models in accordance with this disclosure may be used to generate resolution independent output.

In an aspect, a system is described. The system comprises: a sensor module; and a processor storing instructions in non-transitory memory that, when executed, causes the processor to: communicate a first command to the sensor module to capture one of an image and a video of one or more facial expressions of one or more occupants in a vehicle; extract one or more facial landmark characteristics from the one or more facial expressions; compare the one or more facial landmark characteristics with one or more facial baseline landmark characteristics to determine deviations; classify the one or more facial landmark characteristics based on the deviations exceeding threshold values; determine a physiological state of the one or more occupants based on the classification; and communicate a second command to an electric drive unit based on the physiological state to control the vehicle to ensure safety of the one or more occupants.

The present application relates to the technical field of image detection, in particular to a multi-modal data video inspection concentration analysis and early warning method and system, the method comprising collecting facial video data, extracting eye micro-motion features, eyebrow shape features and forehead muscle television features in facial features; collecting heart rate variability data and skin electricity reaction data in physiological signals; performing individualized standardization processing, converting into Z-score feature vectors of deviation degree relative to user's own baseline level value; based on the Z-score feature vectors, constructing time sequence feature vectors and inputting into a multi-modal time sequence fusion network for time sequence collaborative mode analysis, outputting current concentration probability value and concentration prediction value at a specified time point in the future; triggering a hierarchical early warning mechanism based on the current concentration probability value and the concentration prediction value. The present application utilizes information complementation in multi-modal data fusion, thereby improving the robustness and accuracy of state recognition.

A behavior control system according to an embodiment of the present invention recognizes the behavior of an artist and determines the behavior of an avatar corresponding to the recognized behavior of the artist. Then, the behavior control system controls the avatar on the basis of the determined behavior of the avatar.

A system and method for neurobehavioral testing, including eyeblink conditioning and prepulse inhibition, without air puffs, that utilizes an electronic device with a light source, a camera, and a speaker, and makes assessments based on the degree to which an eyelid is closed after a user is exposed to conditional and unconditional stimuli.

The present application relates to a kind of lightweight facial expression recognition method and system based on attention mechanism, comprising the following steps: establishing convolution model, the picture of data set is cropped, and the picture is preprocessed, and the picture after pre-processing is input into convolution model;Picture is extracted in convolution model, attention mechanism is recalibrated and down-sampling operation is carried out, and the feature map of final output is obtained;Vector in feature map is classified into expression, and recognition result is obtained;Establish loss function model, use recognition result to train model parameter and test, complete facial expression recognition.Ghost convolution in GhostNet network is introduced to reduce the parameter amount of pointwise convolution, in order to eliminate the interference of expression irrelevant factors, the improved coordinate attention mechanism can simultaneously focus on the position information and context information of expression image, increase the weight of key area of facial expression image, improve the performance of identification, compared with conventional expression recognition method, model parameter amount is less, and recognition accuracy is higher.

In some implementations, a computer system identifies multiple sub-sessions of a network-based communication session in which multiple remote endpoint devices each provide media streams over a communication network. For each of the sub-sessions, the computer system can identify the endpoint devices included in the sub-session. The computer system can obtain user state data for each of the endpoint devices, the user state data for each endpoint device being generated based on analysis of face images of the user of the endpoint device. The computer system aggregates the user state data to determine a sub-session state for each of the sub-sessions. During the communication session, the computer system communicates over the communication network with a remote device associated with the communication session to cause a user interface of the remote device to indicate the sub-session states determined for one or more of the multiple sub-sessions.

This invention discloses a method, apparatus, storage medium, and terminal for capturing facial expressions. The method includes: acquiring a face image to be recognized and extracting a target implicit code from the face image; inputting the face image to be recognized and the target implicit code into a pre-trained facial expression capture model, and outputting facial expression information corresponding to the face image to be recognized; wherein the facial expression information is generated based on facial expression features, and the facial expression features are generated by fusing multiple extracted features. Because this application extracts multiple features and fuses them into new features for model training, it can effectively improve the model's accuracy in capturing facial expression information.

Embodiments herein relate to identifying a status of a patient such as in a dental or other medical treatment room. In one aspect, the solutions provide a system to determine whether the patient is anxious and / or a degree of anxiety of the patient. The solutions can include training a model such as a large language model (LLM). In a training phase, data is gathered from patients, the environments of treatment rooms and equipment of the treatment rooms during various medical procedures for a patient population. The model is then trained to correlate the data with a degree of anxiety. Once the model is trained, it can be deployed to determine whether an individual patient is anxious and to take appropriate countermeasures to reduce anxiety.

The present invention enables emotion data, which represents user emotion for each scene of moving image content, to be effectively used.　A representative emotion scene is extracted by an extraction unit on the basis of emotion metadata having user emotion information for each scene of the moving image content. On the basis of the extracted representative emotion scene, playing back a portion of the moving image content or editing for taking out a portion of the moving image content can be effectively performed. For example, the extraction unit extracts the representative emotion scene on the basis of the type or degree of user emotion.

Systems and methods for generating gameplay recommendations are described. A recommendation system detects a user device login for a gaming application and collects video data and biometric data from the user device. The video data includes gameplay video data and camera feed from a camera associated with the user device. The biometric data is collected from one or more biometric devices connected to the user device. The system detects termination of an application session and computes emotional state data for a user of the user device based on the camera feed and the biometric data. The system also detects in-game events based on the gameplay video data. Based on the detected in-game events and respective outcomes correlated with the computed emotional state data, the recommendation system generates gameplay recommendation data.