596 results about "Facial expression recognition" patented technology

The invention relates to a robot man-machine interaction method based on user mood and intension recognition. User mood recognition is carried out by combining human body biological information such as breath, the heart rate and the dermal electricity with facial expression recognition. The user intension is recognized through different kinds of physical sensor information such as pressure, photoelectricity and temperature. Intelligent control and decision making are carried out according to the user mood and intention recognition results. A corresponding execution mechanism of a robot is controlled to complete limb movements and voice interaction. By means of the robot man-machine interaction method based on user mood and intension recognition, the robot can understand the user intension more thoroughly, know the metal changes of a user, meet the functional requirement for emotion care for the user, and better participate in the life of users such as the old people and children.

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

This invention provides an identification method and a device for countenance based on the video, which applies the ASM profile pick-up algorithm in the pick-up of character vectors and picks up the man-face image based on the position of the eyes to generate a normalized character face from the chin and picks up the most effective character in the character face to identify the countenance, which can eliminate the influence of illumination to make the right and left gray value almost the same with the variance.

The invention discloses a household information acquisition and user emotion recognition equipment, which comprises a shell, a power supply, a main controller, a microcontroller, multiple environmental sensors, a screen, a microphone, an audio, multiple health sensors, a pair of robot arms and a pair of cameras, wherein the microphone is arranged on the shell; the power supply, the main controller, the microcontroller, the environmental sensors, the audio and the pair of cameras are arranged symmetrically relative to the screen respectively on the left and right sides; the robot arms are arranged on the two sides of the shell; the main controller is in communication connection with the microcontroller, and is used for controlling the microcontroller to control the movements of the robot arms through motors of the robot arms; the power supply is connected with the main controller and the microcontroller, and is mainly used for providing energy for the main controller and the microcontroller. According to the household information acquisition and user emotion recognition equipment, the intelligent speech recognition technology, the speech synthesis technology and the facial expression recognition technology are integrated, thus the use of the household information acquisition and user emotion recognition equipment is more convenient, and the feedback is more reasonable.

A facial expression recognition apparatus includes an image input unit configured to sequentially input images, a face detection unit configured to detect faces in images obtained by the image input unit, and a start determination unit configured to determine whether to start facial expression determination based on facial image information detected by the face detection unit. When the start determination unit determines that facial expression determination should be started, an acquisition unit acquires reference feature information based on the facial image information detected by the face detection unit and a facial expression determination unit extracts feature information from the facial image information detected by the face detection unit and determines facial expressions of the detected faces based on the extracted feature information and the reference feature information.

The invention discloses a face multi-area fusion expression recognition method based on depth learning, which comprises the following steps of detecting a face position with a detection model; obtaining the coordinates of the key points by using the key point model; aligning the eyes according to the key points of the eyes, then aligning the face according to the coordinates of the key points of the whole face, and clipping the face region by affine transformation; cutting the eye and mouth areas of the image to a certain proportion; dividing the convolution neural network into one backbone network and two branch networks; carrying out the feature fusion in the last convolution layer, and finally obtaining the expression classification results by the classifier. The method of the inventionutilizes the priori information, besides the whole face, the eyes and mouth regions are also used as the input of the network, and the network can learn the whole semantic features of facial expressions and the local features of facial expressions through model fusion, so that the method simplifies the difficulty of facial expression recognition, reduces the external noise, and has strong robustness, high accuracy, low complexity of the algorithm and so on.

The invention discloses a PCANet-CNN-based arbitrary attitude facial expression recognition method. The method comprises the following steps: firstly pre-processing the original images to obtain gray level facial images with uniform size, wherein the gray level facial images comprise front facial images and side facial images; inputting the front face images into an unsupervised characteristic learning model PCANet and learning to obtain characteristics corresponding to the front facial images; inputting the side facial images into a supervised characteristic learning model CNN, and training by taking the front facial characteristics obtained through the unsupervised characteristic learning as labels so as to obtain a mapping relationship between the front facial characteristics and the side facial characteristics; and obtaining uniform front facial characteristics corresponding to the facial images at arbitrary attitudes through the mapping relationship, and finally sending the uniform front facial characteristics into SVM to train so as to obtain a uniform recognition model in allusion to arbitrary attitudes. According to the method provided by the invention, the problem of low model recognition rate caused by the condition of respectively modeling for each attitude in the traditional multi-attitude facial expression recognition and the factors such as attitude and the like is solved, and the correctness of the multi-attitude facial image expression recognition can be effectively improved.

The invention discloses a teaching system based on an internet, facial expression recognition and speech recognition and a realizing method of the teaching system. The realizing method comprises the following steps: S1, playing contents of teaching courses by a first terminal; S2, acquiring video data information, speech data information of a user and user operation in the playing process; S3, transmitting the information and the user operation to a main control processor; S4, extracting facial features and pronunciation features of the user into an analysis processor by the main control processor; S5, respectively comparing the facial features and the pronunciation features with standard templates by the analysis processor; S6, dynamically adjusting the played contents of the teaching courses or/and teaching procedures by the main control processor according to current operation of the user and feedback of the analysis processor, or transmitting a comparison result to a second terminal by a cloud platform in real time. The teaching system disclosed by the invention is teaching software with the characteristics of mobility, entertainment, sociability and the like and can provide a chance for students to study independently outside class at any time and any place; by means of an online teaching mode for assisting real persons, a traditional mode for teaching Chinese as a foreign language is improved.

A facial expression recognition apparatus (10) detects a face image of a person from an input image, calculates a facial expression evaluation value corresponding to each facial expression from the detected face image, updates, based on the face image, the relationship between the calculated facial expression evaluation value and a threshold for determining a facial expression set for the facial expression evaluation value, and determines the facial expression of the face image based on the updated relationship between the facial expression evaluation value and the threshold for determining a facial expression.

The invention provides a deep learning-based emotion recognition method and system. The system includes a face image acquisition module, a deep learning-based facial expression recognition module and a facial expression early warning module. The facial images of employees are acquired when the employees punch in every time; the emotions of the employees are analyzed through adopting a deep learning algorithm-based facial expression analysis algorithm, and the emotions are compared with historical emotions; and when the emotions are abnormal, the system sends alarm information to relevant personnel. According to the deep learning-based emotion recognition method and system of the present invention, the deep learning algorithm is adopted to perform emotional analysis on the employees, and therefore, deep-level humanistic care can be provided for the employees.

The invention discloses a face recognition method and system based on a large-scale face database. The recognition method comprises the following steps: obtaining a recognition image; image preprocessing: carrying out illumination compensation, graying, filtering denoising and normalization processing on an image to be detected to obtain a high-quality grey level image; face detection: detecting and positioning a face in a scene image, and separating the face from a background; characteristic extraction and expression: describing and modeling a face mode, and expressing the face by a characteristic vector; face matching: utilizing a similarity between the characteristic vector used for calculating the face image to be detected and a training sample to obtain discrimination information; and outputting a result. The face recognition method and system based on the large-scale face database not only improves the speed and the performance of face recognition under a large-scale database. Meanwhile, the face recognition method and system can be conveniently applied to the recognition field of other modes including facial expression recognition, face tracking and the like.

The embodiments of the present invention provide a facial expression recognition method, a convolutional neural network model training method, a facial expression recognition device, a convolutional neural network model training device and an electronic apparatus. The facial expression recognition method includes the following steps that: facial expression features are extracted from a face imageto be detected by means of the convolutional layer portion of a convolutional neural network model and acquired face key points in the face image to be detected, so that a facial expression feature image is obtained; ROI (regions of interest) corresponding to the face key points in the facial expression feature image are determined; pooling processing is performed on the determined ROIs through adopting the pooling layer of the convolutional neural network model, so that a pooled ROI feature image can be obtained; and the facial expression recognition result of the face image is obtained at least according to the ROI feature map. With the facial expression recognition method provided by the embodiments of the present invention adopted, subtle facial expression changes can be effectively captured, and at the same time, differences caused by different facial gestures can be better processed; and the detailed information of the changes of the plurality of regions of a face are fully utilized, so that subtle facial expression changes and faces in different postures can be recognized more accurately.

An information processing apparatus includes an image input unit which inputs image data containing a face, a face position detection unit which detects, from the image data, the position of a specific part of the face, and a facial expression recognition unit which detects a feature point of the face from the image data on the basis of the detected position of the specific part and determines facial expression of the face on the basis of the detected feature point. The feature point is detected at a detection accuracy higher than detection of the position of the specific part. Detection of the position of the specific part is robust to a variation in the detection target.

The present invention discloses a facial expression recognition method and device. The method comprises the following steps that: the local features and overall features of a face in a detection image are extracted; the local features, overall features and detection image are inputted into a deep learning model, so that the classification results of the detection image belonging to different facial expressions are obtained, wherein the deep learning model is obtained through training a deep neural network in advance according to the training images of different facial expressions and the local features and overall features of the training images; and the facial expression of the detection image is determined according to the classification results. With the facial expression recognition method and device of the invention adopted, the accuracy of facial expression recognition can be improved.

The invention discloses a multi-view facial expression recognition method based on a mobile terminal, which comprises the steps of cutting out a face region from each image, and carrying out data enhancement to obtain a facial expression recognition method used for training data set of AA-MDNet model; the multi-attitude data set is obtained by GAN model extension, and the multi-attitude data set is obtained by GAN model extension. Using ADN multi-scale clipping method to clip; enter the cropped image into AA-MDNet model, The input image extracts features from DenseNet, a densely connected subnetwork, Then, based on the extracted features, an attention adaptive network (ADN) is used to obtain the position parameters of the attention area of the expression and posture, and the image of the area is scaled from the input image according to the position parameters, which is used as the input of the next scale. Learning the multi-scale high-level feature fusion, we can get the high-level features with global and local fusion features. Finally, we can classify the facial posture and expression categories. The invention has very important significance in the fields of human-computer interaction, face recognition, computer vision and the like.

The invention discloses a method for recognizing human facial expression by virtue of a binary-channel convolutional neural network. According to the method, firstly, pretreatments are carried out aiming at different input signals and include human face detection, rotation correction, downsampling and data sample expansion (if RGB images are input, the RGB images are grayed so as to decrease computation complexity), so that the human face detection precision is improved; secondly, LBP images corresponding to sample-expanded gray images are calculated so as to form a binary-channel sample set for subsequent model training and testing; a binary channel feature extraction network (Binary Channel-Feature Extraction Network, BC-FEN) is utilized for effectively extracting overall and local features of a human face image; and finally, a weighted merge classify network (Weighted Merge Classify Network, WMCN) is utilized for finishing the feature fusion and expression classification of the human face image, so that the human face expression recognition precision is improved.

The invention discloses a face expression recognition method of multi-scale feature extraction and global feature fusion. A human face expression data set is selected as raw data, and the raw data isdivided into training set data and test set data. A TensorFlow artificial intelligence learning system is used to construct a convolution neural network with multi-scale feature extraction and globalfeature fusion. The convolution neural network reads the training set data, pretreats the training set data and performs model training, then reads the test set data, recognizes the expression types of each expression in the test set data in turn, and calculates the average accuracy rate and average F1 of all expressions after completing the recognition of all expressions. Score indicators, the final completion of the process of facial expression recognition. The invention has the advantages of high recognition speed under the condition of ensuring high recognition accuracy, and can be adaptedto multiple illumination environments with strong robustness, so as to effectively meet the practical application requirements.

The invention relates to a facial expression recognition method based on a generative antagonistic network, which relates to computer vision technology. Firstly, a facial expression generation networkbased on generative antagonistic network is designed and pre-trained. The network is composed of a generator and two discriminators, which can generate a face map with random identities of specifiedfacial expressions. Then a facial expression recognition network is designed, which receives the facial expression map from the training set and the random facial expression map generated by the facial expression generation network, and uses an intra-class loss to reduce the facial expression feature differences between the real samples and the generated samples. At the same time, a real sample-oriented gradient updating method is used to promote the feature learning of the generated samples. Finally, according to the trained facial expression recognition network model, the final facial expression recognition results are obtained from the final flexible maximum classification layer of the model.