144 results about "Emotion detection" patented technology

The invention provides a non-contact type emotion detection method and a device, and relates to the technical field of emotion detection. The method comprises the following steps of collecting video information containing a to-be-detected human body and a heat map / hot point area graph of the to-be-detected human body through a camera and an infrared sensor; recognizing the video information and the heat map / hot point area graph, so as to obtain the human body feature data of the to-be-detected human body; performing deep learning, engine correlation and analysis on the human body feature data, so as to obtain the nervousness degree of the to-be-detected human body; outputting the result of the nervousness degree of the to-be-detected human body through the deep learning, engine correlation and analysis. The method has the advantage that by combining intelligent sensing technique, machine vision and deep learning, engine correlation and analysis, the human body feature data can be judged in short time according to the collected video and other data, the hazard index and psychological nervousness degree of the to-be-detected human body can be finally analyzed, and the effective reference is provided for a detector to predict and prevent the hazard.

An image enhancement system and method using automatic emotion detection, the image enhancement system including: an emotional scale detection unit to analyze a pixel value of one or more frames of an input image in order to automatically detect an emotional scale of the input image; and an image enhancement unit to enhance a quality of the input image based on an image mode selected according to the emotional scale.

A system, method and computer-readable medium for practicing a method of emotion detection during a natural language dialog between a human and a computing device are disclosed. The method includes receiving an utterance from a user in a natural language dialog, receiving contextual information regarding the natural language dialog which is related to changes of emotion over time in the dialog, and detecting an emotion of the user based on the received contextual information. Examples of contextual information include, for example, differential statistics, joint statistics and distance statistics.

A method, a system and a computer program product for ranking reviewable entities based on sentiment expressed about the entities. A plurality of review texts are identified wherein each review text references an entity. A plurality of sentiment scores associated with the plurality of review texts are generated, wherein each sentiment score for a review text indicates a sentiment directed to the entity referenced by the review text. A plurality of ranking scores for the plurality of entities are generated wherein each ranking score is based at least in part on one or more sentiment scores associated with one or more review texts referencing the entity. A plurality of search results associated with the plurality of entities are displayed based at least in part on the ranking scores.

The invention discloses an emotion detection method based on brain electric wave analysis. The method includes the following steps: firstly, extracting features by using the original electroencephalo-graph data, wherein the features include fractal dimension feature, energy feature, statistical feature and high-order cross feature; evaluating the feature parameters of the extracted features by an intraclass correlation coefficient-like method so as to obtain the most stable feature parameter; allowing a support vector machine to use the obtained feature parameters to train a classification model; finally, conducting the real-time detection of the emotion with allowing the trained classification model. The emotion detection method has the remarkable advantages that the most stable feature parameter can be obtained by the intraclass correlation coefficient-like method, and the stable and accurate classification model is thus successfully trained; compared with the traditional method, there is no need to re-train the classification model, the operation is simpler and the classification accuracy is higher.

The invention discloses a feature-level fusion method for multi-modal emotion detection. The method comprises the following steps: obtaining a transcript in a text form from a public data set; applying a single-layer CNN on the vector sequence X; extracting audio information from an audio file in the data set by using an open source tool openSMILE, and mapping a high-dimensional vector into a dense neural layer to obtain an audio feature vector; using the multi-dimensional self-attention as a feature fusion method for calculating the attention score probability of the single-peak feature; performing a weighted addition using the attention score probability to create a fusion vector; mapping the generated fusion vector su to another dense neural layer; calculating the classification probability of the fusion vector by using a softmax function; and calculating the batch loss Loss of the training based on back propagation by using the classification cross entropy to obtain an optimal emotion prediction node. According to the method, a self-attention mechanism is introduced, proper weights are allocated to the two modal features, and fusion features are obtained, so that the emotion recognition accuracy is improved.

The invention discloses an emotion trans-individual identification method based on tranquillization electroencephalography similarity. The emotion trans-individual identification method comprises the following steps that a multi-individual emotion database is established, each individual emotion database comprises n tested tranquillization electroencephalography and emotion electroencephalography, power spectral densities of the electroencephalography at four frequency bands are extracted through an AR model, and tranquillization electroencephalography characteristic matrixes and emotion electroencephalography characteristic matrixes are formed; the tested tranquillization electroencephalography needing to be detected is recorded, euclidean distances of the tested tranquillization electroencephalography needing to be detected and n tested tranquillization electroencephalography characteristic matrixes in the emotion database are correspondingly calculated, and smallest-distance 0.2*n subjects are selected to be used as user groups of emotion identification model training sets; and an emotion detecting model is established by using the emotion electroencephalography characteristic matrixes of the user groups, so that trans-individual emotion electroencephalography identification is accurately and objectively conducted. By means of the emotion trans-individual identification method based on the tranquillization electroencephalography similarity, the bottleneck problem that in current emotion identification, the accuracy of trans-individual identification is low can be effectively solved, the model is pushed into application, and considerable social and economic benefits are obtained.

The invention provides a detecting method and device for a personal abnormal emotion based on a personal microblog. The method comprises that steps that first preset several microblog text data of a designated user in a preset time quantum is acquired; a support vector machine is utilized to perform emotion recognition and annotation on the first preset several microblog text data, and second preset several emotions are obtained; according to a preset time unit, emotion distribution statistics is carried out on the microblog text data of the second preset several emotions, and a multi-dimension data set of the designated user is obtained; multivariate normal distribution is adopted to carry out fitting on the multi-dimension data set in the preset time quantum, and a normal distribution curve of corresponding emotions is obtained; a joint probability density value of the multi-dimension data set is counted; based on the normal distribution curve and / or the joint probability density value, the abnormal emotions of the designated user are determined. Therefore, qualitative and quantitative analysis is carried out on the abnormal emotions of the designated user by acquiring the normaldistribution curve and joint probability density value, and the detecting accuracy and reliability can be improved.