Questions related to Emotion Recognition
Can anyone share with me a Health-dataset that is related to emotion recognition, datasets that contents of Text, Audio, Visual, and physiological?
Are emotions just a body reaction? The "Perceptual Theory of Emotion" (see Printz) assumes that the feeling represents the internal state of the body, signaled by interoceptors located throughout the body, in all internal organs. They represent the quality of their functioning - the state of homeostasis. In addition to interoceptor signals, information is transmitted to all cells of the body in the form of hormones, neuromodulators, and neurotransmitters released into the bloodstream.
Of course, we also have higher mental states related to the awareness of our own emotional state. But now we are talking not about propositional and access awareness, but about perceptual and phenomenal awareness. So love is just butterflies in the stomach, general and sexual arousal, etc., according to every psychology textbook. Of course, there are also hopes for better financial position and housing, travel, joint children, etc., but this is precisely the propositional sphere.
Similarly, other feelings can be recognized and associated with the body's corresponding homeostatic/bodily, somatic, and behavioral responses. Bodily reactions represent the internal states of the organism and are closely related to the internal organs. We can mention here a plethora of examples: contraction and relaxation of smooth muscles, constriction of the bronchi, trachea, larynx, pharynx, obstruction of the airways, contractile gasping for air; behavioral reactions: emotions are expressed in facial expressions, body posture, the diaphragm tightens, which causes shallow breathing. Under stress, ignorant people tighten the anus and buttocks, and the weight of the body shifts from the metatarsus to the heels - that's why people move and stand differently. The kneecaps are pulled up, and the thighs are stiffened, the muscles lying along the spine are also strained, the hair stands out, eyes blink, the heart rhythm changes, palpitations. Next let us mention somatic reactions: sleep disturbances, headaches of various nature, pain in the spine and joints, lack of energy, hunger, thirst, heartburn, itching, burning, numbness, colic, tingling, redness, pain in various parts of the body, sweating. And on the part of the digestive system: spasms of the intestines, stomach, flatulence, belching, vomiting, nausea, indigestion, constipation, irritable bowel syndrome, etc. And other psychogenic factors such as teeth grinding, dizziness, euphoria, etc., etc.
Can we add anything more to this list? How about other emotions?
I am currently working on setting up this EVAL-AD5940BIOZ (Bio-Electric Evaluation Board: https://www.analog.com/en/design-center/evaluation-hardware-and-software/evaluation-boards-kits/eval-ad5940bioz.html#eb-overview) for measuring the skin impedance value for my project. Due to the lack of proper documentation from the Analog devices team, I am facing difficulty in obtaining human's skin impedance data.
They have given documentation only for acquiring the data from their Z-Test board (which has various combinations of R, L, C parameters to mimick skin) but not for acquiring EDA data from human skin.
If anyone has experience with the setup, could you share your insights on it? It would be helpful to many researchers who are working in this domain.
P.S: I have tried asking for help in their forum but nothing worked for me as of now.
Given the different methods of speech feature extraction (IS09, IS10,...), which one do you suggest for EMODB and IEMOCAP datasets?
I am working on a paper and using the Schwartz's (1992) value scale. I have translated each value into my own language. Should I conduct a pilot study first in order to get the validity and reliability of the scale?
I am working on my Master's project. I need to consider emotions of learner in real time. Is there any free software available for this purpose.
and mathworks code for behavioral detection and some other but they are too costly to afford can any one help in this regard.
I need a set of images of emotional faces (angry or sad; neutral; happy) of infants. I would like to frontally present these faces on a screen in a computer experiment. If infant images were matched in size, luminance, position, etc, with other images of adults, it would be great! Yhank you
research paper regarding Emotion Recognition using big data analytics and Spark MLlib
Face and emotion detection using spark MLlib
What are the main feature you would extract from a social netowrk to model wellbeing and mental health.
Also what the common formulas for the following features: Engagment, popularity, participation, ego.
The triangular theory of love by Sternberg, characterises love in an interpersonal relationship using three different dimensions: intimacy, passion, and commitment.According to this theory different stages and types of love can be categorised by different combinations of these three elements. My question is this: does this model fully capture the essense of what we call love. Can love not also have other attributes, we can have feelings of love towards a person without having either commitment, passion or intimacy, some people are asexual for example and lack commitment, others such as teenagers lack the understanding of what love is. Do you agree with this theory or do you like me see problems associated with such a sharp division of element of human affects. Best wishes Henrik
This is not a question:
We used an emotion-evaluated corpus consisting of 10 000 English sentences from 7 genres. We applied a specific phonemic decomposition based on the phonetic transcripts. The result of the applied principle component regression showed that the phonemic content is very strongly related (r = 0.96) with the ratings of emotional valence (positive-negative emotion), provided by readers. We designed an online experiment that aims at evaluating are the discovered dependencies valid out of the corpus and how... all this depends on the native language of the reader.
We will be grateful, dear colleagues, if you find time to assist this emotional sound-symbolic statistical analysis by participating in the online experiment here:
It will, unfortunately, take you about 20 minutes.
The experiment is horrible - reading eight texts and deciding how to group them depending on your feelings. The texts are one page long!
But we have no other possibility than to ask colleagues for scientific assistance.
Now - the question is this one:
Could you, please, find 20 minutes to participate?
Thank you in advance!
I am engaged in a project "Human affect based threat prediction". I am interested to see if I can learn to correlate human emotion to predict human behavioral responses. But am collecting such references that make such claims or collecting literature on similar idea. Help is very much appreciated.
In all the Emotion Recognition through EEG research papers for DEAP Dataset, I noticed that the 60-second data was divided into many small chunks and then feature extraction methods were applied on that chunk. So we generally get several hundred values after feature extraction from that single 60-second data (DEAP Dataset has 1280, 60-second data with 128Hz sampling rate).
Now while training the model, all the research works just shuffled the data and divided it into training and testing dataset. So now for example let's say from a single 60-second data we got 100 values after feature extraction, so out of them 60 go-to training dataset and 40 go to testing dataset. So, now would this be a fair evaluation while training it on a classification model. As a part of the video used for evaluation is in the training dataset?
And, when trying out the same model such that the whole values extracted from a single 60-second data goes to either training or testing dataset and no splitting happens in it, then the accuracy drops significantly when compared to the above case or sometimes doesn't even learn anything.
Dear community , currently working on emotions recognition , as a first step I'm trying to extract features , I was checking some recources , I found that they used the SEED dataset , it contains EEG signals of 15 subjects that were recorded while the subjects were watching emotional film clips. Each subject is asked to carry out the experiments in 3 sessions. There are 45 experiments in this dataset in total. Different film clips (positive, neutral, and negative emotions) were chosen to receive highest match across participants. The length of each film clip is about 4 minutes. The EEG signals of each subject were recorded as separate files containing the name of the subjects and the date. These files contain a preprocessed, down-sampled, and segmented version of the EEG data. The data was down-sampled to 200 Hz. A bandpass frequency filter from 0–75 Hz was used. The EEG segments associated with every movie were extracted. There are a total of 45 .mat files, one for each experiment. Every person carried out the experiment three times within a week. Every subject file includes 16 arrays; 15 arrays include preprocessed and segmented EEG data of 15 trials in one experiment. An array named LABELS contains the label of the corresponding emotion- al labels (−1 for negative, 0 for neutral, and +1 for positive). I found that they loaded each dataset separately (negative , neutral , positive) , and they fixed length of signal at 4096 and number of signal for each class at 100 , and fixed number of features extracted from Wavelet packet decomposition at 83 , my question is why they selected 83 , 4096 and 100 exactly ?
I know that my question is a bit long but I tried to explain clearly the situation , I appreciate your help thank you .
Emotion recognition is an upcoming research area with strong applications in the next-generation communications using digital modes. To develop robust algorithms for this, we are looking for some good datasets.
I am working on project which ultimate goal is emotion classification from speech and I want to try several approaches how to do this. One of them is training convolutional neural network using MFCC coefficients extracted from audio. I can easily extract them, since there are several python libraries capable of doing so, but I am not quite sure how to use them, since I have matrix of 13xN values depending on how long is audio input, but that obviously is not good as input for neural network.
I understand that coefficients are calculated for very short frames and that's my N, and I possibly could feed network frame after frame, but since emotions are not changing rapidly in miliseconds, I'd like to work in wider context, let's say I'd like to have 13x1 vector for every 3-4 seconds. Now let's say I am able to isolate coefficients for given time (e.g. 13x200 matrix = 3 seconds of audio), but how do I make it into 13x1 with considering the fact that this vector is intended for emotion recognition? I mean, am I supposed to calculate e.g. mean and use 13 means as neural network input? Or standard deviances, or something else, or combination of few? What about normalisation or some different preprocess of coefficients?
The most papers covering this issue are very vague about this part of whole process, usualy saying only something like "we used 13 mfcc coefficients as neural network input", but no details about how to actually use them.
Can someone tell me what are the best practices with mfcc in emotion recognition or can someone recommend some papers covering this problem?
I am new to Machine Learning and I am currently doing research on speech emotion recognition using deep learning. I found out that recent literatures were using mostly CNN and there are only few literatures found for SER using RNN. I also found out that most approaches used MFCCs.
My questions are:
- Is it true that CNN has been proved to outperform RNN in SER?
- If yes, what are the limitations that RNN have compared with CNN?
- Also, what are the limitations of the existing CNN approaches in SER?
- Why MFCC is used the most in SER? Does MFCC have any limitations?
Any help or guidance would be appreciated.
I am currently working on emotion recognition. I am looking for a dataset that contains labeled image files for emotion (i.e. happiness, sadness, etc).
I've found several dataset but not exactly what I need.
· Google facial expression comparison dataset
There is no label (like emotion categories) for dataset.
The dataset does not include image files. Instead, pixel values of images are stored in CSV files.
· The Japanese Female Facial Expression (JAFFE) Database
If you know a large dataset, could you please inform me? Thanks a lot.
I'm working on a research and I need references to studies, papers, web pages, etc., to know how a computer based system could in the future detect when a student is not understanding a topic of a lecture by detecting the emotions expressed in a student's face. So, imagine you are a Professor and you are explaining a new concept to the class. Maybe you see on your students' faces a confusion or surprise feeling that tells you they aren't understanding well the concept you are explaining. That's what I need, I need a source, reference to a paper or any oficial source of information to proof that what I'm saying aren't just word, but it is proven by researches. I want to prove that certain emotions can tell us the weaker knowledge points of students. All I've found is about how positive/negative emotions affect academic achievement, performance, etc., but I want to focus on the association of weak knowledge points with emotions, how we may know a student isn't understanding a topic by the emotion he is expressing.
Thank you so much and I hope I've explained myself.
Affective technologies are the interfaces concerning the emotional artificial intelligence branch known as affective computing (Picard, 1997). Applications such as facial emotion recognition technologies, wearables that can measure your emotional and internal states, social robots interacting with the user by extracting and perhaps generating emotions, voice assistants that can detect your emotional states through modalities such as voice pitch and frequency and so on...
Since these technologies are relatively invasive to our private sphere (feelings), I am trying to find influencing factors that might enhance user acceptance of these types of technologies in everyday life (I am measuring the effects with the TAM). Factors such as trust and privacy might be very obvious, but moderating factors such as gender and age are also very interesting. Furthermore, I need relevant literature which I can ground my work on since I am writing a literature review on this topic.
I am thankful for any kind of help!
I am researching on Affective Algorithmic Music Composition and will like to know more about the factors that influence emotions to be perceived or induced.
Emotion recognition is part of socially emotional skills. The development of these skills is very important in the modern world. Especially, in childhood. We want to use differential-psychological approach to measuring recognition of emotions in childhood. What literature would you recommend studying?
Emotion recognition is a complex and multifaceted construct. What is included in emotion recognition? Which construct should be measured? We want to create an objective test.
What age is the most interesting to consider emotion recognition? How do you think, how can we use the created test?
I am currently working on facial valance and arousal emotion prediction. I found only affect net dataset, but that has so many mislabeled images. Please share me the dataset links for affect emotion recognition.
I need help for a research project I'm doing. I want to know what expressions are expressed in students' faces at class when they don't understand a topic a Professor is explaining. Do you know where I can find information? Any recommendable paper? All help is welcome.
Thank you so much in advance.
We are in the process of developing a multimodel-multisensor wrist band with variety of sensors including Heart monitor, EDA, Accelerometer, body Temperature and others. Please drop a message here if you think that you will be interested in using such device.
I am currently doing "emotional voice conversion" but suffering from a lack of emotional speech database. Is there any emotional speech database that can be downloaded of academic purpose? I have checked a few databases but only have limited linguistic contents or few utterances for each emotion. IEMOCAP has many overlaps which are not suitable for speech synthesis...I would like to know if there is any database has many utterances with different contents for different emotions and with high speech quality/ no overlap?
I'm working on a little project to measure Emotions with different Methods (K-NN, DNN, NN, Random Forest ...)
At the moment I have two data sets, with one of them I train the different methods (public DEAP data set) and then I want to test the results on the other data set.
Now I am trying to get the two data sets to a suitable level and I noticed that the second data set, which was recorded with the EMOTIV EPOC+, has large peaks in different places.
Is it possible that the peaks were caused by the blinking ?
Furthermore, both datasets are recorded with 128Hz and have as unit µV but the EPOC+ dataset has a value range of [-1000 ; 1000] and the DEAP dataset only a value range of [-10; 10], what could be the cause ?
I uploaded a plot from an example of each dataset.
we are trying to build a cnn model to classify emotions from music. we know that only certain neurons will be triggered while identifying positive and negative emotions. But we can not identify the neurons and a way to trigger them.
Our lab are very interested to use different behavioral measures to tax attentional biases in normal and clinical population, our purpose is collect and analyze data to allows build hypothesis about how different cognitive and emotional operations subserves to a specific cognitive and affective processes.
Hello I work with Convolutional Neural Network and LSTM in speech emotion recognition, in my result I see that CNN has shown better performance than the traditional LSTM in my speech recognition .
Normaly LSTM should better in Speech recognition as I use sequential data.
I wonder is there any way to map emotions on the two-dimensional circumplex space mode based on valence and arousal generated from either heart rate or GSR? (or any other biometrics)
I presume there should have coordinates for each of the emotion on the circumplex model, but I couldn't really fine one. I read several papers using self-report questionnaires - so you can say, for instance, (5 ,1) refers to happiness. But what if we use the results from biometrics such as GSR / heart rate / etc?
Social media is one of the top distribution channels for user created content. By analyzing social media platform user shares, like tweets, photos, likes, re-tweets etc. one can see which types of posts receive the most engagement and use that rich information to form marketing, communication, channel and content strategy. We have a project and articles on social media analytics https://www.researchgate.net/project/Multi-Channel-Social-Media-Predictive-Analytics
Conference Paper BUSEM at SemEval-2017 Task 4A Sentiment Analysis with Word E...
Conference Paper Sector based Sentiment Analysis Framework for Social Media v...
However we would like to hear suggestions and alternative recommendations from the researchers who are working on social media analytics.
What are the algorithms / tools / software packages do you recommend for Social Media Analytics ? How do you relate your solution to machine learning or deep learning frameworks?
I have a set of audio files with each audio annotated by >= 5 annotators, with annotations of the valence, activation and dominance (continuous units of affect). I want to measure the inter-rater agreement (and perhaps plot it). What metric is to be used here?
I'm looking for neuroscience article on different type of emotion and how they influence our daily routines behaviours. I'm looking for type of emotion, feeling/affect and causes
Any sugegstion would be much appreciated
We are looking for a large database (200+) of pictures of human faces with neutral facial expression, in order to conduct an experiment on nonverbal learning mechanisms. We have difficulties finding appropriate pictures because we need people in the pictures to be Caucasian, age 20 to 40, with neutral facial expression, and on neutral background. Also, it would be very good if the database is free for research purposes use. Can somebody please suggest the existing database that he or she knows?
Thank you in advance,
My research group needs to annotate emotions through video observations with recordings of students' faces and screens as they interact with educational software. These annotations will be used in algorithms for detecting emotions. These annotations need to be made by observers and not by students themselves. We're struggling to find protocols for emotion annotation from videos. Does anyone know of any protocol with these characteristics or could to help us in this?
Emotion recognition is a broader area in sentiment analysis. In the current scenarioo, most of the research works for emotion recognition is done in terms of using facial images, gestures and using signals like EEG,ECG,EMG and many,,
how about the use of EOG for emotion recognition? Is eye movement based emotion recognition possible with EOG?
It is fact that the human eye tend to show differences for each emotion by means of dilation in pupil
Is it possible to predict human emotion using EOG along..
I want to take a deeper look at the facial expressions of people with profound intellectual and multiple disabilities (PIMD) to analyse their emotional expressions.
For this purpose, I am searching for a ready-to-use software, which combines image processing (e.g., OpenPose, OpenFace) and machine learning. In addition, I would prefer a software that is free (i.e., Open Source) or at least for non-commercial research purposes.
So, I am not looking for methods, but for ready-to-use software, which includes a feature to train my own models. The reason is that every person with PIMD shows very unique behaviour signals and, therefore, you need one model for each emotion of a person.
Finally, I do not need a GUI or visualization, a simple command line application would enough.
A hint would be very helpful.
Climate Change is among the most pressing issues facing human society today. Eventhough the science of climate change is complex, many studies have shown that the burning of fossil fuels is a major contributor. To reduce or mitigate the effects of climate change and create a sustainable society, sustainable development demands that we must move towards a low-carbon society. One sustainable energy technology that has emerged as a potential solution in addition to renewable energy, is carbon capture and storage (CCS). If you are not familiar with CCS, the video below by ZEP can give you an insight.
According to the Intergovernmental Panel on Climate Change report, we are unlikely to meet our climate targets ( such as the Paris climate target) without CCS. The International Energy Agency's reports also show that renewable energy technologies cannot do it alone. CCS is an important part of the portfolio of low-carbon options we need to move our society into a sustainable direction in the short, medium and long-term. What is the problem? Whilst some are thinking CCS is a good solution, others think it is a bad idea. For example, Greenpeace tagged CCS as a scam. Do you think is a scam? I'd like to hear your opinion. Is carbon capture and storage a good idea or not? Give reasons for your opinion.
External link to the main article: https://www.linkedin.com/pulse/carbon-capture-storage-good-idea-eric-buah/?published=t
I am working on facial emotion recognition task, for that I need either dynamic facial images or morphed one as an experimental stimuli (As static images I have already used). For that purpose, please do suggest some concerned database.
I am interested in automatic emotion recognition from human speech signals and would like to consider an interview for emotional speech elicitation. I would appreciate if someone has any recommendation of validated interviews for eliciting discrete emotions. Many thanks in advance!
We are working in emotional recognition of pre-symptomatic patients with Huntington's disease. We have read about the Bell Lysaker Emotion Recognition Task (BLERT) and its strong psychometric properties and we would like to use it in my research.
Has anyone encountered research literature surrounding the connection between emotion recognition or emotional intelligence and lineup identification or facial recognition?
Out of interest and as part of my MSc I am looking to write a "research grant proposal" (as part of an assignment) for a study using TMS to manipulate facial emotional recognition.
I understand from my research that the fusiform gyrus (face area) is not accessible with rTMS but was wondering if there are any other areas/pathways that rTMS can be applied to manipulate facial emotion recognition?
If anyone has any information please you could kindly link it below, I would be very grateful.
I want to implement Deep Retinal Convolution Neural Network for Speech Emotion Recognition given in this paper https://arxiv.org/ftp/arxiv/papers/1707/1707.09917.pdf. The authors of this paper achieved 99% accuracy on IEMOCAP, EMO-DB databases.
What I understood from this paper is that I first have to convert voices in to spectogram by using Data Augmentation Algorithm Based on Retinal Imaging Principle (DAARIP) algorithm and then input these into DCNN.
I am having a hard time breaking down this approach in to easy steps.
For building Speech Emotion Detection and Recognition system, which approach would be better? Hidden Markov Model or Deep Learning (RNN-LSTM) approach? I have to build a SER system and I am confused between the two. If there are better models than these two, kindly tell.
In addition to the question above, I watch a siraj ravel video ( https://www.youtube.com/watch?v=u9FPqkuoEJ8 ) in which he says that previously HMM were state of the art but now Deep Learning is much more accurate. I need a rationale for this statement
This is way outside my field and I wouldn't know where to begin.
I want to compare “neutral” baseline data with data recorded in a test session to finally be able to evaluate arousal/affect of the infant.
Which software would you recommend? Do you have any literature advice?
Any advice would be appreciated!
All the best
Research on the recognition of facial expression of emotions often seems to rely on pictures on which actors show different acted facial emotions (e. g. they are not genuinely surprised or angry, etc.) I was wondering wether results would be different with the use of real "authentic" facial emotions. Does anyone have one or two papers on the subject to suggest?
I have analysed a number of online articles and have their emotion analysis scores along with their sentiment (pos/neg/neutral) and the sentiment value. The fields are: Anger, Disgust, Fear, Sadness and Joy. What I would like to know if it is possible to somehow combine the values of the fields to represent them as one value. I also have comments related to those articles and have their sentiment and emotion scores as well in similar fields.
This would permit me to find a threshold so that I can use it to grade the article and the comments according to that single value. For example, an article might be: Anger=0.100637, Disgust=0.327951, Fear=0.243857, Joy=0.043951 and Sadness=0.364933.
Clearly in this example, the sadness value is the highest followed by disgust, but would it be right to ignore the lower score fields and classify that article as "sadness" related when "disgust" is that close? Would the "sadness" value be representative of that article? And what if another has 0.148988, 0.14043, 0.070271, 0.609123 and 0.103031? Equal parts "anger" and "disgust" but with 60% "Joy"?
My first thought was to have some sort of mean but that would not be accurate at all as the difference in the different scores will certainly be lost.
Can someone please help me a little with this problem? Can all five values somehow be represented as one? Thank you.
I am totally puzzled about how to proceed with the classification step, after feature extraction. Let's start from the beginning.
I designed an emotion recognition system which produces a massive amount of features. The features number varies depend on four parameters. An average number of features are 7000. My observations also depend on the database I use each time. One of them has 132 individuals. So my final features array is feat_vec(132, 7000). At this point in order to save time and optimise my system's accuracy I thought it would be a good idea not to use the whole amount of data as an entry to my classifier but instead to perform dimensional reduction to my feature array. After a couple of weeks of reading, I decided that the Principal Component Analysis was the best option.
I believe that the sequence of the process is: after the features extraction to use cross validation (5 folds in my case), then to perform PCA on the test features and training features respectively and finally to feed my training data and my test data along with my Labels to a classifier.
In order to do what I described above I wrote the following code:
Folds = 5;
PCA_Comp = 50;
Features = Feat_Vec;
Indices = crossvalind('Kfold',Labels,Folds);
for i = 1:Folds
test = (indices == i); train = ~test;
[TEcoeff, TEscore] = pca(Features(test,:));
[TRcoeff, TRscore] = pca(Features(train,:));
% My 1st question is how to calculate the new reduced data arrays
class = classify(Sample_Data,Training_Data,Labels(train));
For my project, I will be using Linear Discriminant Analysis (LDA). The reason I used the above method to classify my data is that I know how to find the accuracy of my system based on the 5-folds CV. I also know that Discriminant Analysis can be performed by <http://uk.mathworks.com/help/stats/discriminant-analysis.html this command/code>. My 2nd question is how do find the model's accuracy using fitcdiscr(X,Y) instead of use the traditional method classify(Xte,Xtr,L)?
Thank you for your time,
I'm looking for a test to evaluate emotional recognition (face, voice and body) in children, with basic emotions, not social/secundary emotions. I think DANVA-2 can be used, but I'm not sure if includes body recognition, and maybe de pictures can be outdated.
I am working on a Facial Emotion Recognition task and for that I need a database that consists of facial emotions from USA sample. Specifically, facial expression should differ in terms of emotions (defined by valence and arousal).
We conducted an experiment measuring automatically emotions in the face during three n-back tasks with different task difficulties. Often, the different emotions show a fast vacillating, i.e. significant changes of the intensity of the emotion with mostly a low amplitude.
In the literature oscillating or vacillating emotions are in most cases defined as changes between emotions. In papers that report about vacillation of the same emotion often the changes are not described as fast as we have observed (within three seconds or less).
Is there a theory about fast vacillating emotions (i.e. fast changes of its amplitude)?
Do you know good literature with more information (e.g. experiments, theory, observations, ...) about fast vacaillating emotions?
I want to use a chi square test on 2 unequal samples. Both are in the hundreds so there is no issue with minimal cell count. I know that unequal sample size is not a problem for chi square test. However, I'm trying to find a stats book or a published article that I could refer to in order to make this argument in a manuscript. Anyone know of such a reference?
We are conducting a meta-analysis on the relationship between emotion recognition ability and intelligence. More specifically, we are seeking to investigate how different facets of cognitive intelligence correlate with people’s ability to accurately detect emotions in others and how test and study features moderate this relationship.
If you have conducted any published or unpublished studies in which you administered performance-based measures of both emotion recognition and cognitive intelligence to non-clinical adults, we would be very happy to include this data in our meta-analysis.
In this case, please write the following information:
Names of the emotion recognition and cognitive intelligence tests used (if the tests were custom-built for your study, please provide a short description), the zero-order correlation(s) between the tests, and study characteristics (sample size, mean age, gender composition, ethnic composition, year of the study, publication status – published or unpublished). If available, please also specify the number of items and Cronbach’s alpha of each test. In case you used tests that consist of different subtests (e.g., a vocal and a facial emotion recognition test; a test battery for different facets of intelligence), preferably provide the correlations for the subtests rather than the total scores.
Thank you in advance for your help!
Katja Schlegel and Judith Hall (Northeastern University)
Marianne Schmid Mast and Tristan Palese (University of Lausanne)
Nora Murphy (Loyola Marymount University)
Thomas Rammsayer (University of Berne)
Dear fellow researcher,
in a project we aim to measure emotions in real-time to loop this information back into a gamification module. We plan to integrate mainly body cues and facial expressions, but at least in the lab we also look at GSR, heartrate and brain signals. Can anyone recommend a solution (software library, development kit etc. - not a lab software) which integrates such signals and generates emotional states? We search for something like the SHORE kit (facial expressions), only with more modalities.
in emotion recognition from static images, we get the Gabor filters for each face part. the returning is matrices in terms of wavelengths and orientations.
How to construct the feature vector from these Gabor filters?
I want to compare our result with existing system of emotion detection in text. Can i get some recent paper on emotion detection in text???
I am looking for a dataset which contains a list of phrases and the emotion associated with it. For example for x="what the hell just happened", y='surprise'.
and, x="no one loves me",y='sad' etc.
Please its kind of urgent.
Assume we have these types of emotion:
1- Hate 2- Hope 3- Fear 4- Love 5- Dislike 6- Relief 7- Anger 8- Admiration 9- Shame 10- Disappointment 11- Resent 12- Joy 13- Like 14- Sadness
What events or actions would change any of these emotions to "Admiration"?
I would appreciate it if you support your answer with authentic references.
OpenCV is normally used for face recognition applications. I want to embed an emotion recognition algorithm on openCV. How to do? Also which version of openCV is suitable for windows 7?
I need to detect small emotional responses on the audio stimuli. Unfortunately, I don't have access to the MRI or other complex technologies to track emotional changes, so I decided to use eye-tracker, but I am not sure it will give any information.
I am new to counseling clients with schizoaffective disorder. I notice they have limited insight, an impoverished model of self, and frequently go off on perseverative tangents. I am looking for ways to hold their attention and boost their level of insight and self-awareness.
I have provided a link to two separate YouTube videos in which each contain three short excerpts of music. One video focuses on orchestral scored music whilst the other focuses on an ambient/soundscape approach to production.
I am looking to gain feedback on whether the work truly reflects the emotions intended. Feel free to leave comments within the YouTube comments box in order to help me improve my work.