1,646 research outputs found
Discovering Gender Differences in Facial Emotion Recognition via Implicit Behavioral Cues
We examine the utility of implicit behavioral cues in the form of EEG brain
signals and eye movements for gender recognition (GR) and emotion recognition
(ER). Specifically, the examined cues are acquired via low-cost, off-the-shelf
sensors. We asked 28 viewers (14 female) to recognize emotions from unoccluded
(no mask) as well as partially occluded (eye and mouth masked) emotive faces.
Obtained experimental results reveal that (a) reliable GR and ER is achievable
with EEG and eye features, (b) differential cognitive processing especially for
negative emotions is observed for males and females and (c) some of these
cognitive differences manifest under partial face occlusion, as typified by the
eye and mouth mask conditions.Comment: To be published in the Proceedings of Seventh International
Conference on Affective Computing and Intelligent Interaction.201
Recognising Complex Mental States from Naturalistic Human-Computer Interactions
New advances in computer vision techniques will revolutionize the way we interact with computers, as they, together with other improvements, will help us build machines that understand us better. The face is the main non-verbal channel for human-human communication and contains valuable information about emotion, mood, and mental state. Affective computing researchers have investigated widely how facial expressions can be used for automatically recognizing affect and mental states. Nowadays, physiological signals can be measured by video-based techniques, which can also be utilised for emotion detection. Physiological signals, are an important indicator of internal feelings, and are more robust against social masking. This thesis focuses on computer vision techniques to detect facial expression and physiological changes for recognizing non-basic and natural emotions during human-computer interaction. It covers all stages of the research process from data acquisition, integration and application. Most previous studies focused on acquiring data from prototypic basic emotions acted out under laboratory conditions. To evaluate the proposed method under more practical conditions, two different scenarios were used for data collection. In the first scenario, a set of controlled stimulus was used to trigger the user’s emotion. The second scenario aimed at capturing more naturalistic emotions that might occur during a writing activity. In the second scenario, the engagement level of the participants with other affective states was the target of the system. For the first time this thesis explores how video-based physiological measures can be used in affect detection. Video-based measuring of physiological signals is a new technique that needs more improvement to be used in practical applications. A machine learning approach is proposed and evaluated to improve the accuracy of heart rate (HR) measurement using an ordinary camera during a naturalistic interaction with computer
The MuSe 2022 Multimodal Sentiment Analysis Challenge: Humor, Emotional Reactions, and Stress
The Multimodal Sentiment Analysis Challenge (MuSe) 2022 is dedicated to
multimodal sentiment and emotion recognition. For this year's challenge, we
feature three datasets: (i) the Passau Spontaneous Football Coach Humor
(Passau-SFCH) dataset that contains audio-visual recordings of German football
coaches, labelled for the presence of humour; (ii) the Hume-Reaction dataset in
which reactions of individuals to emotional stimuli have been annotated with
respect to seven emotional expression intensities, and (iii) the Ulm-Trier
Social Stress Test (Ulm-TSST) dataset comprising of audio-visual data labelled
with continuous emotion values (arousal and valence) of people in stressful
dispositions. Using the introduced datasets, MuSe 2022 2022 addresses three
contemporary affective computing problems: in the Humor Detection Sub-Challenge
(MuSe-Humor), spontaneous humour has to be recognised; in the Emotional
Reactions Sub-Challenge (MuSe-Reaction), seven fine-grained `in-the-wild'
emotions have to be predicted; and in the Emotional Stress Sub-Challenge
(MuSe-Stress), a continuous prediction of stressed emotion values is featured.
The challenge is designed to attract different research communities,
encouraging a fusion of their disciplines. Mainly, MuSe 2022 targets the
communities of audio-visual emotion recognition, health informatics, and
symbolic sentiment analysis. This baseline paper describes the datasets as well
as the feature sets extracted from them. A recurrent neural network with LSTM
cells is used to set competitive baseline results on the test partitions for
each sub-challenge. We report an Area Under the Curve (AUC) of .8480 for
MuSe-Humor; .2801 mean (from 7-classes) Pearson's Correlations Coefficient for
MuSe-Reaction, as well as .4931 Concordance Correlation Coefficient (CCC) and
.4761 for valence and arousal in MuSe-Stress, respectively.Comment: Preliminary baseline paper for the 3rd Multimodal Sentiment Analysis
Challenge (MuSe) 2022, a full-day workshop at ACM Multimedia 202
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