Searched for: subject%3A%22Emotion%255C+recognition%22
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Rawat, Aditi (author)
Automatic affect prediction systems usually assume its underlying affect representation scheme (ARS). This systematic review aims to explore how different ARS are used for in affect prediction systems based on spoken input. The focus is only on the audio input from speakers. Various datasets for speech emotion recognition were also involved in...
bachelor thesis 2023
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Datcu, D. (author), Rothkrantz, L.J.M. (author)
The recognition of the internal emotional state of one person plays an important role in several human-related fields. Among them, human-computer interaction has recently received special attention. The current research is aimed at the analysis of segmentation methods and of the performance of the GentleBoost classifier on emotion recognition...
conference paper 2006
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Dudzik, B.J.W. (author), Broekens, D.J. (author), Neerincx, M.A. (author), Hung, H.S. (author)
Empirical evidence suggests that the emotional meaning of facial behavior in isolation is often ambiguous in real-world conditions. While humans complement interpretations of others' faces with additional reasoning about context, automated approaches rarely display such context-sensitivity. Empirical findings indicate that the personal...
conference paper 2020
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Pons, Gerard (author), Ali, Abdallah El (author), Cesar, Pablo (author)
Facial thermal imaging has in recent years shown to be an efficient modality for facial emotion recognition. However, the use of deep learning in this field is still not fully exploited given the small number and size of the current datasets. The goal of this work is to improve the performance of the existing deep networks in thermal facial...
conference paper 2020
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Ruan, Xingran (author), Palansuriya, Charaka (author), Constantin, Aurora (author), Tsiakas, K. (author)
The present study aims to investigate the relationship between emotions experienced during learning and metacognition in typically developing (TD) children and those with autism spectrum disorder (ASD). This will assist us in using machine learning (ML) to develop a facial emotion recognition (FER) based intelligent tutor system (ITS) to...
conference paper 2023
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Rao, Shruti (author), Ghosh, Surjya (author), Pons Rodriguez, Gerard (author), Röggla, Thomas (author), El Ali, Abdallah (author), Cesar, Pablo (author)
Automatically inferring drivers' emotions during driver-pedestrian interactions to improve road safety remains a challenge for designing in-vehicle, empathic interfaces. To that end, we carried out a lab-based study using a combination of camera and physiological sensors. We collected participants' (N=21) real-time, affective (emotion self...
conference paper 2022
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Rao, Shruti (author), Wirjopawiro, Sabrina (author), Pons Rodriguez, Gerard (author), Röggla, Thomas (author), Cesar, Pablo (author), El Ali, Abdallah (author)
Eliciting and capturing drivers' affective responses in a realistic outdoor setting with pedestrians poses a challenge when designing in-vehicle, empathic interfaces. To address this, we designed a controlled, outdoor car driving circuit where drivers (N=27) drove and encountered pedestrian confederates who performed non-verbal positive or...
conference paper 2023
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Katsimerou, C. (author)
Affect-adaptive systems have the potential to assist users that experience systematically negative moods. This thesis aims at building a platform for predicting automatically a person’s mood from his/her visual expressions. The key word is mood, namely a relatively long-term, stable and diffused affective state, as opposed to the short-term,...
doctoral thesis 2016
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Datcu, D. (author)
This thesis proposes algorithms and techniques to be used for automatic recognition of six prototypic emotion categories by computer programs, based on the recognition of facial expressions and emotion patterns in voice. Considering the applicability in real-life conditions, the research is carried in the context of devising person independent...
doctoral thesis 2009
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Dudzik, B.J.W. (author)
doctoral thesis 2021
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Zhang, T. (author)
Fine-grained emotion recognition is the process of automatically identifying the emotions of users at a fine granularity level, typically in the time intervals of 0.5s to 4s according to the expected duration of emotions. Previous work mainly focused on developing algorithms to recognize only one emotion for a video based on the user feedback...
doctoral thesis 2022
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Lefter, I. (author), Baird, Alice (author), Stappen, Lukas (author), Schuller, Björn W. (author)
The monitoring of an escalating negative interaction has several benefits, particularly in security, (mental) health, and group management. The speech signal is particularly suited to this, as aspects of escalation, including emotional arousal, are proven to easily be captured by the audio signal. A challenge of applying trained systems in real...
journal article 2022
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Zhang, T. (author), El Ali, Abdallah (author), Hanjalic, A. (author), Cesar, Pablo (author)
Fine-grained emotion recognition can model the temporal dynamics of emotions, which is more precise than predicting one emotion retrospectively for an activity (e.g., video clip watching). Previous works require large amounts of continuously annotated data to train an accurate recognition model, however experiments to collect such large...
journal article 2022
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Zhang, T. (author), Ali, Abdallah El (author), Chen, C. (author), Hanjalic, A. (author), Cesar, Pablo (author)
Recognizing user emotions while they watch short-form videos anytime and anywhere is essential for facilitating video content customization and personalization. However, most works either classify a single emotion per video stimuli, or are restricted to static, desktop environments. To address this, we propose a correlation-based emotion...
journal article 2020
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Rao, Shruti (author), Ghosh, Surjya (author), Rodriguez, Gerard Pons (author), Röggla, Thomas (author), Cesar, Pablo (author), El Ali, Abdallah (author)
Capturing drivers’ affective responses given driving context and driver-pedestrian interactions remains a challenge for designing in-vehicle, empathic interfaces. To address this, we conducted two lab-based studies using camera and physiological sensors. Our first study collected participants’ (N = 21) emotion self-reports and physiological...
journal article 2023
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Zhang, T. (author), El Ali, Abdallah (author), Wang, Chen (author), Hanjalic, A. (author), Cesar, Pablo (author)
Instead of predicting just one emotion for one activity (e.g., video watching), fine-grained emotion recognition enables more temporally precise recognition. Previous works on fine-grained emotion recognition require segment-by-segment, fine-grained emotion labels to train the recognition algorithm. However, experiments to collect these...
journal article 2023
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de Blok, Layla (author)
Grief is a complex experience without a specific endpoint. It follows a unique path for each person. Many support sources are available, but finding support that fits is difficult. This inspired Verening Leven met Dood to develop the Rouw Wegwijzer, an online platform that provides personalised grief support. The focus of this thesis is to...
master thesis 2023
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Albeda, J. (author)
Affect-adaptive systems are dependent on their ability to automatically recognize a user’s affective state. This study aims to contribute to the creation of an affect-adaptive system that can recognize negative moods of elderly in care homes from a video feed, and improve it by adapting the lighting in the room. An affective database of videos...
master thesis 2016
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Sun, X. (author)
This thesis describes a Bayesian Network (BN) model for recognizing the “Action Units (AUs)” of a facial expression using video sequence images as input. Features were extracted by using an optimal estimation optical flow method coupled with a physical (muscle) model describing the facial structure. The muscle action patterns are used for...
master thesis 2009
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Takapoui, P. (author)
In our life we get more and more dependent on our computer and we have less time for face-to-face social activities with friends and families. In face?to?face communication our faces convey lots of emotions using facial expressions and lots of information is transmitted faster non-verbally than verbally, through the facial expressions. Using...
master thesis 2009
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