7,242 research outputs found
Ensemble of Hankel Matrices for Face Emotion Recognition
In this paper, a face emotion is considered as the result of the composition
of multiple concurrent signals, each corresponding to the movements of a
specific facial muscle. These concurrent signals are represented by means of a
set of multi-scale appearance features that might be correlated with one or
more concurrent signals. The extraction of these appearance features from a
sequence of face images yields to a set of time series. This paper proposes to
use the dynamics regulating each appearance feature time series to recognize
among different face emotions. To this purpose, an ensemble of Hankel matrices
corresponding to the extracted time series is used for emotion classification
within a framework that combines nearest neighbor and a majority vote schema.
Experimental results on a public available dataset shows that the adopted
representation is promising and yields state-of-the-art accuracy in emotion
classification.Comment: Paper to appear in Proc. of ICIAP 2015. arXiv admin note: text
overlap with arXiv:1506.0500
Affective Facial Expression Processing via Simulation: A Probabilistic Model
Understanding the mental state of other people is an important skill for
intelligent agents and robots to operate within social environments. However,
the mental processes involved in `mind-reading' are complex. One explanation of
such processes is Simulation Theory - it is supported by a large body of
neuropsychological research. Yet, determining the best computational model or
theory to use in simulation-style emotion detection, is far from being
understood.
In this work, we use Simulation Theory and neuroscience findings on
Mirror-Neuron Systems as the basis for a novel computational model, as a way to
handle affective facial expressions. The model is based on a probabilistic
mapping of observations from multiple identities onto a single fixed identity
(`internal transcoding of external stimuli'), and then onto a latent space
(`phenomenological response'). Together with the proposed architecture we
present some promising preliminary resultsComment: Annual International Conference on Biologically Inspired Cognitive
Architectures - BICA 201
Emotions in context: examining pervasive affective sensing systems, applications, and analyses
Pervasive sensing has opened up new opportunities for measuring our feelings and understanding our behavior by monitoring our affective states while mobile. This review paper surveys pervasive affect sensing by examining and considering three major elements of affective pervasive systems, namely; “sensing”, “analysis”, and “application”. Sensing investigates the different sensing modalities that are used in existing real-time affective applications, Analysis explores different approaches to emotion recognition and visualization based on different types of collected data, and Application investigates different leading areas of affective applications. For each of the three aspects, the paper includes an extensive survey of the literature and finally outlines some of challenges and future research opportunities of affective sensing in the context of pervasive computing
Affective games:a multimodal classification system
Affective gaming is a relatively new field of research that exploits human emotions to influence gameplay for an enhanced player experience. Changes in player’s psychology reflect on their behaviour and physiology, hence recognition of such variation is a core element in affective games. Complementary sources of affect offer more reliable recognition, especially in contexts where one modality is partial or unavailable. As a multimodal recognition system, affect-aware games are subject to the practical difficulties met by traditional trained classifiers. In addition, inherited game-related challenges in terms of data collection and performance arise while attempting to sustain an acceptable level of immersion. Most existing scenarios employ sensors that offer limited freedom of movement resulting in less realistic experiences. Recent advances now offer technology that allows players to communicate more freely and naturally with the game, and furthermore, control it without the use of input devices. However, the affective game industry is still in its infancy and definitely needs to catch up with the current life-like level of adaptation provided by graphics and animation
Machine Analysis of Facial Expressions
No abstract
A dataset of continuous affect annotations and physiological signals for emotion analysis
From a computational viewpoint, emotions continue to be intriguingly hard to
understand. In research, direct, real-time inspection in realistic settings is
not possible. Discrete, indirect, post-hoc recordings are therefore the norm.
As a result, proper emotion assessment remains a problematic issue. The
Continuously Annotated Signals of Emotion (CASE) dataset provides a solution as
it focusses on real-time continuous annotation of emotions, as experienced by
the participants, while watching various videos. For this purpose, a novel,
intuitive joystick-based annotation interface was developed, that allowed for
simultaneous reporting of valence and arousal, that are instead often annotated
independently. In parallel, eight high quality, synchronized physiological
recordings (1000 Hz, 16-bit ADC) were made of ECG, BVP, EMG (3x), GSR (or EDA),
respiration and skin temperature. The dataset consists of the physiological and
annotation data from 30 participants, 15 male and 15 female, who watched
several validated video-stimuli. The validity of the emotion induction, as
exemplified by the annotation and physiological data, is also presented.Comment: Dataset available at:
https://rmc.dlr.de/download/CASE_dataset/CASE_dataset.zi
Emotional Brain-Computer Interfaces
Research in Brain-computer interface (BCI) has significantly increased during the last few years. In addition to their initial role as assisting devices for the physically challenged, BCIs are now proposed for a wider range of applications. As in any HCI application, BCIs can also benefit from adapting their operation to the emotional state of the user. BCIs have the advantage of having access to brain activity which can provide signicant insight into the user's emotional state. This information can be utilized in two manners. 1) Knowledge of the inuence of the emotional state on brain activity patterns can allow the BCI to adapt its recognition algorithms, so that the intention of the user is still correctly interpreted in spite of signal deviations induced by the subject's emotional state. 2) The ability to recognize emotions can be used in BCIs to provide the user with more natural ways of controlling the BCI through affective modulation. Thus, controlling a BCI by recollecting a pleasant memory can be possible and can potentially lead to higher information transfer rates.\ud
These two approaches of emotion utilization in BCI are elaborated in detail in this paper in the framework of noninvasive EEG based BCIs
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