1,834 research outputs found
EMPATH: A Neural Network that Categorizes Facial Expressions
There are two competing theories of facial expression recognition. Some researchers have suggested that it is an example of "categorical perception." In this view, expression categories are considered to be discrete entities with sharp boundaries, and discrimination of nearby pairs of expressive faces is enhanced near those boundaries. Other researchers, however, suggest that facial expression perception is more graded and that facial expressions are best thought of as points in a continuous, low-dimensional space, where, for instance, "surprise" expressions lie between "happiness" and "fear" expressions due to their perceptual similarity. In this article, we show that a simple yet biologically plausible neural network model, trained to classify facial expressions into six basic emotions, predicts data used to support both of these theories. Without any parameter tuning, the model matches a variety of psychological data on categorization, similarity, reaction times, discrimination, and recognition difficulty, both qualitatively and quantitatively. We thus explain many of the seemingly complex psychological phenomena related to facial expression perception as natural consequences of the tasks' implementations in the brain
Social-Context Middleware for At-Risk Veterans
Many veterans undergo challenges when reintegrating into civilian society. These challenges include readapting to their communities and families. During the reintegration process veterans have difficulties finding employment, education or resources that aid veteran health. Research suggests that these challenges often result in veterans encountering serious mental illness. Post-Traumatic Stress Disorder (PTSD) is a common mental disease that veterans often develop. This disease impacts between 15-20% of veterans. PTSD increases the likelihood of veterans engaging in high risk behaviors which may consist of impulsivity, substance abuse, and angry outbursts. These behaviors raise the veteransâ risk of becoming violent and lashing out at others around them. In more recent studies the VA has started to define PTSD by its association to specific high risk behaviors rather than defining PTSD based on a combination of psychiatric symptoms. Some researchers have suggested that high risk behaviors -- extreme anger (i.e., rage or angry outbursts) is particularly problematic within the context of military PTSD. Comparatively little research has been done linking sensor based systems to identify these angry episodes in the daily lives of military veterans or others with similar issues. This thesis presents a middleware solution for systems that work to detect, and with additional work possibly prevent, angry outbursts (also described in psychological literature as ârageâ) using physiological sensor data and context-aware technology. This paper will cover a range of topics from methods for collecting system requirements for a subject group to the development of a social-context aware middleware. In doing such, the goal is to present a system that can be constructed and used in an in lab environment to further the research of building real-world systems that predict crisis events, setting the state for early intervention methods based on this approach
Emotion Recognition from Acted and Spontaneous Speech
DizertaÄnĂ prĂĄce se zabĂœvĂĄ rozpoznĂĄnĂm emoÄnĂho stavu mluvÄĂch z ĆeÄovĂ©ho signĂĄlu. PrĂĄce je rozdÄlena do dvou hlavnĂch ÄastĂ, prvnĂ ÄĂĄst popisuju navrĆŸenĂ© metody pro rozpoznĂĄnĂ emoÄnĂho stavu z hranĂœch databĂĄzĂ. V rĂĄmci tĂ©to ÄĂĄsti jsou pĆedstaveny vĂœsledky rozpoznĂĄnĂ pouĆŸitĂm dvou rĆŻznĂœch databĂĄzĂ s rĆŻznĂœmi jazyky. HlavnĂmi pĆĂnosy tĂ©to ÄĂĄsti je detailnĂ analĂœza rozsĂĄhlĂ© ĆĄkĂĄly rĆŻznĂœch pĆĂznakĆŻ zĂskanĂœch z ĆeÄovĂ©ho signĂĄlu, nĂĄvrh novĂœch klasifikaÄnĂch architektur jako je napĆĂklad âemoÄnĂ pĂĄrovĂĄnĂâ a nĂĄvrh novĂ© metody pro mapovĂĄnĂ diskrĂ©tnĂch emoÄnĂch stavĆŻ do dvou dimenzionĂĄlnĂho prostoru. DruhĂĄ ÄĂĄst se zabĂœvĂĄ rozpoznĂĄnĂm emoÄnĂch stavĆŻ z databĂĄze spontĂĄnnĂ ĆeÄi, kterĂĄ byla zĂskĂĄna ze zĂĄznamĆŻ hovorĆŻ z reĂĄlnĂœch call center. Poznatky z analĂœzy a nĂĄvrhu metod rozpoznĂĄnĂ z hranĂ© ĆeÄi byly vyuĆŸity pro nĂĄvrh novĂ©ho systĂ©mu pro rozpoznĂĄnĂ sedmi spontĂĄnnĂch emoÄnĂch stavĆŻ. JĂĄdrem navrĆŸenĂ©ho pĆĂstupu je komplexnĂ klasifikaÄnĂ architektura zaloĆŸena na fĂșzi rĆŻznĂœch systĂ©mĆŻ. PrĂĄce se dĂĄle zabĂœvĂĄ vlivem emoÄnĂho stavu mluvÄĂho na ĂșspÄĆĄnosti rozpoznĂĄnĂ pohlavĂ a nĂĄvrhem systĂ©mu pro automatickou detekci ĂșspÄĆĄnĂœch hovorĆŻ v call centrech na zĂĄkladÄ analĂœzy parametrĆŻ dialogu mezi ĂșÄastnĂky telefonnĂch hovorĆŻ.Doctoral thesis deals with emotion recognition from speech signals. The thesis is divided into two main parts; the first part describes proposed approaches for emotion recognition using two different multilingual databases of acted emotional speech. The main contributions of this part are detailed analysis of a big set of acoustic features, new classification schemes for vocal emotion recognition such as âemotion couplingâ and new method for mapping discrete emotions into two-dimensional space. The second part of this thesis is devoted to emotion recognition using multilingual databases of spontaneous emotional speech, which is based on telephone records obtained from real call centers. The knowledge gained from experiments with emotion recognition from acted speech was exploited to design a new approach for classifying seven emotional states. The core of the proposed approach is a complex classification architecture based on the fusion of different systems. The thesis also examines the influence of speakerâs emotional state on gender recognition performance and proposes system for automatic identification of successful phone calls in call center by means of dialogue features.
Robust Modeling of Epistemic Mental States
This work identifies and advances some research challenges in the analysis of
facial features and their temporal dynamics with epistemic mental states in
dyadic conversations. Epistemic states are: Agreement, Concentration,
Thoughtful, Certain, and Interest. In this paper, we perform a number of
statistical analyses and simulations to identify the relationship between
facial features and epistemic states. Non-linear relations are found to be more
prevalent, while temporal features derived from original facial features have
demonstrated a strong correlation with intensity changes. Then, we propose a
novel prediction framework that takes facial features and their nonlinear
relation scores as input and predict different epistemic states in videos. The
prediction of epistemic states is boosted when the classification of emotion
changing regions such as rising, falling, or steady-state are incorporated with
the temporal features. The proposed predictive models can predict the epistemic
states with significantly improved accuracy: correlation coefficient (CoERR)
for Agreement is 0.827, for Concentration 0.901, for Thoughtful 0.794, for
Certain 0.854, and for Interest 0.913.Comment: Accepted for Publication in Multimedia Tools and Application, Special
Issue: Socio-Affective Technologie
Classification of Physiological Signals for Emotion Recognition using IoT
Emotion recognition gains huge popularity now a days. Physiological signals provides an appropriate way to detect human emotion with the help of IoT. In this paper, a novel system is proposed which is capable of determining the emotional status using physiological parameters, including design specification and software implementation of the system. This system may have a vivid use in medicine (especially for emotionally challenged people), smart home etc. Various Physiological parameters to be measured includes, heart rate (HR), galvanic skin response (GSR), skin temperature etc. To construct the proposed system the measured physiological parameters were feed to the neural networks which further classify the data in various emotional states, mainly in anger, happy, sad, joy. This work recognized the correlation between human emotions and change in physiological parameters with respect to their emotion
Speech-based recognition of self-reported and observed emotion in a dimensional space
The differences between self-reported and observed emotion have only marginally been investigated in the context of speech-based automatic emotion recognition. We address this issue by comparing self-reported emotion ratings to observed emotion ratings and look at how differences between these two types of ratings affect the development and performance of automatic emotion recognizers developed with these ratings. A dimensional approach to emotion modeling is adopted: the ratings are based on continuous arousal and valence scales. We describe the TNO-Gaming Corpus that contains spontaneous vocal and facial expressions elicited via a multiplayer videogame and that includes emotion annotations obtained via self-report and observation by outside observers. Comparisons show that there are discrepancies between self-reported and observed emotion ratings which are also reflected in the performance of the emotion recognizers developed. Using Support Vector Regression in combination with acoustic and textual features, recognizers of arousal and valence are developed that can predict points in a 2-dimensional arousal-valence space. The results of these recognizers show that the self-reported emotion is much harder to recognize than the observed emotion, and that averaging ratings from multiple observers improves performance
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