An Adaptable Fuzzy Emotion Model for Emotion Recognition
|
|
- Gervais Garrett
- 5 years ago
- Views:
Transcription
1 An Adaptable Fuzzy Emotion Model for Emotion Recognition Natascha Esau Lisa Kleinjohann C-LAB Fuerstenallee 11 D Paderborn, Germany {nesau, lisa, Bernd Kleinjohann Abstract Existing emotion recognition applications usually distinguish between a small number of emotions. However this set of so called basic emotions varies from one application to another depending on their according needs. In order to support such differing application needs an adaptable emotion model based on the fuzzy hypercube is presented. In addition to existing models it supports also the recognition of derived emotions which are combinations of basic emotions. We show the application of this model by a prosody based fuzzy emotion recognition system. Keywords: Fuzzy emotion model, fuzzy hypercube, fuzzy emotion recognition, basic emotion. 1 Introduction Emotions are an evident part of interactions between human beeings. But also for interactions of humans with computer systems emotions play a major role, since humans can never entirely switch off their emotions. During the last years interest in emotions increased considerably in various domains of computer based systems. Examples are robots or virtual agents that show emotions or human-computer interfaces that consider human emotions in their interaction capabilities. In Japan an entire stream called KANSEI information processing [10] deals with subjective human feelings when interacting with IT systems. These few examples already reveal two major tasks of emotion processing in IT systems, the recognition of human emotions and the (re)production of artificial emotions. Whereas robots or virtual agents often show emotions themselves, for many other IT systems the recognition of human emotions and appropriate reactions suffice to improve the system s performance or acceptance. Imagine for instance a user who is angry, because the IT system does not behave in the expected way or she tried several times to accomplish a task without success. In such a situation it would be very helpful and increase the system acceptance, if an IT system could recognize this emotion and react accordingly. Another example is speech recognition. According to investigations at the MIT a doubling of the word error rate to about 32% was observed when people talk in an angry way [4]. In such a case an appropriate system reaction would be to redirect the user to a human operator or to give hints how she could decrease the error rate. Depending on the intended application domain different emotions are relevant for emotion recognition. According to the observation described above, the speech recognition system Mercury distinguishes only two classes of emotions: frustration and neutral. For other applications like personal robots or entertainment robots certainly the recognition of some more emotions like happiness or sadness would be interesting to react with according robot behavior. Since also psychologists have not yet agreed upon a set of basic emotions (see Section 2) it is not likely to identify a set of emotions, that is appropriate for all computer based emotion recognition (CBER) systems. Therefore, in this paper we propose an emotion model for emotion recognition that is easily adaptable to the selected set of basic emotions for the CBER problem (see Section 3). However humans do not only feel some basic emotions in their pure form but also some more complex or derived emotions [16]. An example is for instance curiosity which is according to experiments by Plutchik a combination of acceptance and surprise. Accounting for this observation our emotion model does not only support basic emo- 73
2 tions but also supports the representation of such derived emotions or blends. Furthermore different intensities or degrees of emotions can be observed [16]. For modelling of different degrees of membership to different classes in a classification system in many application domains, among them also emotion recognition, fuzzy logic is a very useful approach. Therefore we developed our adaptable emotion model using the fuzzy hypercube as basis (see Section 3). We show the applicability of our approach by a system for emotion recognition from prosody of natural speech in Section 4. Afterwards we compare our approach with related work and give a short conclusion. 2 Emotion Models in Psychology Psychologists have tried to explain the nature of human emotions for decades or even centuries. Nevertheless no unique established emotion model exists. However emotion is now usually seen as a dynamic process that involves several modalities like motoric expression, physiological arousal and subjective feeling [13, 9]. For computer based emotion recognition (CBER), however models that help with the classification of emotions are more important. Among these two major types of emotion models can be distinguished (also mixtures of these types are found): models that rely on basic emotions and emotion models that classify emotions according to different dimensions like valence, potency, arousal, intensity etc. The first one has a major advantage for CBER, since it considerably decreases recognition complexity due to a small number of basic emotions to which the CBER can be restricted. A well known earlier model of basic emotions is the work of Plutchik [16]. He uses basic emotions as a kind of building block for derived emotions, so called secondary emotions. Plutchik even distinguishes ternary emotions that are combinations of secondary derived emotions. His model like many others also describes the concept of emotion intensity, that represents the strength by which an emotion is felt. Although emotion models exist for several decades, even now there is no general agreement among psychologists how many basic emotions exist and what they are. This is shown in table 1 which is an excerpt from [15]. Due to the variety of basic emotions described in liter- Table 1: Basic emotions distinguished by psychologists Psychologist Plutchik Ekman, Friesen, Ellsworth Frijda Izard James Mowrer, contempt, disgust, distress, fear, guilt, interest, joy, shame, surprise Oatley and Johnson- Laird Basic Emotions Acceptance, anger, anticipaton, disgust, joy, fear, sadness, surprise, disgust, fear, joy, sadness, surprise Desire, happiness, interest, surprise, wonder, sorrow Fear, grief, love, rage Pain, pleasure, disgust, anxiety, happiness, sadness ature it seems reasonable to develop an emotion model for emotion recognition that is easily adaptable to the selected set of basic emotions for the CBER problem. 3 Fuzzy Emotion Model As already stated, according to psychologists like Plutchik humans do not only feel a single basic emotion but have more complex emotional states, where more than one basic emotion is involved with varying strength or intensity. Therefore we propose a fuzzy classification of emotional states using fuzzy hypercubes [12]. Furthermore we assume that the intensity of an emotion can be mapped to the interval [0,1]. First we define a fuzzy set corresponding to an emotional state and then show how it is represented in a fuzzy emotion hypercube. Fuzzy set for emotional state. Let BE be a finite base set of n basic emotions e 1,e 2,... e n and {µ FEj : BE [0,1],j = 1,2,...} an infinite set of fuzzy membership functions. Then each FE j := {(e i,µ FEj (e i ) e i BE},j = 1,2,... defines a fuzzy set corresponding to one emotional state E j. Fuzzy emotion hypercube. If BE, µ FEj and FE j are defined as described above, we shall use the mem- 74
3 bership vector (µ FEj (e 1 ),µ FEj (e 2 ),...,µ FEj (e n )) =: (µ FEj (e i )) to denote a point in an n-dimensional hypercube. Each axis of the hypercube corresponds to one basic emotion e i. Thus a membership vector (µ FEj (e i )) corresponds to one emotional state E j and can be interpreted psychologically as vector of emotion intensities (I ei ) := (I e1,i e2,...,i en ). The number of distinguished emotions depends on the psychological theory or in the case of computer based emotion recognition on the intended application. If for instance the three basic emotions happiness h, anger a and surprise s shall be distinguished, a three dimensional unit cube as depicted in Figure 1 is needed for modelling emotional states. (0,0,1) (0,0,0) (0,1,0) E 2 E 1 Happiness (1,0,0) Figure 1: Fuzzy unit cube for three emotions happiness, suprise and anger The corners in the unit cube describe dual memberships (0 or 1) for all emotions, vertices desribe dual memberships for two emotions and the third one varies from 0 to 1. For example, the point E 1 = (1.0, 0.2, 0.3) corresponding to the fuzzy set FE 1 = {(h,1.0),(a,0.2), (s,0.3)} represents a happy emotional state. The point E 2 = (0.2,1.0,0.9) corresponding to FE 2 = {(h,0.2),(a,1.0),(s,0.9)} certainly represents an emotional state for a derived emotion from anger and suprise. The point (0, 0, 0) represents the entirely neutral state where no emotion is present. Neutral + Happin. Happin Happin. + Happin. Figure 2: Subdivisions of unit cube representing basic and derived emotions Figure 2 shows how the unit cube could be further divided in order to represent basic emotions and their mixtures. In the subcubes denoted by a single emotion the membership function of this emotion takes values in the interval [0.5, 1.0] whereas the membership values for the other emotions respectively their intensities are below 0.5. Therfore it is reasonable to associate the subcube with this basic emotion. In the subcubes denoted with a sum of emotions (e.g. + Happiness) memberships of these emotions are in the interval [0.5,1.0] whereas the membership of the third emotion is below 0.5. Hence a derived emotion from these two basic emotions (e.g. surprise and happiness) is assumed. The subcube where the membership values of all basic emotions are between 0.5 and 1.0 is denoted by the sum + + Happiness. If a general n-dimensional emotion hypercube is regarded certainly not all combinations of up to n emotions make sense. However, whether a combination is reasonable or not is certainly a psychological question. If combinations that do not make sense are recognized by a CBER this could for instance indicate an error. 4 Application This section deals with the application of our adaptable emotion model for the fuzzy rule based emotion recognition system PROSBER [2]. 4.1 Overview of PROSBER PROSBER recognizes emotions from the prosody of natural speech. It takes single sentences as input and classifies them into the emotion categories happiness, sadness, anger and fear. Furthermore a neutral emotional state is distinguished. PROSBER automatically generates the fuzzy models for emotion recognition. Accordingly two working modes are distinguished, training and recognition, as depicted in Figure 3. During the training the training samples with wellknown emotion values are used to create the fuzzy models for the individual emotions. For that purpose sequences of acoustic parameters like fundmental frequency or jitter are extracted. PROSBER extracts about twenty parameters that have shown their relevance for emotion recognition in psychological stud- 75
4 Speech signal Speech signal Preprocessing Training Preprocessing Recognition Frames Frames extraction extraction sequences sequences vectors calculation vectors calculation Emotions of training samples Fuzzy model generation Membership functions generation Membership functions selection Fuzzy rules Fuzzy classification Figure 3: Architecture of PROSBER Fuzzy rule construction Emotion ies or in other speech based emotion recognition systems. The sequences of these acoustic parameters are summarized by statistical analysis steps performed by the feature calculation. The fuzzy model generation is based on a fuzzy grid approach [11]. It performs the following three steps on the training database. First the membership functions for every feature are generated. Afterwards for each emotion up to six most significant features are selected and then the fuzzy rule system for each emotion is generated. These fuzzy models are used in the emotion recognition process to classify unknown audio data. A detailed description of PROSBER can be found in [2]. 4.2 Fuzzy Emotion Recognition in PROSBER In order to describe the application of our emotion model we shall now have a closer look at the fuzzy classification. In principle it is structured as depicted in Figure 4. vectors Emotion 1 Emotion n Fuzzification.. Rule Base Emotion n Fuzzy Inference Defuzzification Emotion 1 intensity.. Emotion n intensity Max Emotion Figure 4: Principle structure of fuzzy classification For each basic emotion e i,i = 1,...,n, a separate rule set is generated by an adapted fuzzy grid method. Each rule takes the fuzzified features f j,j = 1,...,K,K 6, as input and produces a fuzzy emotion value I ei as output. We represent each feature f j and emotion intensity I ei by five triangular membership functions verylow, low, medium, high and veryhigh as schematically depicted in Figure 5. However, the actual start and end coordinates as well as the maximum coordinates are generated automatically during the training phase. This representation is simple enough to support real time emotion recognition, yet allows to distinguish degrees to which a feature or emotion is present in the current input sentence. Furthermore, it is in line with psychologists approaches who often use two up to ten levels for characterizing psychological phenomena like emotion intensities. very low low med very high high Figure 5: Membership functions for features and emotions The rule set for the emotion e i is generated by a fuzzy grid approach [11]. Since this approach uses only the AND connector it generates 5 K+1 rules of the following form: IF f 1 IS verylow AND... AND f K IS verylow THEN I ei IS veryhigh IF f 1 IS verylow AND... AND f K IS low THEN I ei IS veryhigh IF f 1 IS verylow AND... AND f K IS medium THEN I ei IS medium... The number of rules could be reduced, if the OR connector or rule pruning could be used. However, both features are not yet supported by the fuzzy library we use. For defuzzification of emotion values we use the center of gravity (COG) method. By projecting the COG to the x-axis we calculate the corresponding emotion intensity. Hence a four dimensional vector (I h,i s,i a,i f ) = (µ E (h),µ E (s),µ E (a),µ E (f)) containing the intensities of the four emotions happiness h, sadness s, anger a, and fear f is generated. This vector represents the membership values for each 76
5 emotion and hence determines a point in the four dimensional emotion hypercube. Presently PROSBER recognizes a single basic emotion. In order to select this emotion we determine the emotion e rec {h,s,a,f} with maximum intensity I erec = max{i h,i s,i a,i f }. If the maximum cannot be determined unambiguously, since two or more intensity values are maximal, that emotion is selected which was recognized for the previous sentence. The neutral emotional state is identified by a hypercube part near to the origin as depicted in Figure 2. We plan to extend PROSBER for recognition of combined emotions as described above. 5 Related Work Up to now a variety of emotion models have been described in literature. They are mainly dedicated to computer based emotion (re)production or simulation in different application domains. A broad application domain are virtual agents, that show (pseudo)emotional behavior in their communication with humans [3, 8, 5, 7]. They rely on a dimensional model of emotions based on the event-appraisal emotion model of Ortony et al. [14]. They usually distinguish the dimensions pleasure, arousal and dominance and try to maintain their dynamics over time. The PETEEI system [7] for simulation of a pet s evolving emotional intelligence similarly to our system uses fuzzy sets for emotion representation. However this system is different from our approach since it associates certain types of events with positive or negative feelings in order to react with according emotions whereas our approach is dedicated to emotion recognition from certain features (of speech, facial expression etc.). Emotion recognition as investigated in our approach is to some extent covered by Kismet [6]. However Kismet recognizes intentions rather than emotions. The emotional system developed for AIBO and SDR [1] like our model uses basic emotions. Since it is intended for production of emotional behavior it uses the dimensions pleasure and arousal as mentioned above. But the dominance dimension is substituted by a confidence dimension representing the certainty of recognized external stimuli. The models described above only deal with single emotions and do not allow to represent combinations or blends of emotions like our approach. The Cathexis model [17] supports this feature and is also adaptable to different sets of basic emotions or emotion families as they are called there by supporting the coexistence of several active so called emotion proto-specialists representing different emotion families. However Cathexis is also dedicated to the (re)production of emotional behavior in synthetic agents whereas our adaptable emotion model is intended for emotion recognition. 6 Conclusion and Outlook This paper presented an adaptable emotion model for emotion recognition. It uses the concept of an n- dimensional fuzzy hypercube to represent emotional states made up of n basic emotions. In contrast to other approaches this allows not only the representation and recognition of a fixed set of basic emotions but also supports the handling of derived emotions. We showed the application of this model using the fuzzy prosody based emotion recognition system PROSBER. As a first step we proposed a division of the unit hypercube in equally sized subcubes to distinguish basic emotions and their combinations or blends. An interesting point for further investigation is whether this subdivision corresponds to human recognition. This could for instance be done using a learning approach that automatically finds such subdivisions and compares them with human interpretations of corresponding emotional states. References [1] R. C. Arkin, M. Fujita, T. Takagi, and R. Hasegawa. An ethological and emotional basis for human-robot interaction. In Robotics and Autonomous Systems, vol. 42, no. 3, pages Elsevier Science, [2] A. Austermann, N. Esau, L. Kleinjohann, and B. Kleinjohann. Prosody based emotion recognition for mexi. In Proceedings of IEEE/RSJ Int. Conference on Intelligent Robots and Systems (IROS 2005), Edmonton, Alberta, Canada, August [3] J. Bates. The role of emotion in believable agents. Communication of the ACM, 37(7), pages , [4] A. Boozer. Characterization of Emotional Speech in Human-Computer-Dialogues. M.Sc. Thesis. MIT Press,
6 [5] C. Breazeal. Affective interaction between humans and robots. In Proc. of ECAL 01, pages , Prague, [6] C. Breazeal and L. Aryananda. Recognition of affective communicative intent in robot-directed speech. In Autonomous Robots 12, pages Kluwer Academic Publishers, [7] M. S. El-Nasr, J. Yen, and T. Ioerger. Flame - a fuzzy logic adaptive model of emotions. In Automous Agents and Multi-agent Systems 3, pages , [8] C. Elliot. The Affective Reasoner: A Process model of emotions in a multi-agent system. Ph.D. Thesis. Institute for the Learning Sciences, Evanston, IL: Northwestern University, [9] N. H. Frijda. Neural Networks and Fuzzy Systems; A Dynamical Systems Approach to Machine Intelligence. Cambridge University Press, [10] S. Hashimoto. Kansei as the third target of information processing and related topics. In Proceedings of Intl. Workshop on Kansei Technology of Emotion, pages , [11] H. Ishibuchi and T. Nakashima. A study on generating fuzzy classification rules using histogramms. In Knowledge based Intelligent electronic Systems, Bd. 1. Prentice Hall, [12] B. Kosko. Neural Networks and Fuzzy Systems; A Dynamical Systems Approach to Machine Intelligence. Prentice Hall, Englewood Cliffs, NJ, [13] R. S. Lazarus. Emotion and Adaptation. Oxford University Press, [14] A. Ortony, G. Clore, and A. Collins. The Cognitive Structure of Emotions. Cambridge University Press, [15] A. Ortony and W. Turner. What s basic about basic emotions? Psychological Review, pages , [16] R. Plutchik. The Emotions. University Press of America, Inc., revised edition, [17] J. Velasquez. Modeling emotions and other motivations in synthetic agents. In Proceedings of the AAAI Conference, pages Providence, RI,
Introduction to affect computing and its applications
Introduction to affect computing and its applications Overview What is emotion? What is affective computing + examples? Why is affective computing useful? How do we do affect computing? Some interesting
More informationStrategies using Facial Expressions and Gaze Behaviors for Animated Agents
Strategies using Facial Expressions and Gaze Behaviors for Animated Agents Masahide Yuasa Tokyo Denki University 2-1200 Muzai Gakuendai, Inzai, Chiba, 270-1382, Japan yuasa@sie.dendai.ac.jp Abstract. This
More informationAffective Dialogue Communication System with Emotional Memories for Humanoid Robots
Affective Dialogue Communication System with Emotional Memories for Humanoid Robots M. S. Ryoo *, Yong-ho Seo, Hye-Won Jung, and H. S. Yang Artificial Intelligence and Media Laboratory Department of Electrical
More informationArtificial Emotions to Assist Social Coordination in HRI
Artificial Emotions to Assist Social Coordination in HRI Jekaterina Novikova, Leon Watts Department of Computer Science University of Bath Bath, BA2 7AY United Kingdom j.novikova@bath.ac.uk Abstract. Human-Robot
More informationThe Importance of the Mind for Understanding How Emotions Are
11.3 The Importance of the Mind for Understanding How Emotions Are Embodied Naomi I. Eisenberger For centuries, philosophers and psychologists alike have struggled with the question of how emotions seem
More informationNatural Emotion Expression of a Robot Based on Reinforcer Intensity and Contingency
2007 IEEE International Conference on Robotics and Automation Roma, Italy, 10-14 April 2007 ThB4.3 Natural Emotion Expression of a Robot Based on Reinforcer Intensity and Contingency Seung-Ik Lee, Gunn-Yong
More informationEmotions of Living Creatures
Robot Emotions Emotions of Living Creatures motivation system for complex organisms determine the behavioral reaction to environmental (often social) and internal events of major significance for the needs
More informationAffect in Virtual Agents (and Robots) Professor Beste Filiz Yuksel University of San Francisco CS 686/486
Affect in Virtual Agents (and Robots) Professor Beste Filiz Yuksel University of San Francisco CS 686/486 Software / Virtual Agents and Robots Affective Agents Computer emotions are of primary interest
More informationReferences. Note: Image credits are in the slide notes
References Reeve, J. (2009). Understanding motivation and (5th ed.). Hoboken, NJ: Wiley. Tomkins, S. S. (1970) Affect as the primary motivational system. In M. B. Arnold (ed.), Feelings and s (pp. 101-110).
More informationEMOTIONS S E N I O R S P E C I A L I S T I N P S Y C H I A T R Y A N D S E X T H E R A P Y
EMOTIONS C O L. S A E D S H U N N A Q S E N I O R S P E C I A L I S T I N P S Y C H I A T R Y A N D S E X T H E R A P Y EMOTIONS Emotion is any conscious experience characterized by intense mental activity
More informationAffective Game Engines: Motivation & Requirements
Affective Game Engines: Motivation & Requirements Eva Hudlicka Psychometrix Associates Blacksburg, VA hudlicka@ieee.org psychometrixassociates.com DigiPen Institute of Technology February 20, 2009 1 Outline
More informationEmotion Lecture 26 1
Emotion Lecture 26 1 The Trilogy of Mind Immanuel Kant (1791); Hilgard (1980) There are three absolutely irreducible faculties of mind: knowledge, feeling, and desire. Cognition Knowledge and Beliefs Emotion
More informationAffective Systems. Rotterdam, November 11, 2004
Affective Systems Rotterdam, November 11, 2004 What is an affective system? A fly? A dog? A software? A human? An ant? What is an affective system? We need a definition of affect in order to define affective
More informationemotions "affective computing" 30/3/
emotions "affective computing" 1 emotion and thought Globe & Mail, March 25, p. A15 "Emotions are an intrinsically human process..", [Dr. Mayberry] said. "One cannot separate emotions from thinking." Are
More informationAgents, Emotional Intelligence and Fuzzy Logic
Agents, Emotional Intelligence and Fuzzy Logic Magy Seif El-Nasr John Yen Computer Science Department Computer Science Department Texas A&M University Texas A&M University College Station, TX 77844-3112
More informationUsing an Emotional Intelligent Agent to Improve the Learner s Performance
Using an Emotional Intelligent Agent to Improve the Learner s Performance Soumaya Chaffar, Claude Frasson Département d'informatique et de recherche opérationnelle Université de Montréal C.P. 6128, Succ.
More informationResearch Proposal on Emotion Recognition
Research Proposal on Emotion Recognition Colin Grubb June 3, 2012 Abstract In this paper I will introduce my thesis question: To what extent can emotion recognition be improved by combining audio and visual
More informationAffective Computing for Intelligent Agents. Introduction to Artificial Intelligence CIS 4930, Spring 2005 Guest Speaker: Cindy Bethel
Affective Computing for Intelligent Agents Introduction to Artificial Intelligence CIS 4930, Spring 2005 Guest Speaker: Cindy Bethel Affective Computing Affect: phenomena manifesting i itself under the
More informationEmotions. These aspects are generally stronger in emotional responses than with moods. The duration of emotions tend to be shorter than moods.
LP 8D emotions & James/Lange 1 Emotions An emotion is a complex psychological state that involves subjective experience, physiological response, and behavioral or expressive responses. These aspects are
More informationHuman Emotion. Psychology 3131 Professor June Gruber
Human Emotion Psychology 3131 Professor June Gruber Human Emotion What is an Emotion? QUESTIONS? William James To the psychologist alone can such questions occur as: Why do we smile, when pleased, and
More informationA ective Computing of Constitutional States for Human Information Interaction
A ective Computing of Constitutional States for Human Information Interaction Brian Jalaian 1, Hooman Samani 2, Michael Lee 1, and Adrienne Raglin 1 1 U.S. Army Research Laboratory, Adelphi, MD {brian.jalaian.ctr,michael.h.lee.civ,adrienne.j.raglin.civ}@mail.mil
More informationFuzzy Perception, Emotion and Expression for Interactive Robots *
Fuzzy Perception, Emotion and Expression for Interactive Robots * Hossein Mobahi Dept. of Electrical and Computer Engineering Faculty of Engineering, University of Tehran North Karegar Street, Tehran,
More informationAnalysis of Emotion Recognition using Facial Expressions, Speech and Multimodal Information
Analysis of Emotion Recognition using Facial Expressions, Speech and Multimodal Information C. Busso, Z. Deng, S. Yildirim, M. Bulut, C. M. Lee, A. Kazemzadeh, S. Lee, U. Neumann, S. Narayanan Emotion
More informationLearning and Emotional Intelligence in Agents
From: AAAI Technical Report FS-98-03. Compilation copyright 1998, AAAI (www.aaai.org). All rights reserved. Learning and Emotional Intelligence in Agents Magy Seif El-Nasr Thomas R. Ioerger John Yen Assistant
More informationFeelings. Subjective experience Phenomenological awareness Cognitive interpretation. Sense of purpose
Motivation & Emotion Aspects of Feelings Subjective experience Phenomenological awareness Cognitive interpretation What is an? Bodily arousal Bodily preparation for action Physiological activiation Motor
More informationAspects of emotion. Motivation & Emotion. Aspects of emotion. Review of previous lecture: Perennial questions about emotion
Motivation & Emotion Aspects of Dr James Neill Centre for Applied Psychology University of Canberra 2016 Image source 1 Aspects of (Emotion Part 2): Biological, cognitive & social aspects Reading: Reeve
More informationFrom Affect Programs to Higher Cognitive Emotions: An Emotion-Based Control Approach
From Affect Programs to Higher Cognitive Emotions: An Emotion-Based Control Approach Juan D. Velásquez MIT Artificial Intelligence Laboratory 545 Technology Square, NE43-935 Cambridge, Massachusetts 02139
More informationStatistical and Neural Methods for Vision-based Analysis of Facial Expressions and Gender
Proc. IEEE Int. Conf. on Systems, Man and Cybernetics (SMC 2004), Den Haag, pp. 2203-2208, IEEE omnipress 2004 Statistical and Neural Methods for Vision-based Analysis of Facial Expressions and Gender
More informationRecognising Emotions from Keyboard Stroke Pattern
Recognising Emotions from Keyboard Stroke Pattern Preeti Khanna Faculty SBM, SVKM s NMIMS Vile Parle, Mumbai M.Sasikumar Associate Director CDAC, Kharghar Navi Mumbai ABSTRACT In day to day life, emotions
More informationA Fuzzy Logic System to Encode Emotion-Related Words and Phrases
A Fuzzy Logic System to Encode Emotion-Related Words and Phrases Author: Abe Kazemzadeh Contact: kazemzad@usc.edu class: EE590 Fuzzy Logic professor: Prof. Mendel Date: 2007-12-6 Abstract: This project
More informationA Possibility for Expressing Multi-Emotion on Robot Faces
The 5 th Conference of TRS Conference 26-27 May 2011, Bangkok, Thailand A Possibility for Expressing Multi-Emotion on Robot Faces Trin Veerasiri 1*, Djitt Laowattana 2 Institute of Field robotics, King
More informationCOMBINING CATEGORICAL AND PRIMITIVES-BASED EMOTION RECOGNITION. University of Southern California (USC), Los Angeles, CA, USA
COMBINING CATEGORICAL AND PRIMITIVES-BASED EMOTION RECOGNITION M. Grimm 1, E. Mower 2, K. Kroschel 1, and S. Narayanan 2 1 Institut für Nachrichtentechnik (INT), Universität Karlsruhe (TH), Karlsruhe,
More informationSociable Robots Peeping into the Human World
Sociable Robots Peeping into the Human World An Infant s Advantages Non-hostile environment Actively benevolent, empathic caregiver Co-exists with mature version of self Baby Scheme Physical form can evoke
More informationEmotion in Intelligent Virtual Agents: the Flow Model of Emotion
Emotion in Intelligent Virtual gents: the Flow Model of Emotion Luís Morgado 1,2 and Graça Gaspar 2 1 Instituto Superior de Engenharia de Lisboa Rua Conselheiro Emídio Navarro, 1949-014 Lisboa, Portugal
More informationRepresenting Emotion and Mood States for Virtual Agents
Representing Emotion and Mood States for Virtual Agents Luis Peña 1, Jose-María Peña 2, and Sascha Ossowski 1 1 Universidad Rey Juan Carlos {luis.pena,sascha.ossowski}@urjc.es 2 Universidad Politecnica
More informationIntroduction to Psychology. Lecture no: 27 EMOTIONS
Lecture no: 27 EMOTIONS o Derived from the Latin word Emovere emotion means to excite, stir up or agitate. o A response that includes feelings such as happiness, fear, sadness, grief, sorrow etc: it is
More informationThe Effect of Dominance Manipulation on the Perception and Believability of an Emotional Expression
The Effect of Dominance Manipulation on the Perception and Believability of an Emotional Expression Wim F.J. van der Ham 1(B), Joost Broekens 2, and Peter H.M.P. Roelofsma 1 1 AAL-VU, VU Amsterdam, De
More informationEmotion Recognition using a Cauchy Naive Bayes Classifier
Emotion Recognition using a Cauchy Naive Bayes Classifier Abstract Recognizing human facial expression and emotion by computer is an interesting and challenging problem. In this paper we propose a method
More informationMotivation represents the reasons for people's actions, desires, and needs. Typically, this unit is described as a goal
Motivation What is motivation? Motivation represents the reasons for people's actions, desires, and needs. Reasons here implies some sort of desired end state Typically, this unit is described as a goal
More informationEmotional Development
Emotional Development How Children Develop Chapter 10 Emotional Intelligence A set of abilities that contribute to competent social functioning: Being able to motivate oneself and persist in the face of
More informationA Fuzzy Emotional Agent for Decision-Making in a Mobile Robot
A Fuzzy Emotional Agent for Decision-Making in a Mobile Robot Magy Seif El-Nasr Marjorie Skubic Department of Computer Science Computer Engineering and Computer Science Dept. Texas A&M University University
More informationManaging emotions in turbulent and troubling times. Professor Peter J. Jordan Griffith Business School
Managing emotions in turbulent and troubling times Professor Peter J. Jordan Griffith Business School Overview Emotions and behaviour Emotional reactions to change Emotional intelligence What emotions
More informationBrain Mechanisms Explain Emotion and Consciousness. Paul Thagard University of Waterloo
Brain Mechanisms Explain Emotion and Consciousness Paul Thagard University of Waterloo 1 1. Why emotions matter 2. Theories 3. Semantic pointers 4. Emotions 5. Consciousness Outline 2 What is Emotion?
More informationAffect and Affordance: Architectures without Emotion
Affect and Affordance: Architectures without Emotion Darryl N. Davis, Suzanne C. Lewis and Gauvain Bourgne University of Hull, UK. Abstract General frameworks of mind map across tasks and domains. By what
More informationEmotion-Aware Machines
Emotion-Aware Machines Saif Mohammad, Senior Research Officer National Research Council Canada 1 Emotion-Aware Machines Saif Mohammad National Research Council Canada 2 What does it mean for a machine
More informationAffective Agent Architectures
Affective Agent Architectures Matthias Scheutz Artificial Intelligence and Robotics Laboratory Department of Computer Science and Engineering University of Notre Dame Notre Dame, IN 46556, USA mscheutz@cse.nd.edu
More informationEmotions and Motivation
Emotions and Motivation LP 8A emotions, theories of emotions 1 10.1 What Are Emotions? Emotions Vary in Valence and Arousal Emotions Have a Physiological Component What to Believe? Using Psychological
More informationComparison of Multisensory Display Rules. in Expressing Complex Emotions between Cultures
ISCA Archive http://www.isca-speech.org/archive FAAVSP - The 1 st Joint Conference on Facial Analysis, Animation, and Auditory-Visual Speech Processing Vienna, Austria, September 11-13, 2015 Comparison
More informationFormulating Emotion Perception as a Probabilistic Model with Application to Categorical Emotion Classification
Formulating Emotion Perception as a Probabilistic Model with Application to Categorical Emotion Classification Reza Lotfian and Carlos Busso Multimodal Signal Processing (MSP) lab The University of Texas
More informationCS148 - Building Intelligent Robots Lecture 5: Autonomus Control Architectures. Instructor: Chad Jenkins (cjenkins)
Lecture 5 Control Architectures Slide 1 CS148 - Building Intelligent Robots Lecture 5: Autonomus Control Architectures Instructor: Chad Jenkins (cjenkins) Lecture 5 Control Architectures Slide 2 Administrivia
More informationMODULE 41: THEORIES AND PHYSIOLOGY OF EMOTION
MODULE 41: THEORIES AND PHYSIOLOGY OF EMOTION EMOTION: a response of the whole organism, involving 1. physiological arousal 2. expressive behaviors, and 3. conscious experience A mix of bodily arousal
More informationMultilevel Emotion Modeling for Autonomous Agents
Multilevel Emotion Modeling for Autonomous Agents Andreas H. Marpaung Department of Electrical Engineering and Computer Science University of Central Florida Orlando, Florida 32816 marpaung@cs.ucf.edu
More informationCulture and Emotion THE EVOLUTION OF HUMAN EMOTION. Outline
Outline Culture and Emotion The Evolution of Human Emotion Universality in Emotion- The Basic Emotions Perspective Cultural Differences in Emotion Conclusion Chapter 8 THE EVOLUTION OF HUMAN EMOTION Emotion:
More informationAffective Computing Ana Paiva & João Dias. Lecture 1. Course Presentation
Affective Computing Ana Paiva & João Dias Lecture 1. Course Presentation Motivation. What is Affective Computing? Applications and Problems Perspectives on Emotions History of Affective Sciences Communication
More informationUser Affective State Assessment for HCI Systems
Association for Information Systems AIS Electronic Library (AISeL) AMCIS 2004 Proceedings Americas Conference on Information Systems (AMCIS) 12-31-2004 Xiangyang Li University of Michigan-Dearborn Qiang
More informationThis is the accepted version of this article. To be published as : This is the author version published as:
QUT Digital Repository: http://eprints.qut.edu.au/ This is the author version published as: This is the accepted version of this article. To be published as : This is the author version published as: Chew,
More informationEmotion Theory. Dr. Vijay Kumar
Emotion Theory Dr. Vijay Kumar Emotions Just how many emotions are there? Basic Emotions Some have criticized Plutchik s model as applying only to English-speakers Revised model of basic emotions includes:
More informationOutline. Emotion. Emotions According to Darwin. Emotions: Information Processing 10/8/2012
Outline Emotion What are emotions? Why do we have emotions? How do we express emotions? Cultural regulation of emotion Eliciting events Cultural display rules Social Emotions Behavioral component Characteristic
More informationNature of emotion: Six perennial questions
Motivation & Emotion Nature of emotion James Neill Centre for Applied Psychology University of Canberra 2017 Image source 1 Nature of emotion: Six perennial questions Reading: Reeve (2015) Ch 12 (pp. 337-368)
More informationComputational Intelligence Lecture 21: Integrating Fuzzy Systems and Neural Networks
Computational Intelligence Lecture 21: Integrating Fuzzy Systems and Neural Networks Farzaneh Abdollahi Department of Electrical Engineering Amirkabir University of Technology Fall 2013 Farzaneh Abdollahi
More informationIndian Institute of Technology Kanpur National Programme on Technology Enhanced Learning (NPTEL) Course Title A Brief Introduction of Psychology
Indian Institute of Technology Kanpur National Programme on Technology Enhanced Learning (NPTEL) Course Title A Brief Introduction of Psychology Lecture 20 Emotion by Prof. Braj Bhushan Humanities & Social
More informationTemporal Context and the Recognition of Emotion from Facial Expression
Temporal Context and the Recognition of Emotion from Facial Expression Rana El Kaliouby 1, Peter Robinson 1, Simeon Keates 2 1 Computer Laboratory University of Cambridge Cambridge CB3 0FD, U.K. {rana.el-kaliouby,
More informationNature of emotion: Six perennial questions
Motivation & Emotion Nature of emotion Nature of emotion: Six perennial questions Dr James Neill Centre for Applied Psychology University of Canberra 2016 Image source 1 Reading: Reeve (2015) Ch 12 (pp.
More information1/12/2012. How can you tell if someone is experiencing an emotion? Emotion. Dr.
http://www.bitrebels.com/design/76-unbelievable-street-and-wall-art-illusions/ 1/12/2012 Psychology 456 Emotion Dr. Jamie Nekich A Little About Me Ph.D. Counseling Psychology Stanford University Dissertation:
More informationTalking Heads for the Web: what for? Koray Balci Fabio Pianesi Massimo Zancanaro
Talking Heads for the Web: what for? Koray Balci Fabio Pianesi Massimo Zancanaro Outline XFace an open source MPEG4-FAP based 3D Talking Head Standardization issues (beyond MPEG4) Synthetic Agents the
More informationMotives as Intrinsic Activation for Human-Robot Interaction
Motives as Intrinsic Activation for Human-Robot Interaction Jochen Hirth and Karsten Berns Abstract For humanoid robots that should assist humans in their daily life the capability of an adequate interaction
More informationEmotions in Intelligent Agents
From: FLAIRS-02 Proceedings. Copyright 2002, AAAI (www.aaai.org). All rights reserved. Emotions in Intelligent Agents N Parameswaran School of Computer Science and Engineering University of New South Wales
More informationGender Based Emotion Recognition using Speech Signals: A Review
50 Gender Based Emotion Recognition using Speech Signals: A Review Parvinder Kaur 1, Mandeep Kaur 2 1 Department of Electronics and Communication Engineering, Punjabi University, Patiala, India 2 Department
More informationWhat is Emotion? Emotion is a 4 part process consisting of: physiological arousal cognitive interpretation, subjective feelings behavioral expression.
What is Emotion? Emotion is a 4 part process consisting of: physiological arousal cognitive interpretation, subjective feelings behavioral expression. While our emotions are very different, they all involve
More informationUseful Roles of Emotions in Animated Pedagogical Agents. Ilusca L. L. Menezes IFT6261 :: Winter 2006
Useful Roles of Emotions in Animated Ilusca L. L. Menezes IFT6261 :: Winter 2006 Objectives To provide opportunities: To understand the importance of the emotions in learning To explore how to model emotions
More informationCase-based Reasoning in Health Care
Introduction Case-based Reasoning in Health Care Resembles human reasoning Shahina Begum Introduction -Case represent individual s entire case history -A A single visit to a doctor Limitations Limitations
More informationR Jagdeesh Kanan* et al. International Journal of Pharmacy & Technology
ISSN: 0975-766X CODEN: IJPTFI Available Online through Research Article www.ijptonline.com FACIAL EMOTION RECOGNITION USING NEURAL NETWORK Kashyap Chiranjiv Devendra, Azad Singh Tomar, Pratigyna.N.Javali,
More informationCPSC81 Final Paper: Facial Expression Recognition Using CNNs
CPSC81 Final Paper: Facial Expression Recognition Using CNNs Luis Ceballos Swarthmore College, 500 College Ave., Swarthmore, PA 19081 USA Sarah Wallace Swarthmore College, 500 College Ave., Swarthmore,
More informationEmotion Affective Color Transfer Using Feature Based Facial Expression Recognition
, pp.131-135 http://dx.doi.org/10.14257/astl.2013.39.24 Emotion Affective Color Transfer Using Feature Based Facial Expression Recognition SeungTaek Ryoo and Jae-Khun Chang School of Computer Engineering
More informationEstimating Intent for Human-Robot Interaction
Estimating Intent for Human-Robot Interaction D. Kulić E. A. Croft Department of Mechanical Engineering University of British Columbia 2324 Main Mall Vancouver, BC, V6T 1Z4, Canada Abstract This work proposes
More informationComputational Analytical Framework for Affective Modeling: Towards Guidelines for Designing Computational Models of Emotions
1 Chapter 1 Computational Analytical Framework for Affective Modeling: Towards Guidelines for Designing Computational Models of Emotions Eva Hudlicka Psychometrix Associates, Inc., USA & University of
More informationCAAF: A Cognitive Affective Agent Programming Framework
CAAF: A Cognitive Affective Agent Programming Framework F. Kaptein, J. Broekens, K. V. Hindriks, and M. Neerincx Delft University of Technology, Mekelweg 2, 2628 CD Delft, The Netherlands, F.C.A.Kaptein@tudelft.nl
More informationFuzzy Expert System Design for Medical Diagnosis
Second International Conference Modelling and Development of Intelligent Systems Sibiu - Romania, September 29 - October 02, 2011 Man Diana Ofelia Abstract In recent years, the methods of artificial intelligence
More informationPredicting Learners Emotional Response in Intelligent Distance Learning Systems
Predicting Learners Emotional Response in Intelligent Distance Learning Systems Soumaya Chaffar, Claude Frasson Département d'informatique et de recherche opérationnelle Université de Montréal C.P. 6128,
More informationThe challenge of representing emotional colouring. Roddy Cowie
The challenge of representing emotional colouring Roddy Cowie My aim: A. To outline the way I see research in an area that I have been involved with for ~15 years - in a way lets us compare notes C. To
More informationImplementation of Inference Engine in Adaptive Neuro Fuzzy Inference System to Predict and Control the Sugar Level in Diabetic Patient
, ISSN (Print) : 319-8613 Implementation of Inference Engine in Adaptive Neuro Fuzzy Inference System to Predict and Control the Sugar Level in Diabetic Patient M. Mayilvaganan # 1 R. Deepa * # Associate
More informationDacher Keltner (professor of psychology at University of California, Berkeley) helped filmmakers understand emotions for the Pixar movie Inside Out.
Emotion Theories Dacher Keltner (professor of psychology at University of California, Berkeley) helped filmmakers understand emotions for the Pixar movie Inside Out. Outline Review why emotion theory useful
More informationAudio-based Emotion Recognition for Advanced Automatic Retrieval in Judicial Domain
Audio-based Emotion Recognition for Advanced Automatic Retrieval in Judicial Domain F. Archetti 1,2, G. Arosio 1, E. Fersini 1, E. Messina 1 1 DISCO, Università degli Studi di Milano-Bicocca, Viale Sarca,
More informationAnxiety Detection during Human-Robot Interaction *
Anxiety Detection during Human-Robot Interaction * Dana Kulić and Elizabeth Croft Department of Mechanical Engineering University of British Columbia Vancouver, Canada {dana,ecroft}@mech.ubc.ca Abstract
More informationQ-Learning with Basic Emotions
Q-Learning with Basic Emotions Wilfredo Badoy Jr. Department of Information System and Computer Science Ateneo de Manila University Quezon City, Philippines wbadoy@yahoo.com Kardi Teknomo Department of
More informationCOMPARISON BETWEEN GMM-SVM SEQUENCE KERNEL AND GMM: APPLICATION TO SPEECH EMOTION RECOGNITION
Journal of Engineering Science and Technology Vol. 11, No. 9 (2016) 1221-1233 School of Engineering, Taylor s University COMPARISON BETWEEN GMM-SVM SEQUENCE KERNEL AND GMM: APPLICATION TO SPEECH EMOTION
More informationFever Diagnosis Rule-Based Expert Systems
Fever Diagnosis Rule-Based Expert Systems S. Govinda Rao M. Eswara Rao D. Siva Prasad Dept. of CSE Dept. of CSE Dept. of CSE TP inst. Of Science & Tech., TP inst. Of Science & Tech., Rajah RSRKRR College
More informationA Fuzzy Logic Computational Model for Emotion Regulation Based on Gross Theory
Proceedings of the Twenty-Sixth International Florida Artificial Intelligence Research Society Conference A Fuzzy Logic Computational Model for Emotion Regulation Based on Gross Theory Ahmad Soleimani
More informationA framework for the Recognition of Human Emotion using Soft Computing models
A framework for the Recognition of Human Emotion using Soft Computing models Md. Iqbal Quraishi Dept. of Information Technology Kalyani Govt Engg. College J Pal Choudhury Dept. of Information Technology
More informationThe innate hypothesis
The innate hypothesis DARWIN (1872) proposed that the facial expression of emotion evolved as part of the actions necessary for life: Anger: Frowning (to protect eyes in anticipation of attack) Surprise:
More informationWho Needs Cheeks? Eyes and Mouths are Enough for Emotion Identification. and. Evidence for a Face Superiority Effect. Nila K Leigh
1 Who Needs Cheeks? Eyes and Mouths are Enough for Emotion Identification and Evidence for a Face Superiority Effect Nila K Leigh 131 Ave B (Apt. 1B) New York, NY 10009 Stuyvesant High School 345 Chambers
More informationHierarchically Organized Mirroring Processes in Social Cognition: The Functional Neuroanatomy of Empathy
Hierarchically Organized Mirroring Processes in Social Cognition: The Functional Neuroanatomy of Empathy Jaime A. Pineda, A. Roxanne Moore, Hanie Elfenbeinand, and Roy Cox Motivation Review the complex
More informationEMOTIONAL LEARNING. Synonyms. Definition
EMOTIONAL LEARNING Claude Frasson and Alicia Heraz Department of Computer Science, University of Montreal Montreal (Québec) Canada {frasson,heraz}@umontreal.ca Synonyms Affective Learning, Emotional Intelligence,
More informationFuzzy Model on Human Emotions Recognition
Fuzzy Model on Human Emotions Recognition KAVEH BAKHTIYARI &HAFIZAH HUSAIN Department of Electrical, Electronics and Systems Engineering Faculty of Engineering and Built Environment, Universiti Kebangsaan
More informationNothing in biology makes sense except in the light of evolution Theodosius Dobzhansky Descent with modification Darwin
Evolutionary Psychology: Emotion, Cognition and Intelligence Bill Meacham, Ph.D. APDG, 11 May 2015 www.bmeacham.com Evolution Nothing in biology makes sense except in the light of evolution Theodosius
More informationMental State Recognition by using Brain Waves
Indian Journal of Science and Technology, Vol 9(33), DOI: 10.17485/ijst/2016/v9i33/99622, September 2016 ISSN (Print) : 0974-6846 ISSN (Online) : 0974-5645 Mental State Recognition by using Brain Waves
More informationFacial expression recognition with spatiotemporal local descriptors
Facial expression recognition with spatiotemporal local descriptors Guoying Zhao, Matti Pietikäinen Machine Vision Group, Infotech Oulu and Department of Electrical and Information Engineering, P. O. Box
More informationDesigning Emotion-Capable Robots, One Emotion at a Time
Designing Emotion-Capable Robots, One Emotion at a Time Afshin Ganjoo (afshin.ganjoo@intel.com) Intel Corporation, 1900 Prairie City Road Folsom, CA 95630, USA Abstract Recent advances in emotion modeling
More informationWorld Journal of Engineering Research and Technology WJERT
wjert, 2019, Vol. 5, Issue 1, 283-292. Original Article ISSN 2454-695X Amarnath et al. WJERT www.wjert.org SJIF Impact Factor: 5.218 ADVANCED E-LEARNING MODEL AND ARTIFICIAL EMOTIONAL INTELLIGENCE 1 *Amarnath
More informationTime-varying affective response for humanoid robots 1
Time-varying affective response for humanoid robots 1 Lilia Moshkina 1, Ronald C. Arkin 1, Jamee K. Lee 2, and HyunRyong Jung 2 1 Georgia Tech Mobile Robot Laboratory, Atlanta, GA, USA 30332 2 Samsung
More information