AffectiveComputing
A seminar is presented by
Gourab Dey (11700212101)Namrata Kundu (11700211041)Soumitra Haldar (11700211067)Maitrayee Kundu (11700212104)
RCC INSTITUTE OF INFORMATION TECHNOLOGY
Introduction
Affective computing is the study and development of systems and devices that can recognize, interpret, process, and simulate human affects.
Motivation for Research : Ability to simulate Empathy (i.e. The machine should interpret the emotional state of humans and adapt its behavior to them, giving an appropriate response for those emotions).
Objective To develop a computing device with its capacity to gather cues to user
emotion from a variety of sources.In Simple words, produce “emotion aware machines”.
Facial expression, posture, gesture, speech, force or rhythm of key stroke, temperature change of hand on mouse can signify changes in user’s. emotional state, detected and interpreted by a computer.
There exist a limitless range of applications : E-Learning
Tutor expands explanation when user is found in a state of confusion, adds information when user is found in a state of curiosity etc.
E-TherapyProvide psychological health services (i.e. online counseling) revealing the emotional state as in real world session. Through Affective Computing, the patient’s posture, face expression and gesture in real world leads to accurate evaluation of psychological state.
PSYCHOLOGICAL THEORIES OF EMOTION
OPTIMISM
LOVE
SUBMISSION
AWE
AGGRESIVENESS
CONTEMPT
REMOVE
DISSAPPOINTMENT
JOY
ANTICIPATION
ANGER DANGER
SADNESS
ACCEPTANCE FEAR
SURPRISEJOY
ANTICIPATION
ANGER DANGER
SADNESS
ACCEPTANCE FEAR
SURPRISE
Classes of Expressions
Broadly classified into happy, sad, disgust,
fear, anger, surprise and neutral.
Goal is to classify an unknown expression
into one of these classes
COMPONENTS OF EMOTIONS
Subjective experience (feeling of fear and so on).
Physiological Changes in Autonomic Nervous System(ANS) and Endocrine System (Glands and Hormones released from them).e.g. trembling with fear precedes conscious control of them
Behavior evoked (such as running away or fainting due to fear)
Theories of Emotion
Cognitive Theories : Emotions are a heuristic to process information in the cognitive domain.
Two Factor theory : Appraisal of the situation, and the physiological state of the body creates the emotional response. Emotion, hence, has two factors.
SOME THEORIES
JAMES-LANGE THEORY Introduced in 1890 by James and Lange. Argues that action precedes emotion (brain interprets action as
emotion).e.g. something scary moving towards us → pulse starts rising up →
interpreting our state of body → we are afraid(Fear).
Perception of Emotion arousing
Stimulus
Visceral and skeletal Changes
Interpretation
Feedback loop
[A,V,S] Emotion Model
[Arousal , Valence , Stance] :- A 3-tuple models an “emotion”.
Arousal:- Surprise at high arousal, fatigue at low arousal
Valence:- Content at high valence, Unhappiness at low valence
Stance:- Stern at closed stance, accepting at open stance
Areas of Affective Computing
AFFECTIVE WEARABLESSensors & tools can be used in recognizing affective patterns, but these tools require a lot of attention/ maintenance.
Figure : Wearer’s Blood Volume Pressure using photoplethysmography
Figure : Sample & transmit biometric data to larger computer for analysis
Areas of Affective Computing
Detecting Emotional Information (Basic capabilities in a computer to discriminate emotions) Input : Getting a large variety of i/p signals. E.g. Face, Hand gesture,
posture, gait, respiration, electro thermal response, ECG, temperature, blood pressure, blood volume.
Pattern Recognition : Feature Extraction and their classification of signals. E.g. Analysis of Video motion features(to discriminate a frown from a smile)
Reasoning : Predicts underlying emotion based about how emotions are generated and expressed.
Learning : Factors tends to emotion (of an individual) which helps better to recognize a person’s emotion.
Bias : If a system has emotions, then recognizing ambiguous emotion becomes easier.
Output : Recognize expression and likely underlying emotion.
Areas of Affective Computing
Expressing Emotional Need of Computers to express emotions :
1. Computers expressing emotions can improve the quality and effectiveness of communication between people and technologies.
2. How people can communicate with computer such that they can express their emotions?
3. How technology can stimulate and support new modes of affective communication between people.
Efforts made :1. ‘Kismet’ an expressive robot at MIT is equipped with auditory
and proprioceptive (touch) sensory inputs. Kismet can express emotion through vocalization, facial expression and adjustment of Gaze direction and head orientation.
Areas of Affective Computing
Expressing Emotional
Figure : MS Office Assistant Figure : Kismet Robot
Evolution over the years
How can this be done?
We can recognize : Facial Features and cues Head Pose/Eye Gaze (to estimate attention) Hand Gestures (usually fixed vocabulary , signs) Directions and Commands (usually fixed vocabulary) Anger in speech (useful in call centers)
Affective InteractionsWhen computers can sense affective cues :
Users cannot read text off the screen and approach screen? Redraw text with larger font!
Call centre user is angry? Redirect to human operator!
Users not familiar with/cannot use mouse/keyboard? Spoken commands/hand gestures are another option!
Users not comfortable with on-screen text? Use virtual characters and speech synthesis!
Methods of Facial Recognition
Early methods used optical flow to capture
movement of features.(Such as facial muscles) Broadly methods are Model-Based, Feature-Based
or Holistic Spatial Based. Model & Feature-Based Methods have a set of
predefined features which are further used. Though this is simple and reduces complexity,
there is a loss of information.
Holistic Spatial Analysis
Whole image is taken not just specific features. No pre-defined features. Rather try to discover
intrinsic structural information. These are then used
to recognise the class of expression. Further divided into unsupervised (examples PCA,
ICA) and supervised (example FDA). In supervised
training is done on class-specified samples. Math behind this is quite complex, based on feature
subspaces.
CONCLUSION
Affective Computing is a young field of research
•For interactive systems, something far better than the current crop of “intelligent” systems are needed.•Affective Computing has applications in improving the quality of life in impaired people (successfully demonstrated for Autism)•Ethical compromises need to be done to inculcate affective computers•This field can really benefit from research into the human brain/mind.
THANK YOU
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