Affective Computing a Seminar topic of WBUT Paper Code:-IT 681 Presented By Soumitra Halder, Gourab Dey & Maiteayee Kundu.
- 1.Affective Computing A seminar is presented by Gourab Dey (11700212101) Namrata Kundu (11700211041) Soumitra Haldar (11700211067) Maitrayee Kundu (11700212104) RCC INSTITUTE OF INFORMATION TECHNOLOGY
2. 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). 3. 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 users. 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-Therapy Provide psychological health services (i.e. online counseling) revealing the emotional state as in real world session. Through Affective Computing, the patients posture, face expression and gesture in real world leads to accurate evaluation of psychological state. 4. 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 5. 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 6. 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) 7. 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. 8. 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 9. [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 10. Areas of Affective Computing AFFECTIVE WEARABLES Sensors & tools can be used in recognizing affective patterns, but these tools require a lot of attention/ maintenance. Figure : Wearers Blood Volume Pressure using photoplethysmography Figure : Sample & transmit biometric data to larger computer for analysis 11. 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 persons emotion. Bias : If a system has emotions, then recognizing ambiguous emotion becomes easier. Output : Recognize expression and likely underlying emotion. 12. 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. 13. Areas of Affective Computing Expressing Emotional Figure : MS Office Assistant Figure : Kismet Robot Evolution over the years 14. 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 Interactions When 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! 15. 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. 16. 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. 17. 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. 18. THANK YOU