This document discusses affective computing in artificial intelligence. It defines affective computing as the study and development of systems that can recognize, interpret, process, and simulate human emotion and respond appropriately. The document outlines the architecture of intelligent systems that incorporate affective computing and describes how emotions can be perceived, particularly through facial expressions. Several algorithms used for emotion classification are mentioned. Applications of affective computing discussed include e-learning, marketing, employee monitoring, robotics, healthcare, and vehicle driving. Examples of artificial intelligence incorporating affective computing like Jibo, Pepper robot, and Amazon Alexa are provided.

1. AFFECT COMPUTING in AI
PRESENTED BY
THAMBA MESHACH.W,
ASSOCIATE PROFESSOR IN CSE
PRATHYUSHA ENGINEERING COLLEGE
TECHNOLOGY UPDATE MEETING (TUM)

2. INTRODUCTION
Affective Computing in AI By Thamba Meshach W

3. CONTENTS
Affective Computing in AI By Thamba Meshach W

4. “The question is not whether intelligent machines can have
any emotions, but whether machines can be intelligent
without any emotions.”
◦ — Marvin Minsky (1927–2016) (Co-founder of AI Lab at MIT, Turing Award winner
(most prestigious award in Computer Science)).
Affective Computing in AI By Thamba Meshach W

5. What is Affective Computing?
Affective Computing is the study and development of
systems and devices that can recognize, interpret,
process, and simulate human affect/emotion and
appropriately respond to its user’s emtions.
Affective Computing in AI By Thamba Meshach W

6. Cont….
-Machine Interpreting the emotinal state of humans
and adpts its behavior to them
-A reserach field - computer science, psychology, and
cognitive science.
Affective Computing in AI By Thamba Meshach W

7. Architecture
Intelligent System
System
Response
Affective Computing in AI By Thamba Meshach W

8. Emotions
Affective Computing in AI

9. Affective Computing in AI By Thamba Meshach W
Percieve Emotions

10. Facial Expressions
1. Face is extracted from the background
2. Facial geometry (e.g. locations of eyes,
nose, mouth) can be estimated.
3. Based on facial geometry, facial
expressions can be normalized, taking
out the impact of head rotations or other
head movements
Affective Computing in AI By Thamba Meshach W

11. On deducing and c1assifying human emotions, the most frequently used c1assifiers
Linear Discriminant Classifiers (LDC),
Hidden Markov Models (HMMs),
Gaussian Mixture Model (GMM),
Support Vector Machines (SVM),
k-nearest neighbour (k-NN),
Artificial Neural Networks (ANN) and
Decision Tree Algorithms.
Affective Computing in AI By Thamba Meshach W
Algorithms Employed

12. Applications
Affective Computing in AI By Thamba Meshach W

13. Applications
E -leaming : On computers wherein computing the users feelings can be used to adjust the presentation,
content can be more polished and tasteful teaching when a leamer is uninterested, or
paused.
Marketing : measure customer reactions to their campaigns, new products, and services to
optimize their marketing strategies.
Training & Tracking : employees’ stress and anxiety levels during the job and observe if they are
satisfied with their current tasks and workload.
Robotic : Capable of giving out affective data exhibit greater tlexibility while one works in uncertain
or complex environments. Buddy gadgets, like digital pets
Health Care : Emotion AI can be used in counseling sessions to track and understand mental states
better and help doctors support counselee more effectively
Vechile Driving
Affective Computing in AI By Thamba Meshach W

14. Artificial Intelligence and Affective Computing
- JIBO , Family Robot, MIT Lab of Robotics, like a Disney character that can
recognize face and do simple talks
- Pepper Emotional Humanoid Robot, Japanese robot that can tell apart
feelings such as joy, anger, sadness and can respond accordingly
- Amazon Alexa, Amazon working on alexa to recognize emotions
Affective Computing in AI By Thamba Meshach W

15. Artificial Intelligence and Affective Computing
Affective Computing in AI

16. References
1. “ Affective Computing in Tele-Home Health”. Lisetti, Christine, Lerouge, Cynthia VL - 37,
DO - 10.1109/HICSS.2004.1265373, 2004/01/01-
2. "Facial Expression Recognition via Learning Deep Sparse Autoencoders“
3. https://research.aimultiple.com/affective-computing-applications/
4. https://thedatascientist.com/affective-computing-mindless-ai/
5. “Analysing Human Feelings by Affective Computing Survey” , Taranpreet Singh Saini , Dr.
MangeshBedekar , Saniya Zahoor , MAEER's MIT, Kothrud, Pune, India
6. Image Courtsey : Google Images
Affective Computing in AI By Thamba Meshach W

17. THANK YOU
Affective Computing in AI By Thamba Meshach W