Affective Computing Interactions & Applications Emotional speech Algorithms Databases Speech Descriptors Detection: Facial affect Emotion classification Facial Action Coding System Physiological monitoring Potential Applications and Examples for Applications Conclusion

2.  Affective Computing
 Interactions & Applications
 Emotional speech
 Algorithms
 Databases
 Speech Descriptors
 Detection:Facial affect
 Emotion classification
 Facial Action Coding System
 Physiological monitoring
 Potential Applications and Examples for Applications
 Conclusion

3.  What is affective computing?
“the affective computing thinks about how
emotions are generated in computers,
realized by computer, and tells by
computer. The affective computing is used
in four area which are recognizing emotion,
expressing emotion, having emotions and
having emotional intelligence”

4.  Affective Computing technologies can
be organized into five areas
1. Technology for sending affective
information
2. Technology for receiving and interpreting
affective information
3. Methods for computers to respond
intelligently and respectfully to handle
perceived affective information

5. 4. Computational mechanisms that
synthesize or simulate internal emotions
5. Social, ethical, and philosophical issues
related to the development and
deployment of affective computing
technologies

7.  Speech recognition is a great method of
identifying affective state, having an
average success rate reported in
research of 63%.
 This result appears fairly satisfying when
compared with humans’ success rate at
identifying emotions, but a little
insufficient.
 Many speech characteristics are
independent of semantics or culture.

8.  LDC - Classification happens based on the
value obtained from the linear combination
of the feature values, which are usually
provided in the form of a feature vector.
 k-NN Classification happens by locating the
object in the feature space, and
comparing it with the k nearest neighbors
(training examples). The majority vote
decides on the classification.

9.  GMM – is a probabilistic model used for
representing the existence of sub-
populations within the overall
population. Each sub-population is
described using the mixture distribution,
which allows for classification of
observations into the sub-populations

10.  Decision tree algorithms – work based on
following a decision tree in which leaves
represent the classification outcome,
and branches represent the conjunction
of subsequent features that lead to the
classification.

11.  SVM – is a type of (usually binary) linear
classifier which decides in which of the
two (or more) possible classes, each
input may fall into.

12.  Advantage of databases:
The nature of the data allows for
authentic real life implementation, due
to the fact it describes states naturally
occurring during the human computer
interaction (HCI).

13.  Disadvantages:
Naturalistic data has some difficulties to
obtain and this kind of data has low
emotional intensity.

14.  Frequency characteristics
 Time-related features
 Voice quality parameters and energy
descriptors

15.  Various methods such as optical
flow, hidden Markov model, neural
network processing or active
appearance model helps the detection
and processing of facial expression.

16.  Paul Ekman, 1972;
anger, disgust, fear, happiness, sadness
and surprise

17.  1990;
 Amusement
 Contempt
 Contentment
 Embarrassment
 Excitement
 Guilt
 Pride in achievement
 Relief
 Satisfaction
 Sensory pleasure
 Shame

18.  Emotion: Action Units
1. Happiness: 6+12
2. Sadness:1+4+15
3. Surprise: 1+2+5B+26
4. Fear: 1+2+4+5+20+26
5. Anger: 4+5+7+23
6. Disgust: 9+15+16
7. Contempt: R12A+R14A

19.  Galvanic Pulse,
 Galvanic Skin Response,
 Facial Electromyography.

20.  Wearable computer applications
 Human – computer interaction
 Kismet
 Educational technology

21.  Affective computing thinks about how
emotions are generated in computers,
realized by computer, and tells by
computer.
 It is obvious that the affective
computing is used in many areas within
computer technology related systems.
 These computing systems are proficient
for evaluating and testing the
performance of affective computers.

22. “Computing is not about computers any
more. It is about living.”
Nicholas Negroponte
“Information technology and business are
becoming inextricably interwoven. I don't
think anybody can talk meaningfully about
one without the talking about the other.”
Bill Gates
”They've finally come up with the perfect office
computer. If it makes a mistake, it blames
another computer.”
Milton Berle

23.  Affective computing,
http://en.wikipedia.org/wiki/Affective_computing
 Affective computing,
 http://www.springerlink.com/content/j015018wn121t810/fu
lltext.pdf
 Body gestures,
http://www.acs.org.au/documents/public/crpit/CRPITV36
Gunes.pdf
 Blood volume pulse,
http://affect.media.mit.edu/areas.php?id=sensing
 Galvanic Skin Response,
http://en.wikipedia.org/wiki/Skin_conductance
 The importance of visual aesthetics in HCI,
 http://www.interaction-
design.org/encyclopedia/visual_aesthetics.html

24. Thank You! 