Publication
Modeling Affective State using Learning Vector Quantization
de Vries, J., 2014, [S.l.]: [S.n.]. 176 p.Research output: Thesis › Thesis fully internal (DIV) › Academic

Documents
- Title and contents
Final publisher's version, 188 KB, PDF document
- Chapter 1
Final publisher's version, 105 KB, PDF document
- Chapter 2
Final publisher's version, 905 KB, PDF document
- Chapter 3
Final publisher's version, 546 KB, PDF document
- Chapter 4
Final publisher's version, 751 KB, PDF document
- Chapter 5
Final publisher's version, 407 KB, PDF document
- Chapter 6
Final publisher's version, 4 MB, PDF document
- Chapter 7
Final publisher's version, 8 MB, PDF document
- Chapter 8
Final publisher's version, 117 KB, PDF document
- Publications
Final publisher's version, 84 KB, PDF document
- Samenvatting
Final publisher's version, 104 KB, PDF document
- Bibliography
Final publisher's version, 170 KB, PDF document
- Complete dissertation
Final publisher's version, 14 MB, PDF document
- Propositions
Final publisher's version, 56 KB, PDF document
The research performed shows that computers, based upon these self-learning systems, can detect emotions from photos of facial expressions and can detect stress from cardiac signals, both with high accuracy. The methods used, also provide models based upon which new knowledge can be gained. As an example, the mouth and eyes were found most vital for recognizing facial expressions and it was found that emotion recognition from physiology can be improved by adding measurements of specific heart frequencies.
Original language | English |
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Qualification | Doctor of Philosophy |
Awarding Institution | |
Supervisors/Advisors |
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Award date | 28-Nov-2014 |
Place of Publication | [S.l.] |
Publisher | |
Print ISBNs | 978-90-367-7387-4 |
Electronic ISBNs | 978-90-367-7388-1 |
Publication status | Published - 2014 |
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