Compound emotion detection

Simple item page
dc.contributor.authorNadeem Arif
dc.contributor.authorJuniad Jabbar Faizi
dc.contributor.authorRaees Hafeez
dc.contributor.authorHassan Ali
dc.date.accessioned2018-02-23T11:49:02Z
dc.date.available2018-02-23T11:49:02Z
dc.date.issued2017
dc.descriptionSupervised by: Syed Farooq Alien_US
dc.description.abstractEmotion recognition and detection is an emerging area of research for the last decade. The studies on emotions highlights the fact that the focus of researchers has been further extended from basic emotions to compounds emotions due to its vast applications in surveillance systems, suspicious person detection, pain detection, automated patients observation in hospitals and driver monitoring. The study proposes and implements novel approaches using the Martinez dataset [2] that compute various features using facial fiducial points given in Martinez dataset to recognize basic and compound emotions. The results show that our approaches outperform the existing approach given in Martinez paper [2] using the same data set in terms of accuracy and time efficiency. Different approaches applied which have list of feature used to get the accuracy of 77.33 %. List of features include angles, length to width ratio of eyes and lips, triangles, ratio of triangles and angles, slope table, histogram of gradients, regional properties.en_US
dc.identifier.urihttps://escholar.umt.edu.pk/handle/123456789/2733
dc.language.isoenen_US
dc.publisherUniversity of Management and Technology Lahoreen_US
dc.subjectEmotion recognitionen_US
dc.subjectCompounds emotionsen_US
dc.subjectBS Thesisen_US
dc.titleCompound emotion detectionen_US
dc.titleCompound emotion detectionen_us
dc.typeThesisen_US
Files
Original bundle
Now showing 1 - 2 of 2
Thumbnail Image
Name:
Summary.pdf
Size:
180.1 KB
Format:
Adobe Portable Document Format
Description:
Download
No Thumbnail Available
Name:
Full View.htm
Size:
23.33 KB
Format:
Hypertext Markup Language
Description:
Download
License bundle
Now showing 1 - 1 of 1
No Thumbnail Available
Name:
license.txt
Size:
1.71 KB
Format:
Item-specific license agreed upon to submission
Description:
Download
Collections
Department of Computer Science