Spatio-temporal framework and algorithms for video-based face recognition
The objective of this research is to devise algorithms for video-based face recognition based on spatio-temporal manifolds. The primary focus is to better represent nonlinear face patterns on a data manifold, while incorporating spatio-temporal information that is inherent in videos. In addition, a...
Saved in:
| 主要作者: | |
|---|---|
| 格式: | Thesis |
| 出版: |
2014
|
| 主題: | |
| 標簽: |
添加標簽
沒有標簽, 成為第一個標記此記錄!
|
| id |
my-mmu-ep.5988 |
|---|---|
| record_format |
uketd_dc |
| spelling |
my-mmu-ep.59882015-06-30T09:06:03Z Spatio-temporal framework and algorithms for video-based face recognition 2014-05 See, John Su Yang TA Engineering (General). Civil engineering (General) TA1501-1820 Applied optics. Photonics The objective of this research is to devise algorithms for video-based face recognition based on spatio-temporal manifolds. The primary focus is to better represent nonlinear face patterns on a data manifold, while incorporating spatio-temporal information that is inherent in videos. In addition, a systematic approach to evaluate sampling is also crucial as current experimental setups are inadequate to provide balanced evaluation. These goals are inspired by motivations from computer vision, psychology and cognitive neuroscience. 2014-05 Thesis http://shdl.mmu.edu.my/5988/ http://library.mmu.edu.my/diglib/onlinedb/dig_lib.php phd doctoral Multimedia University Faculty of Computing and Informatics |
| institution |
Multimedia University |
| collection |
MMU Institutional Repository |
| topic |
TA Engineering (General) Civil engineering (General) TA Engineering (General) Civil engineering (General) |
| spellingShingle |
TA Engineering (General) Civil engineering (General) TA Engineering (General) Civil engineering (General) See, John Su Yang Spatio-temporal framework and algorithms for video-based face recognition |
| description |
The objective of this research is to devise algorithms for video-based face recognition based on spatio-temporal manifolds. The primary focus is to better represent nonlinear face patterns on a data manifold, while incorporating spatio-temporal information that is inherent in videos. In addition, a systematic approach to evaluate sampling is also crucial as current experimental setups are inadequate to provide balanced evaluation. These goals are inspired by motivations from computer vision, psychology and cognitive neuroscience. |
| format |
Thesis |
| qualification_name |
Doctor of Philosophy (PhD.) |
| qualification_level |
Doctorate |
| author |
See, John Su Yang |
| author_facet |
See, John Su Yang |
| author_sort |
See, John Su Yang |
| title |
Spatio-temporal framework and algorithms for video-based face recognition |
| title_short |
Spatio-temporal framework and algorithms for video-based face recognition |
| title_full |
Spatio-temporal framework and algorithms for video-based face recognition |
| title_fullStr |
Spatio-temporal framework and algorithms for video-based face recognition |
| title_full_unstemmed |
Spatio-temporal framework and algorithms for video-based face recognition |
| title_sort |
spatio-temporal framework and algorithms for video-based face recognition |
| granting_institution |
Multimedia University |
| granting_department |
Faculty of Computing and Informatics |
| publishDate |
2014 |
| _version_ |
1747829603351658496 |