Facial feature point detection is a key issue in facial image processing. One main challenge of facial feature point detection is the variation of facial structures due to expressions. This thesis aims to explore more accurate and robust facial feature point detection algorithms, which can facilitate the research on facial image processing, in particular the facial expression analysis. This thesis introduces a facial feature point detection system, where the Multilinear Principal Component Analysis is applied to extract the highly descriptive features of facial feature points. In addition, to improve the accuracy and efficiency of the system, a skin color based face detection algorithm is studied. The experiment results have indicated that this system is effective in detecting 20 facial feature points in frontal faces with different expressions. This system has also achieved a higher accuracy during the comparison with the state-of-the-art, BoRMaN.
| Identifer | oai:union.ndltd.org:LACETR/oai:collectionscanada.gc.ca:OTU.1807/30546 |
| Date | 06 December 2011 |
| Creators | Chen, Fang |
| Contributors | Plataniotis, Konstantinos N. |
| Source Sets | Library and Archives Canada ETDs Repository / Centre d'archives des thèses électroniques de Bibliothèque et Archives Canada |
| Language | en_ca |
| Detected Language | English |
| Type | Thesis |
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