Erwin, Erwin and Muhammad, Fachrurrozi (2017) A Study about Principle Component Analysis and Eigenface for Facial Extraction. Journal of Physics: Conf. Series 1282. ISSN 17426596
![[thumbnail of Artikel]](http://repository.unsri.ac.id/style/images/fileicons/text.png) |
Text (Artikel)
A Study about Principle.pdf - Published Version Download (626kB) |
Abstract
Facial recognition is one of the most successful applications of image analysis and understanding. This paper presents a Principal Component Analysis (PCA) and eigenface method for facial feature extraction. Several performance metrics, i.e. accuracy, precision, and recall are taken into account as a baseline of experiment. Furthermore, two public data sets, namely SOF (Speech on faces) and MIT CBCL Facerec are incorporated in the experiment. Based on our experimental result, it can be revealed that PCA has performed well in terms of accuracy, precision, and recall metrics by 0.598, 0.63, and 0.598, respectively.
| Item Type: | Article |
|---|---|
| Subjects: | Q Science > Q Science (General) > Q334-342 Computer science. Artificial intelligence. Algorithms. Robotics. Automation. |
| Divisions: | 09-Faculty of Computer Science > 55201-Informatics (S1) |
| Depositing User: | Dr. Muhammad Fachrurrozi |
| Date Deposited: | 05 Apr 2023 11:59 |
| Last Modified: | 05 Apr 2023 11:59 |
| URI: | http://repository.unsri.ac.id/id/eprint/92459 |
Actions (login required)
 |
View Item |