A Study about Principle Component Analysis and Eigenface for Facial Extraction. (March 2019)

• Record Type: Journal Article
• Authors: Erwin, ; Azriansyah, M; Hartuti, N; Fachrurrozi, Muhammad; Adhi Tama, Bayu
• Is Part Of: Journal of physics. Volume 1196(2019)
• Publication Date: 2019-03
• Subjects: Physics -- Congresses; 530.5
• DOI: 10.1088/1742-6596/1196/1/012010
• Languages: English
• ISSNs: 1742-6588

Abstract

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.