Open AccessA Study about Principle Component Analysis and Eigenface for Facial Extraction
Author(s) -
Erwin Erwin,
Muhammad Azriansyah,
N Hartuti,
Muhammad Fachrurrozi,
Bayu Adhi Tama
Publication year - 2019
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1196/1/012010
Subject(s) - eigenface , principal component analysis , pattern recognition (psychology) , computer science , artificial intelligence , facial recognition system , feature extraction , precision and recall , component analysis , face (sociological concept) , feature (linguistics) , recall , speech recognition , psychology , cognitive psychology , social science , linguistics , philosophy , sociology
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.