Open AccessImproved accuracy of recognizing of low-quality face images using two directional matrix in 2D-PCA algorithms and euclidean distance
Author(s) -
R D K Kuncoro,
Endang Sugiharti
Publication year - 2021
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 - 10.1088/1742-6596/1918/4/042142
Subject(s) - artificial intelligence , pattern recognition (psychology) , facial recognition system , euclidean distance , eigenface , face (sociological concept) , computer science , feature extraction , image (mathematics) , matrix (chemical analysis) , feature (linguistics) , principal component analysis , computer vision , social science , linguistics , philosophy , materials science , sociology , composite material
Face recognition is a technique that can be used to distinguish the characteristic facial patterns of a person. A very influencing factor in the facial recognition process is image quality, so it can affect the level of accuracy. Improving the accuracy of low-quality facial recognition can be done by processing the image for its feature extraction using Two Directional Matrix on 2D-PCA. From the extraction process, the Eigenfaces value is then generated to be classified. Image classification is done by using Euclidean Distance. Furthermore, the accuracy results between image recognition with or without Two Directional Matrix will be compared. The data used were the AT&T face of database and database of Essex. Of the 18 tests carried out on the Two Directional Matrix method, it was proven to improve facial recognition accuracy by as many as 12 trials. In 5 other experiments the accuracy decreased and 1 experiment was unable to increase or decrease facial recognition accuracy. The highest accuracy result in the experiment using AT&T was 98.25%, while in Essex it was 97.27%. Suggestions for further research by conducting experiments with more diverse dataset ratios in order to get better low-quality image accuracy results.