Open AccessAccuracy enhancement in face recognition using 1D-PCA & 2D-PCA based on multilevel reverse-biorthogonal wavelet transform with KNN classifier
Author(s) -
Iin Dinariyah,
Alamsyah Alamsyah
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 - 10.1088/1742-6596/1918/4/042144
Subject(s) - artificial intelligence , facial recognition system , pattern recognition (psychology) , computer science , biorthogonal system , face (sociological concept) , biometrics , classifier (uml) , k nearest neighbors algorithm , principal component analysis , computer vision , wavelet transform , wavelet , social science , sociology
Face recognition is a physical biometric technique that allows computers to recognize human faces. Currently, face recognition is widely used in the authentication security system. Accuracy improvement in face recognition can be made by doing image decomposition using multi-level reverse biorthogonal wavelets. Composed image features will be extracted using 1D-PCA and 2D-PCA. Face classification was carried out by using the K-Nearest Neighbor algorithm. Furthermore, the face recognition accuracy results using and without the wavelet decomposition will be compared. This research used 565 images in which 400 face images are obtained from the AT&T database, and 400 face images are obtained from the YALE database. This study revealed that the highest accuracy in face recognition using AT&T is98.75% which obtained by 60% training images and 40% testing images, while in face recognition using YALE highest accuracy is obtained on face recognition using 90% training images and 90% testing images with 100% accuracy.