Fig 1.
Face recognition system.
Fig 2.
Learning and classification process.
Fig 3.
PCA-based face recognition system flow.
Fig 4.
Feature detection [49].
Fig 5.
Eigenfaces [49].
Fig 6.
Original images and projected images [49].
Fig 7.
Framework design model.
Fig 8.
Framework as a feature diagram.
Fig 9.
Image representation.
Fig 10.
Face detection.
Fig 11.
Pre-processing.
Fig 12.
Face separation [49].
Fig 13.
LBP.
Fig 14.
LBP weight.
Fig 15.
LBP result.
Fig 16.
Circle LBP.
Fig 17.
LTP.
Fig 18.
PCA.
Fig 19.
Original dataset [69].
Fig 20.
Eigenvalues and eigenvectors [69].
Fig 21.
Classification by using standard PCA [69].
Fig 22.
Linear (left) and nonlinear (right) data types [69].
Fig 23.
Original data [69].
Fig 24.
Standard PCA result [69].
Fig 25.
Original data [69].
Fig 26.
Kernel PCA result [69].
Fig 27.
Verification.
Fig 28.
Face recognition for smart phones.
Fig 29.
Case Study 2.
Fig 30.
Case Study 3.
Fig 31.
Case Study 4.