https://jjcit.org/paper/156
AN IMPROVED FRACTIONAL TWO-DIMENSIONAL PRINCIPAL COMPONENT ANALYSIS FOR FACE RECOGNITION
10.5455/jjcit.71-1637874750
Falah Alsaqre
Face recognition,Feature extraction,Fractional covariance matrix,2DPCA,F2DPCA
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613
25-Nov.-2021
15-Jan.-2022
26-Jan.-2022
Two-dimensional principal component analysis (2DPCA) is a subspace technique used for facial image
representation and recognition. Standard 2DPCA may be unable to extract informative features to adequately
describe the inherent structural information of the original facial images with the presence of irrelevant variations,
such as lighting conditions, facial expressions and so on. To deal with this, an improved fractional two-
dimensional principal component analysis (IF2DPCA) is proposed in this paper. It is an extension of fractional
2DPCA (F2DPCA), which was developed based on the concept of fractional covariance matrix (FCM). IF2DPCA
employs the same principle as F2DPCA for learning a projective matrix, but further extends the use of fractional
transformed 2D images throughout the entire recognition task. As a result, the feature subspace modeled by
IF2DPCA maintains the most informative content of the 2D face images and is relatively insensitive to irrelevant
variations. Experimental results on three face datasets confirm the effectiveness of the suggested IF2DPCA method
in facial recognition.