The principal component analysis (PCA) is a valuable statistical means,
implemented in time domain that has found application in many fields such as face
recognition and image compression, and is a common technique for finding patterns in
data of high dimension. This paper investigates the ability to implement PCA in
frequency domain, by using the wavelet transform (WT), and evaluate its effectiveness
based on face recognition as a means to find patterns in data. The basic idea of
frequency domain implementation of the PCA refers to the correlation
implementation using wavelet transform.
The Min-max is invoked to increase wavelet based eigenface robustness to
variations in facial geometry and illumination. Two face images are contrast in terms
of their correlation distance. A threshold is used to restrict the impostor face image
from being identified. Experimental results point up the effectiveness of a new method
in either using varying (noisy images, unknown images, face expressions, illumine,
and scales ).