Abstract:
We propose a novel face recognition using Dual Tree
Complex Wavelet Transform (DTCWT), which is
used to extract features from face images. The
Complex Wavelet Transform is a tool that uses a dual
tree of wavelet filters to find the real and imaginary
parts of complex wavelet coefficients. The DT-CWT
is, however, less redundant and computationally
efficient. CWT is a relatively recent enhancement to
the discrete wavelet transform (DWT). We show that
it is a well-suited basis for this problem as it is
directionally selective, smoothly shift invariant,
optimally decimated at coarse scales and invertible
(no loss of information). Our face recognition scheme
is fast because of the decimated nature of the
DTCWT. Dual Tree methods are based on image at
different resolution. Normalization is done to reduce
dimensionality which will reduce memory problem
and computation time. Here Principal Component
Analysis which is a linear dimensionality reduction
technique, that attempt to represent data in lower
dimensions, is used to perform the face recognition.
PCA is applied that deals with the decomposition of
the training set into the Eigenvectors called Eigen
faces. Various discrimination analyzes such as,
Euclidean, L1, L2 and Cosine similarity are used for the recognition of face images.
