Face Recognition Using Dual Tree Complex Wavelet Transform

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.