Master Thesis - Department of Computer Science

ZEROS
LH2
HL2
HH2
2
2
2
Rows
h(.)
g(.)
h(.)
2 g(.)
2
2
Columns
h(.)
g(.)
2 h(.)
LH1 2
g(.)
HL1
HH1
2
2
Rows
h(.)
g(.)
2
Columns
h(.)
2 g(.)
Subband
face image
Figure 3.5: Reconstruction of a subband face using two dimensional Inverse Discrete
Wavelet Transform (IDWT) from level-2 subbands. The approximation at level-2,
LL2, is suppressed (by replacing with zeros) while reconstructing the approximation
at level-1, LL1. This is further used to generate the subband face at level-0.
subband at a larger level of decomposition leads to reconstruction of a face that is
similar to the original gray level image.
Let the original face image, at the root of the decomposition tree, be A0 and the
approximations at different levels of decomposition be denoted by A1, A2 and so on.
The subband face that is reconstructed with the approximation suppressed at level-i
be denoted as (A0 − Ai). In [94], it was shown that the subband face reconstructed
(using Haar transform) with the approximation suppressed at level-5, (A0 − A5),
gives the best performance for PCA based face recognition for Yale database. In this
chapter, we propose a method of implementation to obtain a subband face, (Al −Ak),
which is reconstructed to the level-l, after suppressing the approximation at level-
k, where 0 ≤ l < k ≤ log N, N ∗ N being the resolution of the image. Note that
this eliminates the details: Vi, Hi, Di, for levels i = 1, 2, . . . , l when l > 0, from the
original face image. It was found that for optimal performance of face recognition,
the levels of decomposition l and k depend on two factors:
• Subject under consideration,
• Linear subspace method used for face recognition
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