Master Thesis - Department of Computer Science

in the accuracy of face recognition. We will verify the efficiency of subband face
representation using experimental results, which will be discussed in section 3.4.
Fig. 3.4 shows the block diagram of IDWT that is used to reconstruct the original
image from different subbands at level-1 decomposition. Fig. 3.5 shows the block
diagram of the subband face reconstruction from level-2 subbands, by suppressing
(replacing the coefficients by zeros) only the approximation coefficients at level-2.
The subband face thus obtained will hence not posses the spectral energies in the
[0 − Π/4] subband which correspond to a smoothed version (i.e. average structure)
of the face image.
LL
LH
HL
HH
2
2
2
2
Rows
h(.)
g(.)
h(.)
g(.)
2
2
Columns
h(.)
g(.)
Reconstructed
image
Figure 3.4: Two dimensional inverse discrete wavelet transform (IDWT) for an image.
The rows are initially upsampled by factor of two and filtered using low pass h(.) and
high pass g(.) filters. This is followed by upsampling along columns by a factor of
two and applying the low pass h(.) and high pass g(.) filters along the columns to get
the reconstructed image.
Fig. 3.6 shows the original face images along with the subband faces generated by
suppressing the approximations at different levels. The face image in Fig. 3.6 (a) is
illuminated from the left and the corresponding subband faces shown in Figs. 3.6 (a1-
a3) have the effect of illumination reduced or almost eliminated. This can also be
observed for the face image in Fig. 3.6 (b) which is illuminated from the right, and
its corresponding subband faces in Figs. 3.6 (b1-b3) do not posses similar amount
of illumination variation. Fig. 3.6 (c) shows a face with frontal illumination, where
the intensity variations (edges) of the subband faces in Figs. 3.6 (c1-c5) are stronger
than the gray level image. It is also noticeable that the suppression of approximation
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