Master Thesis - Department of Computer Science
Master Thesis - Department of Computer Science
Master Thesis - Department of Computer Science
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(a) (b) (c)<br />
Figure 3.3: (a) Original face, (b) the level-1 wavelet decomposition <strong>of</strong> the face im-<br />
age into subbands A, H (top) and V , D (bottom) and (c) level-3 dyadic wavelet<br />
decomposition. All subband signals are normalized individually.<br />
ations) information. Since level-1 decomposition may not be adequate to effectively<br />
isolate these pair <strong>of</strong> visual features, it is necessary to explore multi-resolution, multi-<br />
channel representation at higher levels to obtain a suitable isolation. With higher<br />
levels <strong>of</strong> decomposition, the approximation will have increasing effect <strong>of</strong> smoothening<br />
(with lesser subband bandwidth) on the face image, and the details will have more <strong>of</strong><br />
the discriminatory information. At a certain higher level <strong>of</strong> decomposition, the entire<br />
discriminatory information will remain in the details and approximation will contain<br />
the common (or similar) structural information. In order to eliminate the similarity<br />
and retain the discrimination, the approximation is suppressed (by replacing with<br />
zeros) and the face image is reconstructed using IDWT.<br />
The details at lower levels (1 or 2) <strong>of</strong> decomposition also do not contain any useful<br />
information. This can be realized from the fact that humans do not need an image at<br />
a very large resolution to identify an individual. Too low a resolution, on the other<br />
hand, is not adequate to hold all <strong>of</strong> the discriminatory information. Hence there exists<br />
an optimal resolution at which the image contains most <strong>of</strong> the discriminatory and less<br />
(or no) redundant pixels (information). Thus it is <strong>of</strong>ten necessary to eliminate certain<br />
details at lower levels <strong>of</strong> decomposition to discard redundant image pixels or noise<br />
if present in the signal. Thus the subbands which only contain the discriminatory<br />
information for face recognition are selected for representation and the others are<br />
discarded. Based on the explanation presented above, we propose an efficient method<br />
<strong>of</strong> face recognition using subband face representation which provides an improvement<br />
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