Master Thesis - Department of Computer Science

for a face. This was illustrated by experimentation in [94] where the reconstruction
of a face image from subbands upto a certain level, below that of the original face
image at the root, provides better performance for face recognition. It is thus often
necessary to eliminate the details for the representation of a face, and in such cases
the subband face is obtained by partial reconstruction. In this chapter, we provide an
algorithm to obtain optimum subject-specific subbands on the basis of minimization
of four different criteria. Results of our experimentation presented in section 3.4
will validate a hypothesis that “subband face representation provides efficient face
recognition”. Our approach is different from that suggested in [36, 40, 26, 140] where
the individual subbands of the face image have been used as features separately
or unified by subspace analysis techniques for face recognition. None of the past
approaches deals with a method to reconstruct a subband face using IDWT (synthesis
filter bank).
Face image
Subband faces
LSA (Linear Subspace Analysis):
Subbands
DWT IDWT
LSA
PCA, LDA, 2D−PCA, 2D−LDA, DCV.
(a)
Features
(b)
Subband selection
Find nearest
neighbor
Templates from
training set
Subband face
Distance
Figure 3.1: (a) Block diagram for generating subband face. The face image is decom-
posed using Discrete Wavelet Transform (DWT) into subbands. Selected subbands
are used to reconstruct the subband face using inverse DWT. (b) Subband faces are
projected onto a lower dimension featurespace using PCA or LDA (or their variants)
and matched using the nearest neighbor strategy.
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