Master Thesis - Department of Computer Science

on three face databases and compared with the state-of-art closest competitor [36] of
it. Section 3.5 concludes the chapter.
3.1 Introduction and Motivation
How do humans identify individuals with remarkable ease and accuracy? This ques-
tion has haunted psychologists, neurologists and, recently, engineers in biometry for a
long time. The human face is different from any other natural or man-made objects,
but has similar structural features across different races, sex and regions. The subtle
variations in the face structure are captured by the human brain and help in discrim-
inating one face from the another. The human brain is able to filter out the common
visual features of a face and retain only those suitable to exhibit the unique charac-
teristics (discriminatory evidence) of an individual. An efficient face-based biometric
system must possess these properties (as in those in the retina and visual cortex), to
perform efficiently like a human being.
Wavelet-based features have already been used to obtain a better face representa-
tion in [36, 40, 26, 140]. The use of wavelet packet for face recognition was reported in
[40]. Here, 2D-DWT is used to fully decompose the face image and simple statistical
features such as mean and variance are extracted from the decomposed coefficients,
and used as a feature vector for representation. The use of wavelet subband represen-
tation along with kernel associative memory for face recognition was reported in [140].
Here wavelet decomposed faces are used to build an associative memory model for
each class and kernel methods are used to exploit higher order relations which cannot
be captured by linear transformations. Face images with illumination variations can
be modeled by low-dimensional linear subspaces. The existence of single light source
directions that are effective for face recognitions was discussed in [75]. Ekenel [36] et
al. proposed multiresolution face recognition with fusion at data, feature and deci-
sion levels to test on face images that differ in expression or illumination separately,
obtained from CMU PIE, FERET and Yale databases. Significant performance gains
are reported against illumination perturbations. They selected a number of subbands
on the basis of performance on testing set and termed them as successful subbands.
Data, feature and decision level fusion are performed on these successful subbands to
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