Master Thesis - Department of Computer Science
Master Thesis - Department of Computer Science
Master Thesis - Department of Computer Science
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Abstract<br />
Biometrics is a rapidly evolving technology, which has been widely used in forensics<br />
such as criminal identification, secured access, and prison security. A biometric system<br />
is essentially a pattern recognition system that recognizes a person by determining<br />
the authenticity <strong>of</strong> a specific physiological and/or behavioral characteristic possessed<br />
by that person. Face is one <strong>of</strong> the commonly acceptable biometrics used by humans<br />
in their visual interaction. The challenges in face recognition stem from various issues<br />
such as aging, facial expressions, variations in the imaging environment, illumination<br />
and pose <strong>of</strong> the face.<br />
In this thesis, we propose three novel techniques for extraction <strong>of</strong> facial features<br />
and recognition <strong>of</strong> faces from frontal and near-frontal face images. The principal<br />
objective <strong>of</strong> facial feature extraction is to capture certain discriminative features that<br />
are unique for a person. In the first face recognition technique, we propose a new<br />
method for representing faces, called as subband face representation. Subject-specific<br />
subband face extracts features that are invariant within a subject and at the same<br />
time distinguishable across different subjects. This method involves the process <strong>of</strong><br />
selecting suitable subbands <strong>of</strong> a face, and then reconstructing it using Inverse Discrete<br />
Wavelet Transform (IDWT), based on certain criteria. Subband face representation<br />
has been integrated with recent linear subspace analysis techniques to obtain an<br />
efficient face recognition system. Other two proposed face recognition techniques<br />
deal with two subspaces, namely, range space and null space <strong>of</strong> within-class scatter<br />
which constitute the entire face space if combined. The range space holds the entire<br />
intra-class variations and the null space contains the intra-class commonalities present<br />
across samples containing variations in expression, illumination and pose. Two <strong>of</strong> the<br />
proposed methods <strong>of</strong> face recognition combine discriminative features from null space<br />
and range space to utilize the whole gamut <strong>of</strong> discriminative informations present<br />
in the face space, using feature fusion and decision fusion. Feature fusion based<br />
method uses Gramm-Schmidt Orthonormalization and covariance sum method to