Biometric systems have achieved a great deal of success for identity recognition of individuals in
most of the civilian, law-enforcement, and forensic applications in recent years. The ever increasing
popularity of biometric systems offer reliable identity recognition than traditional possession
and knowledge based approaches, as biometric characteristics cannot be shared, forgotten, or lost.
Biometric recognition operation refer to verification or identification of individuals based on physiological
or behavioral characteristics such as fingerprint, palmprint, iris, face, ear, gait, signature,
voice etc. Over last two decades, the ear has been predominantly attracted many researchers as an
emerging biometric trait due to its encouraging features such as uniqueness, consistent shape, high
acceptability, easy collectability, and passive biometrics.
Despite of several inherent advantages of ear biometrics, issues in uncontrolled scenarios such as
illumination variation, pose changes, poor contrast, partial occlusion, and presence of noise restrict
to increase recognition performance. This opportunity gives sufficient chance for the recognition
improvement in ear biometric system. This factor motivates us to investigate the potential of ear
biometric characteristic with 2-D imagery. Objective of this thesis is to improve recognition performance
of the ear based unimodal and multimodal biometric systems.
