  Human identification has always been a topic that interested researchers
around the world. Biometric methods are found to be more effective and much
easier for the users than the traditional identification methods like keys,
smart cards and passwords. Unlike with the traditional methods, with biometric
methods the data acquisition is most of the times passive, which means the
users do not take active part in data acquisition. Data acquisition can be
performed using cameras, scanners or sensors. Human physiological biometrics
such as face, eye and ear are good candidates for uniquely identifying an
individual. However, human ear scores over face and eye because of certain
advantages it has over face. The most challenging phase in human identification
based on ear biometric is the segmentation of the ear image from the captured
image which may contain many unwanted details. In this work, PDE based image
processing techniques are used to segment out the ear image. Level Set Theory
based image processing is employed to obtain the contour of the ear image. A
few Level set algorithms are compared for their efficiency in segmenting test
ear images.
