  The randomness and uniqueness of human eye patterns is a major breakthrough
in the search for quicker, easier and highly reliable forms of automatic human
identification. It is being used extensively in security solutions. This
includes access control to physical facilities, security systems and
information databases, Suspect tracking, surveillance and intrusion detection
and by various Intelligence agencies through out the world. We use the
advantage of human eye uniqueness to identify people and approve its validity
as a biometric. . Eye detection involves first extracting the eye from a
digital face image, and then encoding the unique patterns of the eye in such a
way that they can be compared with pre-registered eye patterns. The eye
detection system consists of an automatic segmentation system that is based on
the wavelet transform, and then the Wavelet analysis is used as a pre-processor
for a back propagation neural network with conjugate gradient learning. The
inputs to the neural network are the wavelet maxima neighborhood coefficients
of face images at a particular scale. The output of the neural network is the
classification of the input into an eye or non-eye region. An accuracy of 90%
is observed for identifying test images under different conditions included in
training stage.
