The dramatic growth in practical applications for iris biometrics has been accompanied
by relevant developments in the underlying algorithms and techniques. Along
with the research focused on near-infrared images captured with subject cooperation,
e orts are being made to minimize the trade-o between the quality of the captured
data and the recognition accuracy on less constrained environments, where images are
obtained at the visible wavelength, at increased distances, over simpli ed acquisition
protocols and adverse lightning conditions. At a rst stage, interpolation e ects on
normalization process are addressed, pointing the outcomes in the overall recognition
error rates. Secondly, a couple of post-processing steps to the Daugman's approach
are performed, attempting to increase its performance in the particular unconstrained
environments this thesis assumes. Analysis on both frequency and spatial domains
and nally pattern recognition methods are applied in such e orts. This thesis embodies
the study on how subject recognition can be achieved, without his cooperation,
making use of iris data captured at-a-distance, on-the-move and at visible wavelength
conditions. Widely used methods designed for constrained scenarios are analyzed
