  This chapter shows that combining Haar-Hilbert and Log-Gabor improves iris
recognition performance leading to a less ambiguous biometric decision
landscape in which the overlap between the experimental intra- and interclass
score distributions diminishes or even vanishes. Haar-Hilbert, Log-Gabor and
combined Haar-Hilbert and Log-Gabor encoders are tested here both for single
and dual iris approach. The experimental results confirm that the best
performance is obtained for the dual iris approach when the iris code is
generated using the combined Haar-Hilbert and Log-Gabor encoder, and when the
matching score fuses the information from both Haar-Hilbert and Log-Gabor
channels of the combined encoder.
