Recognition of iris based on visible light (VL) imaging is a difficult problem because of the light reflection from the cornea. Nonetheless, pigment melanin provides a rich feature source in VL, which is unavailable in near-infrared (NIR) imaging. This is due to the biological spectroscopy of eumelanin, a chemical not stimulated in NIR. In this case, a plausible solution to observe such patterns may be provided by an adaptive procedure using a variational technique on the image histogram. To describe the patterns, a shape analysis method is used to derive the feature code for each subject. An important question is how the melanin patterns, which are extracted from VL, are independent of the iris texture in NIR. With this question in mind, the present investigation proposes fusion of features extracted from NIR and VL to boost recognition performance. We have collected our own database (UTIRIS), consisting of both NIR and VL images of 158 eyes of 79 individuals. This investigation demonstrates that the proposed algorithm is highly sensitive to the patterns of chromophores and improves the iris recognition rate.