Optimal wavelength band clustering for multispectral iris recognition

Abstract

This work explores the possibility of clustering spectral wavelengths based on the maximum dissimilarity of iris textures. The eventual goal is to determine how many bands of spectral wavelengths will be enough for iris multispectral fusion and to find these bands that will provide higher performance of iris multispectral recognition. A multispectral acquisition system was first designed for imaging the iris at narrow spectral bands in the range of 420 to 940 nm. Next, a set of 60 human iris images that correspond to the right and left eyes of 30 different subjects were acquired for an analysis. Finally, we determined that 3 clusters were enough to represent the 10 feature bands of spectral wavelengths using the agglomerative clustering based on two-dimensional principal component analysis. The experimental results suggest (1) the number, center, and composition of clusters of spectral wavelengths and (2) the higher performance of iris multispectral recognition based on a three wavelengths-bands fusion.

IREX IX part two:

“…al. [39] similarly found that accuracy could be improved by combining scores acquired at various illumination wavelengths. Most of the improvement in their analysis occurred when they combined scores acquired at 700 and 850 nm within the NIR band.…”

Multiple local feature representations and their fusion based on an SVR model for iris recognition using optimized Gabor filters

“…Tan et al utilized ordinal measures, color analysis, texture representation, and semantic information as iris features as well as the weighted sum rule to generate the fused score for classification [15]. Gong et al selected three wavelength bands to represent an iris and then integrated them using agglomerative clustering based on a two-dimensional principal component analysis [16]. The fusion of multiple features is regarded as a positive step towards the development of extremely ambitious types of iris recognition [17].…”