Iris Recognition: What Is Beyond Bit Fragility?

The concept of fragility of some bits in the iris codes regards exclusively their within-class variation, i.e., the probability that they take different values in templates computed from different images of the same iris. This paper extends that concept, by noticing that a similar phenomenon occurs for the between-classes comparisons, i.e., some bits have higher probability than others of assuming a predominant value, which was observed for near-infrared and (in a more evident way) for visible wavelength data. Accordingly, we propose a new measure (bit discriminability) that considers both the within-class and between-classes variabilities, and has roots in the Fisher discriminant. Based on the bit discriminability, we compare the usefulness of the different regions of the iris for biometric recognition, with respect to multispectral data and to different filters parameterizations. Finally, we measure the amount of information lost in codes quantization, which gives insight to further research on iris matching strategies that consider both phase and magnitude. Albeit augmenting the computational burden of recognition, such kind of strategies will consistently improve performance, particularly in poor-quality data.