PROJECT TITLE :

Gender classification from the same iris code Used for recognition - 2016

ABSTRACT:

Previous researchers have explored numerous approaches for predicting the gender of an individual primarily based on the features of the iris texture. This paper is the primary to predict gender directly from the same binary iris code that would be used for recognition. We have a tendency to found that the knowledge for gender prediction is distributed across the iris, rather than localized in specific concentric bands. We have a tendency to also found that using selected features representing a subset of the iris region achieves higher accuracy than using options representing the full iris region. We have a tendency to used the measures of mutual information to guide the selection of bits from the iris code to use as options in gender prediction. Using this approach, with someone-disjoint coaching and testing analysis, we have a tendency to were able to realize eighty ninepercent correct gender prediction using the fusion of the best features of iris code from the left and right eyes.