Iris recognition performance enhancement using weighted majority voting

Biometric authentication is a convenient and increasingly reliable way to prove one's identity. Iris scanning in particular is among the most accurate biometric authentication technologies currently available. However, despite their extremely high accuracy under ideal imaging conditions, existing iris recognition methods degrade when the iris images are noisy or the enrollment and verification imaging conditions are substantially different. To address this issue and enable iris recognition on less-than-ideal images, we introduce a weighted majority voting technique applicable to any biometric authentication system using bitwise comparison of enrollment-time and verification-time biometric templates. In a series of experiments with the CASIA iris database, we find that the method outperforms existing majority voting and reliable bit selection techniques. Our method is a simple and efficient means to improve upon the accuracy of existing iris recognition systems.