Efficient Cancelable Iris Recognition Scheme Based on Modified Logistic Map

Abstract

This paper presents a chaos-based cancelable biometric scheme for iris recognition. Instead of using original iris features, a masked version of the features is generated through chaotic map encryption for increasing the iris recognition system privacy. The proposed framework for iris recognition includes iris localization, normalization, and feature extraction with Gabor filter. Finally, chaotic encryption is used for generating cancelable IrisCodes. A modification of the Logistic map is included to increase the key space, and hence, the privacy is enhanced. The encryption key depends on the input image. So, the resultant encrypted feature vector is sensitive to the key. Thus, the encrypted feature vector is robust as the key space is large. The proposed iris recognition framework excludes the effects of eyelids and eyelashes and masks the original Gabor features to increase the level of security. Matching is performed with a Hamming Distance (HD) metric. The proposed framework achieves promising recognition rates of 99.08% and an Equal Error Rate (EER) of 1.17%.