Feature quality based score level fusion using relative entropy measure for Iris recognition

In this paper we propose an adaptive fusion approach for iris biometric. The proposed fusion method incorporates four matching algorithms using feature quality and relative entropy to enhance iris fusion performance. This method introduces relative entropy measure to the fusion process to assign low weighting coefficients to features with less information and higher weights to features with more information. We investigated the parameters which influence the rejection rates and acceptance rates to determine the optimal equal error rate. The best equal error rates were aimed at high recognition accuracy. The proposed method was tested on two public iris databases. CASIA left eye images produced 99.36% recognition accuracy and 0.041% equal error rate as compared to 98.93% recognition accuracy and 0.066% error rate produced by the weighted sum fusion. For the CASIA right eye images, the proposed method produced 99.18% recognition accuracy and 0.087% equal error rate as compared to weighted sum fusion with 98.81% recognition accuracy and 0.096% equal error rate. From the UBIRIS database, the proposed method produced 99.59% recognition accuracy and 0.038% equal error rate as compared to 98.53% recognition accuracy and 0.074% equal error rate produced by weighted sum fusion. The proposed method shows improved recognition performance in terms of AUC and the EER.