A Dimensionality Reduced Iris Recognition System with Aid of AI Techniques
Technologies that exploit biometrics have the potential for the identification and verification of individuals designed for controlling access to secured areas or materials. One of the biometrics used for the identification is iris. Many techniques have been developed for iris recognition so far. Here we propose a new iris recognition system utilizing unbalanced wavelet packets and FFBNN-ABC. In our proposed system, the eye images obtained from the iris database are preprocessed using the adaptive median filter to remove the noise. After removing the noise, iris part is localized by using contrast adjustment and active contour technique. Then unbalanced wavelet packets coefficients and Modified Multi Text on Histogram (MMTH) features are extracted from the localized iris image. Then MMTH features extracted are clustered by using the MFCM technique. After clustering, the dimensionality of the features is reduced by using PCA. Then the dimensionality reduced features & unbalanced wavelet packet coefficients are given to FFBNN to complete the training process. During the training, the parameters of the FFBNN are optimized using ABC Algorithm. The performance of our proposed iris recognition system is validated by using CASIA database and compared with the existing systems. Our proposed iris recognition system is implemented in the working platform of MATLAB.
