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This paper presents a novel approach towards iris recognition based on dual boundary (Pupil-Iris & Sclera-Iris) detection and then using a modified Multilayer Feed Forward neural network (MFNN) to perform an efficient automatic classification. The novelty of the work resides in the fact that the proposed method features the localization of the dual iris boundaries to be used as feature vector for classification. The process of information extraction starts by preprocessing the eyeimage to remove specular highlight and then locating the pupil of the eye by using edge detection. The centroid of the detected pupil is chosen as the reference point for extracting the boundary points. The boundary points are recorded using radius vector functions approach. The proposed feature vector is obtained by concatenating the contour points of the Pupil-Iris boundary and the Sclera-Iris boundary which will yield a unique pattern named as Iris signature. The proposed method is translational and scale invariant. The classification is performed using the MFNN via a modified version of back-propagation algorithm which uses a time varying learning rate. The proposed system has been tested on moderate no of pictures taken from MMU iris database in the presence of additive noise for different values of signal-to-noise ratio (SNR). Experimental result for percentage recognition shows that the proposed method outperforms the single boundary method.

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Due to increasing proliferation of multimedia signals, specifically, image, video and their applications in our daily life, it is indispensable to have methods that can efficiently predict and correct visual quality of images with high measures of accuracy. Therefore, in this work a state-of-theart (STOA) image quality assessment (IQA) metric, gradient magnitude similarity deviation (GMSD) has been incorporated in a STOA least-square-based non-local means (NLM) filtering framework for image denoising. The denoising process works by estimating and weighting neighbouring patches similar to the patch being denoised in terms of Euclidean distance (ED) and GMSD coefficient. The overall process is broken down into two steps; initially, local noise estimates for the underlying noisy patch are approximated and removed, then the refined patch is fed to the weighting process as the final step. Further, the proposed methodology also helps in mitigating the patch jittering blur effect (PJBE) and over smoothing of denoised images as observed with conventional NLM algorithm. Experimental evaluations based on visual-quality assessment and least-squarebased metrics have shown that the proposed algorithm yields better denoised image estimates than the conventional NLM algorithm. Moreover, experiments conducted on a subjective database, i.e. CSIQ, have shown higher performance in terms of peak signal-to-noise ratio (PSNR), structural similarity (SSIM) and GMSD coefficients. The resultant denoised images were in high correlation with the subjective judgements compared to the ones obtained with conventional NLM algorithm.Index Terms-Image quality assessment, perceptual image filtering, non-local means filter, image denoising, visual perception.

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