Circular Hough Transformation Approach for Iris Detection

Iris recognition is an automated method of biometric identification that utilizations mathematical pattern-recognition techniques on video images of either of the irises of an individual's eyes, whose complex random patterns are unique, stable, and can be seen from some distance. Retinal scanning is a different, ocular-based biometric innovation that uses the unique patterns on a person's retina blood vessels and is regularly confused with iris recognition. Digital templates encoded from these patterns by mathematical and statistical algorithms allow the identification of an individual or somebody pretending to be that individual. Databases of enrolled templates are searched by matcher engines at speeds measured in the millions of templates every second per (single-core) CPU, and with remarkably low false match rates. To detect the iris from the image efficient feature extraction technique is required. In the base paper, the popular Transformation methods [Discrete cosine transform] DCT, [Discrete wavelet transform] DWT, and [Singular vector decomposition] SVD are used for analyzing and Feature Extraction. The proposed improvement will be based on applying GLCM algorithm which will extract the contrast, energy, entropy and heterogeneity of the detected iris has been calculated. To increase the accuracy of iris detection and reduce execution time, improvement in existing GLCM algorithm, feature extraction technique is being proposed. The technique Circular Hough Transformation and improved GLCM are used. The simulation is being performed in MATLAB and it has been analyzed that performance is increased in terms of certain parameters.