DWT Based Feature Extraction for Iris Recognition

DWT Based Feature Extraction for Iris Recognition

Abstract— A unique approach to create an iris recognition system in which a mechanism that uses canny edge detection scheme and a circular Hough transform to determine the iris boundaries in the eye image which are used. Later two level discrete wavelet transform is applied to extract the patterns in a person’s iris in the form of a feature vector. Matching is done using pair wise distance, which computes the Euclidean distance between two pairs of iris in data matrix. To conclude, our method has achieved a better total successive rate (TSR) and we have reduced equal error rate < Final Year Projects 2016 > EER, false accept rate (FAR) and false reject rate (FRR).There are various types of biometric recognition systems which include face, fingerprint, iris, palm, signature, voice, etc. Biometric identification systems rely on number of
random variations among people. The more complex the randomness the better it is, as more dimensions of independent variation produce greater uniqueness.