Abstract
In order to provide accurate recognition of individuals, the most discriminating information present in an iris pattern must be extracted. Only the significant features of the iris must be encoded so that comparisons between templates can be made. Most iris recognition systems make use of a band pass decomposition of the iris image to create a biometric template. In this paper, the feature extraction techniques are improved and implemented. These techniques are using wavelet filters. The encoded data by wavelet filters are converted to binary code to represent the biometric template. The Hamming distance is used to classify the iris templates, and the False Accept Rate (FAR), False Reject Rate (FRR) and recognition rate (RR) are calculated [1].
The wavelet transform using DAUB12 filter proves that it is a good feature extraction technique. It gives equal FAR and FRR and a high recognition rate for the two used databases. When applying the DAUB12 filter to CASIA database, the FAR and FRR are equal to 1.053%, while the recognition rate is 97.89%. For Bath database the recognition rate when applying DAUB12 filter is 100%. CASIA and Bath databases are obtained through personal communication. These databases are used in this paper.
