

![0_image_0.png](0_image_0.png)

The coordinate transformation process produces a 448 × 96 biometric pattern for each meaning ROI: 448 is the No. of the chosen radial samples (to avoid data loss in the round angle), while 96 pixels are the highest difference between iris and pupil radius in the iris images. In order to achieve invariance with regards to roto-translation and scaling distortion, the r polar coordinate is normalized in the [0, 1] range. For each Cartesian point of the ROI, image is assigned a polar coordinates pair *(r, θ)*, with r ∈ [R1*, R2*] and θ ∈ [0, 2π], where R1 is the pupil radius and R2 is the iris radius, as shown in figure 3. The result of the normalized image is shown in figure 4.

![0_image_1.png](0_image_1.png)

## 3. Feature Extraction

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 [13].

The template that is generated in the feature encoding process will also need a corresponding matching metric, which gives a measure of similarity between two iris templates. This metric should give one range of values when comparing templates generated from the same eye, known as intra-class comparisons, and another range of values when comparing templates created from different irises, known as inter-class comparisons. These two cases should give distinct and separate values, so that a decision can be made with high confidence as to whether two templates are from the same iris, or from two different irises.

Feature extraction is a special form of dimensionality reduction. When the input data is too large to be processed and it is suspected to be notoriously redundant (much data, but not much information) then the input data will be transformed into a reduced representation set of features. For the purpose of feature extraction we will be Transforming into a reduced representation set of features is called feature extraction using the following three algorithms Zero crossing based 1-D wavelet