## 3 Literature Survey
### Feature Extraction  
Several models are used to extract iris characteristics. The Gabor filter plays a crucial role in the depiction of iris texture because it delivers the best trade-off between spatial and frequency resolution. Iris texture will be divided into frequency over a range of sizes and orientations using gabor filtering. A survey of the literature reveals that different values of the filter parameters are utilized with Gabor filters, leading to different impacts on texture classification. Extremely dissimilar options emerged in the literature when researchers worked to find Gabor parameters in Gabor functions, suggesting that there is no standard  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Solution Pace** & **Formula 1** & **Formula 2** & **Formula 3** & **Formula 4** \\ \hline \multirow{3}{*}{\(b\in\{-1,1\}^{n}\)} & \multirow{3}{*}{\(\parallel b-f\parallel_{2}^{2}\)} & \multirow{3}{*}{\(\sum_{i}(-f_{i})b_{i}+\)} & \multirow{3}{*}{\(\sum_{i}(-f_{i})b_{i}+\)} & \multirow{3}{*}{\(\sum_{i}\sum_{j\
eq i}b_{i}b_{j}\)} & \multirow{3}{*}{\(\alpha\sum_{i}\sum_{j\in N}\left|b_{i}-b_{j}\right|+\)} \\  & & & & \\ \cline{1-1}  
arrangement of Gabor filters for various texture representations. Ref. [25], state that Gabor filters with the right settings can significantly increase recognition rates because their filter banks may produce higher coverage in the Fourier domain, enabling the extraction of more discriminative information. Because of this, it might be challenging to determine the ideal Gabor filters for iris imaging from a range of acquisition situations, user groups, and devices. However, a thorough search of all viable parameters might not be feasible since Gabor parameter selection is a nonlinear, constrained programming problem with a number of continuous and discrete variables. The time-frequency transition makes it challenging to create a mathematical goal function between the performance of iris recognition using Gabor kernels. An iris data-driven Gabor filter optimization method is presented in [25], and is based on particle swarm optimization (PSO) and its binary version binary particle swarm optimization (BPSO). The latter are intended to find appropriate Gabor kernels and prevent the unreasoning predefinition of the Gabor filter. To build a set of data-driven Gabor kernels for fitting the most informative filtering bands, they used the particle swarm optimization technique and its binary counterpart. Next, they trained a deep belief network to capture complicated patterns from the ideal Gabor filtered coefficients.  
The feature extraction step, which has the greatest impact on recognition performance, has recently been the subject of a lot of research investigations. Iris analysis in a certain transform domain using a specified type of filter is the most often used and well-studied approach among the several feature extraction methods. These techniques have the ability to identify texture characteristics that are absent in the spatial domain and to avoid noise in spatial iris pictures. However, they must all consider choosing appropriate filters and transform kernels. In other studies, the idea was to describe iris texture information using geometric descriptors. They could simply and naturally design in the spatial realm. The performance of this type of approach is, however, rather prone to iris image noises. Combining several iris feature types is now thought to be a potential strategy. They might make up for the unimodal iris feature's shortcomings, but doing so would incur computing complexity and time costs. In Table 2, we provide overview of previous approaches for feature extraction in iris recognition.