## 3 Literature Survey
### Feature Selection and Iris Coding  
The following operational modules should fundamentally be included in an iris recognition system: picture capture, iris segmentation, normalization, feature extraction, matching, and recognition [29]. The two modules that directly affect the overall system accuracy out of these four are iris segmentation and feature extraction. As a result, these modules alone are the subject of the majority of the linked study. Feature selection blocks have only been added by a few numbers of researchers to the modules for iris recognition. A novel optimization approach of linked feature selection for cross-sensor iris identification was put out in the work of [30], The first portion of our model's objective function seeks to reduce misclassification errors, while the second part uses l 2,1-norm regularization to achieve sparsity in linked feature spaces. The suggested feature selection model may be thought of as a half-quadratic optimization problem during the training phase, with  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Article** & **Category** & **Approach** & **Strength** & **Limitations** \\ \hline
[26] & Filter based & Gabor, 2D Haar Wavelet & Good immunity & The size of features is long \\  & & Transform & to illumination & Information loss \\  & & quadrature 2-D Gabor & & \\
[27] & Filter based & wavelet coefficients & Good immunity & Information loss \\  & & with most-significant bits & & \\
[28] & Filter based & wavelet transform (WT) & Good discriminative & Requires feature selection \\  & & zero crossings & performance & \\ \hline \hline \end{tabular}
\end{table}
Table 2: Overview of previous approaches for feature extraction in iris recognition.  
an iterative algorithm being constructed to find the answer. An automated method for iris recognition based on 2-D iris photographs was created in the work of [31], In order to reduce the dimensionality of iris characteristics without losing important information, the 2 DPCA (two-dimensional principal component analysis) and GA (Genetic Algorithm) have been utilized as feature extraction and feature selection approaches. Levenberg-learning Marquardt's rule is used to create the Back Propagation Neural Network (BPNN) for iris recognition. In order to choose the best sub-component set, an iris recognition algorithm was presented in the work of [32], in the context of teaching learning-based optimization. It has been used to combine significant findings from the selection of several components. The Chebyshev distance classifier is used to relocate the optimal subsets. A filter-based approach to multiple feature construction using genetic programming (GP) called multiple feature construction (FCM) that stores the top individuals was proposed in the work of [33], and a filter-based approach to feature selection using GP named FS that employs a correlation-based evaluation method is also used. It is suggested to use a hybrid strategy called FCMFS, which first creates many features using FCM before choosing useful features using FS. The iris code was determined in the work of [24], from an optimization standpoint. Following their proof that the conventional iris code is the result of an optimization problem that reduces the distance between the feature values and the iris codes, they go on to show that more efficient iris codes can be produced by including terms in the objective function of this optimization problem. They also look into two more objective terms. The first objective term takes use of how an iris code's bits are arranged spatially in various places. The second target term reduces the impact of iris codes' less trustworthy bits. The two goal terms may be used separately or jointly to solve the optimization issue.