## 3 Literature Survey
### Iris Classification  
The literature contains numerous articles with different type of classifiers used for iris classification. Three categories of application for iris recognition are found in the literature. The first one is iris recognition. In [34], iris segmentation, quality enhancement, and match score fusion for recognition were proposed. For segmenting the nonideal iris image, curve evolution approach is used based on modified Mumford-Shah functional. Furthermore, the algorithm uses enhancement algorithms applied on the iris image. For classification, support-vector-machine based learning algorithm for classification is used. The second category is for contact lens detection for improving iris recognition. A deep convolutional network with extensive connections between its layers, the Densely Connected Contact Lens Detection Network (DCLNet), has been suggested in [35], Using support vector machines in addition to Densenet121, DCLNet was created through a number of modifications. The work of [36] has concentrated on a contact lens detection for improving iris recognition. Deep Convolutional Neural Network is utilized (CNN). The fifteen-layer CNN architecture ContlensNet is constructed for the three-class detection issue with the following classes: photographs with textured (or colorful), soft (or clear), and no contact lenses. The third category is fake iris detection. In ref. [37], fake iris detection algorithm is proposed based on improved local binary pattern and statistical features. It is combined of several steps. First, a simplified scale invariant feature detection descriptor is used for feature extraction at each pixel of the image. Second, scale invariant frequency transformation (SIFT) descriptor is used to rank the LBP encoding sequence. Next, statistical features are extracted from the weighted LBP map. Lastly, support vector machine is used for classification which has the role of classifying genuine and counterfeit iris images. In ref. [38], the effect of contact lenses on iris recognition has been studied. They have prepared iris contact lens databases: IIIT-Delhi Contact Lens Iris database and ND Contact Lens Detection 2013 database. In their work, they are interested in feature coding for the first category. In ref. [39], deep convolutional neural network is used for extracting features from iris. The approach uses an optimized center loss function to improve the insufficient discrimination caused by the traditional softmax loss function. For classification, cosine similarity is used to estimate the similarity between the features. In ref. [40], they has evaluated convolutional neural networks (CNNs) on iris recognition. It has shown off-the-shelf CNN features for iris recognition. In order to extract features from the normalized iris pictures, five cutting-edge and commercial CNNs (AlexNet, VGG, Google Inception, ResNet, and DenseNet) are employed in this study. Each CNN has a number of levels, as you can see. The classification module is then given the recovered CNN feature vector. The Support Vector Machine (SVM) with many classes is then employed for classification. The iris classification problem is solved in ref. [41], using a multiclassification strategy based on the Hadamard error correction output code and a convolutional neural network. Following the fully connected layer and all convolutional layers for classification, the ReLU activation function is employed.  
Overall, we observe that despite the significant amount of work that has been carried out in the scope of iris recognition. Less amount of work was performed in the role of feature selection for improving the performance of iris recognition in terms of memory and accuracy. Furthermore, the lacking of initial knowledge of the relevant number of features for iris recognition urges us to use variable length nature of algorithm for searching and optimizing the feature selection. Hence, the role of this article is to fill this research gap.