# A Robust Framework for Real-Time Iris Landmarks Detection Using Deep Learning
## 2 Related Work  
Several applications such as authorization, identification, and surveillance are based on iris recognition systems. Near-infrared (NIR) [10] sensors are used for iris identification and extracting iris patterns containing enough textural information regardless of the color of the eyes. A SoftMax classifier and CNN-based approach is presented in [11] to extract the discriminative features from the input images to locate the iris. The purpose of iris recognition is to develop a variety of user authentication and security applications. A high-quality camera was used to take images that needed to be very close to the eyes, and these were used for iris localization. Iris recognition is one of the best and most widely used biometric authentication methods and is more secure than the other methods. These have been used in large-scale applications. However, it is necessary to identify the originality of the iris to improve security. In some industries, contact lenses are used, which reduces the accuracy of the iris recognition system. Several studies on iris tracking and iris detection exist in the literature. This section presents details of the state-of-the-art techniques, methods, datasets, applications, and performance of the prior studies. The authors in [12] used a dataset containing 66 pupil videos with exceptional quality that allowed pupil detection and the evaluation of existing methods. According to the results, the detection rate of half of all the cases is insufficient, and the dataset needs to be enhanced to support the best solution for pupil detection. An eye-tracking portable device with a high-quality camera was used to capture the pupil videos in different environmental situations. The pupil detection performance in outdoor and indoor scenes varies due to other environmental conditions. Computer vision gas faced many problems earlier, and image segmentation was one of them, playing a critical role in several real-world scenarios. The eye segmentation dataset presented in [13] is based on low-resolution images. Some landmark models, i.e., Active Appearance Model (AAM), Ensemble Regression Tree (ERT), and deep semantic segmentation models such as Atrous Convolutional Neural Network (ACNN), are implemented to evaluate the performance of the dataset. ACNN performs well in terms of IOU-based ROC curves. We cannot negate the significance of iris segmentation for detection and recognition. More precise segmentation supports more accurate iris recognition. The authors in [14] proposed a unique model that implements U-NET with some fully enlarged or dilated convolutions, named the improved U-NET model. CASIA-iris-interval-v4.0, ND-IRIS-0405, and UBIRIS.v2 were used to evaluate the performance, and the f1-scores of the three datasets stated above were 97.36%, 96.74%, and 94.81%, respectively. [PERSON] et al. [15] proposed a technique for iris detection centered on the watershed, and it primarily works with mobile devices. The watershed helps to estimate the boundary of the iris more precisely. The watershed is an image processing technique used to enhance the edges in the images, and in this work, this technique was used for iris boundary detection. After that, the circle fitting was applied to the iris boundary to find the border of the pupil. This method was analyzed on 15,000 eye images of 75 individuals. Gaze estimation plays a vital role in the era of interactive systems. A piece of helpful information perceived from the pupil, iris, and corners of the eyes is enough to estimate the gaze directions. Iris localization is an initial and essential setup in gaze tracking. [PERSON] et al. [16] proposed a method that uses cascade classifiers for eye and face detection, Haar features for the pupil, sclera, eye corners detection, and the Hough gradient for iris localization. According to the different existing studies, the performance of cascade classifiers is not suitable for face and eye detection.