# A Robust Framework for Real-Time Iris Landmarks Detection Using Deep Learning
## 2 Related Work
[24] proposed a stacked-hourglass network-based learning method for eye region landmarks localization. EYEDIAP, MPIIGaze, UT Multiview, and Columbia Gaze datasets were used in evaluating the proposed model. Real-time gaze estimation applications can implement these types of models to aid human-computer interactions. Iris recognition systems need efficient algorithms or models that have less computational cost and that can be used in security-sensitive applications in real time. Haar cascade classifiers and CNN-based techniques were developed to recognize head movements to control the cursor, and different Haar features such as the edge-line and four rectangles were used [25]. In [26], the authors proposed a hybrid approach that uses the circular Hough transform, Viola-Jones, and Kanade-Lucas-Tomasi (KLT) algorithms for eyeball tracking. The Hough transform is an image processing approach for detecting circles in distorted images. The Hough transform technique combined with a Canny edge detector was used for pupil detection. Additionally, this study demonstrated the hardware required for the proposed method. They claimed that the proposed model achieved 96% accuracy. In [27], higher efficiency is claimed when using the combination of double threshold, morphological operations, and center of gravity method. It also required a very high-quality image to proceed further with pupil detection in iris images. This technique requires specific hardware such as a camera placed very close to the eyes for image acquisition purposes. The main drawback is that it cannot be used in real-time; if the author tries to apply this in real-time, they need to make an expensive but tiny device containing a very small camera with excellent resolution. Recently, several significant developments have been made in image processing disciplines, mainly in feature identification methods, and researchers are struggling to develop accurate and effective feature detection algorithms. Eye detection has been achieved using a feature-based approach that uses low-quality attributes such as corners, color, form, and texture [28]. Eye movement is an appropriate communication medium between humans and computers, particularly helpful for disabled persons. Template, appearance, and feature-based methods are also eye detection techniques with great accuracy but are computationally expensive. In the 1990s, researchers conducted research in which they adopted a template-based approach for eyes detection. Initially, a standard eye-shaped template is crafted, and it is time-consuming, requires high-quality eye photos, and is restricted to frontal view images [29]. According to the article [30], the head-mounted devices are essentially based on a customized structure that keeps a small camera in front of the user's face. This mini camera helps capture the high-quality and detailed images of the human eye used for accurate pupil detection. However, this study demands a high-quality camera embedded in a particular device, which may be costly. Iris landmarks detection and tracking are still open areas of research. With iris landmarks detection and iris tracking, we can easily use a mobile device with the iris, which will be very beneficial for disabled individuals. The previous studies have been working on techniques that do not need a dataset because they are using some image segmentation techniques to detect the iris, and also these techniques do not require a dataset. As the previous studies have been using the old methods for iris detection, such as the Hough transform and contour detection, to detect the iris, these methods also need very high-quality images to detect the iris. However, MobileNet version 2 is deep learning-based--an updated version--reducing the model size and increasing the response rate compared to the other models when used in real-time scenarios.