# A Robust Framework for Real-Time Iris Landmarks Detection Using Deep Learning
## 1 Introduction  
Iris landmark localization is a significant element applied in various fields, including security, health, augmented reality, computer vision, and many more [1]. The main focus and the ultimate goal of iris landmark localization is to localize the iris key landmark points to extract irises with higher accuracy [2]. The iris knowledge gained from iris landmark locations can be handy in many recognition systems such as human-computer interaction, identification, and security programs [3]. Iris detection plays an essential role in different fields such as automatic biometric systems, custom clearance, and identity matching by police and can be very effective in identifying a citizen. Iris detection and tracking-based applications are more stable compared to fingerprints as fingerprints disappear with time or due to some diseases. There is no need to contact the machine, and the machine can easily take images of a walking person. Most importantly, environmental and bodily changes do not affect the iris [4]. However, iris detection is a time-consuming and computationallyexpensive problem [5]. Human-computer interaction, remote tracking, enjoyment, security surveillance, and medical applications can be facilitated by facial information from facial landmark areas [6].  
Iris landmark detection and tracking are still open areas of research. With iris landmark detection and iris tracking, a mobile device can be used, which is beneficial for disabled individuals. We provide a manually annotated iris landmarks dataset that can help researchers to localize and track the iris. We also provide a comparison between deep learning techniques to find a technique that performs better. The previous studies have worked on techniques that do not need any datasets because they use some image segmentation techniques to detect the iris, and these techniques do not require a dataset. The previous studies have used the old methods for iris detection, such as the Hough transform and contour detection, to detect the iris; however, these methods also need very high-quality images to detect 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 used in real-time scenarios.  
This article proposes a robust framework for iris key landmark localization to extract the iris efficiently. It is a viable approach for aiding handicapped people to use a mouse using their iris. A dual-shot detector is used for face detection, and the ShuffleNet.v2 model is used to identify facial landmarks [7; 8; 9]. This research demonstrated the effectiveness of various Convolutional Neural Network (CNN) models on performance parameters selected to evaluate the proposed model. The main contributions of this research are as follows:  
1. We collected a novel annotated dataset of iris landmarks and made it publicly available;
2. We proposed a deep learning-based model for the robust and real-time identification of iris landmarks.  
The rest of the article is organized as follows. Section 2 discusses the related state-of-the-art research for iris landmark localization and applications, while Section 3 provides a detailed description of the acquisition of our novel dataset of iris landmarks and proposed methods. Section 4 is dedicated to the results and discussion, while conclusive remarks are provided in Section 5.