# A Robust Framework for Real-Time Iris Landmarks Detection Using Deep Learning
## 2 Related Work
Template matching techniques are used to estimate the variation in the frames, which enhances the performance. Biometrics necessitates recognizing a human being based on their behavioral and biological traits. Initially, image segmentation, localization, and feature extraction were performed. Then, a 1D-Gober filter was used to extract distinctive characteristics of the iris, and humming distance was involved in matching the iris images. The evaluation of the proposed model was performed on UBIRIS v1, IIT DELHI DATABASE, and CASIA-IRIS TWINS datasets. The enhancement of the database may improve the performance of the proposed model [17]. A Deep Learning (DL) and Gray-Level Co-Occurrence Matrix (GLCM) based methodology is proposed in [18] for iris liveliness detection and localization. Energy, entropy, contrast, and correlation features were obtained with the help of GLCM. Several deep learning models such as VGG16, VGG19, DenseNet121, DenseNet169, DenseNet201, and ResNet50 were applied to Clarkson LivDet2013, Clarkson LivDet2015, and IIITD datasets to evaluate the performance of the model. Eye movements play an imperative role in eye tracking, and they also contribute to human-computer interaction. A pre-trained convolution neural network-based model was implemented to segment the iris by [PERSON] et al. [19]. The Speed Up Robust Features (SURF) feature was also used to feature vectors. Experiments were conducted on costly hardware such as a high-quality camera, and to illuminate cheek regions, Infrared IREDs were utilized. Iris localization is a significant step in iris identification systems, and its correctness may directly affect iris normalization, feature extraction, and matching steps. The authors in [20] proposed a Coarse-To-Fine (CTF) algorithm-based computational cost-effective method, and also [PERSON]'s integrodifferential operator was used to refine the pupil and iris centers. The proposed technique was evaluated on the CASIA-V3-Interval database of images. Different computer techniques are helpful in producing segmented eye parts; later on, these can be used in iris tracking-based video oculography. A convolutional neural network-based approach was proposed by [PERSON] et al. [21], named EllSeg. The prediction of complete elliptical eye regions with the background was made through the EllSeg framework. The presented model was trained and evaluated with NVGaze, OpenEDS, RITEyes, ElSe, ExCuSe, PupilNet, and LPW datasets with significantly less error margin. Fast iris position tracking is essential when working with real-time applications to assist human-computer interaction. However, in most eye image databases, the irises in the images are affected by different noises. [PERSON] et al. [22] proposed a novel Conformal Geometric Algebra (CGA) based algorithm which is used to track the iris position more precisely. Initially, the grayscale images were converted into three channels images with an adaptive threshold, and a Sobel edge detector was implemented to produce edge points. Finally, an enhanced CGA-based algorithm was used to detect circles for iris boundaries estimation. Properly extracting the iris areas from images is the critical step in the identification of an individual. Therefore, we cannot deny the importance of segmentation in the localization of the iris. The authors in [23] came up with a novel technique that employs rough entropy, a soft-computing approach to segment the iris, and Circle Sector Analysis (CSA) was used to measure the iris error-free position. The proposed technique was compared with the circular Hough transform, which is a state-of-the-art (iris region segmentation) technique, on the CASIA-Iris-V3-Interval, IITD, and MMU1s datasets. According to the authors, the presented work performed well compared to the state-of-the-art methods. However, this method is still computationally expensive to execute in real-time scenarios. The use of cameras in smartphones can provide benefits to disabled persons with gaze estimation. [PERSON] et al.