Eye Gaze Detection Based on Computational Visual Perception and Facial Landmarks

The pandemic situation in 2020 brought about a‘digitized new normal’and created various issues within the current education systems.One of the issues is the monitoring of students during online examination situations.A system to determine the student’s eye gazes during an examination can help to eradicate malpractices.In this work,we track the users’eye gazes by incorporating twelve facial landmarks around both eyes in conjunction with computer vision and the HAAR classifier.We aim to implement eye gaze detection by considering facial landmarks with two different Convolutional Neural Network(CNN)models,namely the AlexNet model and the VGG16 model.The proposed system outperforms the traditional eye gaze detection system which only uses computer vision and the HAAR classifier in several evaluation metric scores.The proposed system is accurate without the need for complex hardware.Therefore,it can be implemented in educational institutes for the fair conduct of examinations,as well as in other instances where eye gaze detection is required.

Eye and Iris Detection Using Projection and Radial Symmetry Transform

This paper presents an eye and iris detection algorithm for human facial images. The authors combine three features of the eye to develop the algorithm：1） the pixels surrounding the eyes are more variable than other parts of the face; 2） eye pixels are darker than their neighbors; 3） eyes often exhibit radial symmetric properties. Through the first feature,two rough regions of both eyes are detected on the face. Eye masks are then formed based on the second feature,and a fast radial symmetry transform is applied to the two rough regions of both eyes. Finally,accurate iris centers are located by searching the maximum value of the radial symmetry transform results. Using 450 human facial images from the Caltech face database,experiments show that the success rate of the proposed method is 91.7%. The effectiveness of the method was also verified through detection of video frames.

AI-Based Advanced Approaches and Dry Eye Disease Detection Based on Multi-Source Evidence:Cases,Applications,Issues,and Future Directions

This study explores the potential of Artificial Intelligence(AI)in early screening and prognosis of Dry Eye Disease(DED),aiming to enhance the accuracy of therapeutic approaches for eye-care practitioners.Despite the promising opportunities,challenges such as diverse diagnostic evidence,complex etiology,and interdisciplinary knowledge integration impede the interpretability,reliability,and applicability of AI-based DED detection methods.The research conducts a comprehensive review of datasets,diagnostic evidence,and standards,as well as advanced algorithms in AI-based DED detection over the past five years.The DED diagnostic methods are categorized into three groups based on their relationship with AI techniques:(1)those with ground truth and/or comparable standards,(2)potential AI-based methods with significant advantages,and(3)supplementary methods for AI-based DED detection.The study proposes suggested DED detection standards,the combination of multiple diagnostic evidence,and future research directions to guide further investigations.Ultimately,the research contributes to the advancement of ophthalmic disease detection by providing insights into knowledge foundations,advanced methods,challenges,and potential future perspectives,emphasizing the significant role of AI in both academic and practical aspects of ophthalmology.