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Sahoo, B. and Li, L. (2020) Challenges and Improvements in Facial Landmark Detection for Robust Eye Center Annotation. IEEE Transactions on Image Processing, 29, 7845-7857.

has been cited by the following article:

• TITLE: Accuracy and Response Speed of Eye Center Annotation Using Eye Movement Models: Validating the Effectiveness of Eyesight Detection

  AUTHORS: Xinzhe An, Xiaofan Xu, Zhenwei Ye

  KEYWORDS: Eye Center Annotation, Mediapipe, Dlib, Haar Cascade, RetinaFace, Accuracy, Eyesight

  JOURNAL NAME: Open Journal of Applied Sciences, Vol.16 No.2, February 14, 2026

  ABSTRACT: Eye center annotation is vital for ophthalmic diagnostics and surgery. However, existing algorithms often require specialized equipment and face challenges in real-time performance, particularly under varying lighting. This study evaluates four widely used facial landmarking algorithms—Mediapipe, Dlib, Haar Cascade, and RetinaFace—in the task of eye iris center annotation. The optimal algorithm is employed to validate the effectiveness in optokinetic nystagmus (OKN) detection and eyesight assessment. The results demonstrate that Mediapipe outperforms the other algorithms, offering superior real-time performance, high accuracy, and robust adaptability to different lighting conditions. Additionally, this study validates its potential in eyesight detection.