A Robust Framework for Real-Time Iris Landmarks Detection Using Deep Learning

Abstract

Iris detection and tracking plays a vital role in human–computer interaction and has become an emerging field for researchers in the last two decades. Typical applications such as virtual reality, augmented reality, gaze detection for customer behavior, controlling computers, and handheld embedded devices need accurate and precise detection of iris landmarks. A significant improvement has been made so far in iris detection and tracking. However, iris landmarks detection in real-time with high accuracy is still a challenge and a computationally expensive task. This is also accompanied with the lack of a publicly available dataset of annotated iris landmarks. This article presents a benchmark dataset and a robust framework for the localization of key landmark points to extract the iris with better accuracy. A number of training sessions have been conducted for MobileNetV2, ResNet50, VGG16, and VGG19 over an iris landmarks dataset, and ImageNet weights are used for model initialization. The Mean Absolute Error (MAE), model loss, and model size are measured to evaluate and validate the proposed model. Results analyses show that the proposed model outperforms other methods on selected parameters. The MAEs of MobileNetV2, ResNet50, VGG16, and VGG19 are 0.60, 0.33, 0.35, and 0.34; the average decrease in size is 60%, and the average reduction in response time is 75% compared to the other models. We collected the images of eyes and annotated them with the help of the proposed algorithm. The generated dataset has been made publicly available for research purposes. The contribution of this research is a model with a more diminutive size and the real-time and accurate prediction of iris landmarks, along with the provided dataset of iris landmark annotations.