Abstract
This work presents BlinkLinMulT, a transformer-based framework for eye blink detection. While most existing approaches rely on frame-wise eye state classification, recent advancements in transformer-based sequence models have not been explored in the blink detection literature. Our approach effectively combines low- and high-level feature sequences with linear complexity cross-modal attention mechanisms and addresses challenges such as lighting changes and a wide range of head poses. Our work is the first to leverage the transformer architecture for blink presence detection and eye state recognition while successfully implementing an efficient fusion of input features. In our experiments, we utilized several publicly available benchmark datasets (CEW, ZJU, MRL Eye, RT-BENE, EyeBlink8, Researcher's Night, and TalkingFace) to extensively show the state-of-the-art performance and generalization capability of our trained model. We hope the proposed method can serve as a new baseline for further research.