Eyelid’s Intrinsic Motion-Aware Feature Learning for Real-Time Eyeblink Detection in the Wild

This paper presents a new approach to measuring eyelid movement using millimeter wave (mmW) radar technology. A two-step method is proposed, involving the observation of a small resolution cell corresponding to the monitored eye and the evaluation of the phase evolution over the measurement period. Simulations are conducted to support radar system optimization and data interpretation with a focus on detecting eyelid movement patterns and compensating for interference from other parts of the body. The feasibility of using this method with eyeglasses is also explored. The proposed technique’s advantages and limitations are discussed in comparison with existing measurement alternatives. The characteristics of eyelid dynamics, including blink frequency, regularity, duration, and velocity can be used to assess neurological conditions and driver drowsiness.

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Eyelid’s Intrinsic Motion-Aware Feature Learning for Real-Time Eyeblink Detection in the Wild

Cited by 3 publications

References 37 publications

A multi-aware graph convolutional network for driver drowsiness detection

A multi-aware graph convolutional network for driver drowsiness detection

mEBAL2 database and benchmark: Image-based multispectral eyeblink detection

Eyelid Dynamics Characterization with 120 GHz mmW Radar

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