The correct identification of the eyelids and its aperture provide essential data to infer a subject's mental state (e.g., vigilance, fatigue, and drowsiness) and to validate or reduce the search space of other eye features (e.g., pupil, and iris). This knowledge can be used not only to improve many applications, such as eye tracking and iris recognition, but also to derive information about the user (such as, the take-over readiness of the driver in the automated driving context). In this paper, we propose a computervision-based approach to eyelids identification and aperture estimation. Evaluation was performed on an existing data set from the literature as well as on a new data set introduced in this work. The new data set contains 4000 hand-labeled eye images from 11 subjects driving in a city, these contain several challenges such as reflections, makeup, wrinkles, blinks, and changing illumination. The proposed method outperformed state-of-the-art methods by up to 16.11 percentage points in terms of average similarity to the hand-labeled eyelid outline (from 34px to 12px) and 21.7 pixels (or 7.53% of the eye image height) in terms of average eyelid aperture estimation error. The proposed method implementation runs in real time even on a single core (7ms) and is available, together with the new data set, at http://www.ti.uni-tuebingen.de/Eyelid-detection.2007.0.html.