A blink detection algorithm on eye images tailored towards head-mounted eye-trackers. Blinks are an indicator of fatigue and drowsiness. They can assist in the diagnose of mental disorders, such as schizophrenia. Additionally, a blink that obstructs the pupil impairs the performance of eye-tracking algorithms, such as pupil detection. This often results in noise to the gaze estimation. The algorithm presented here is tailored towards head-mounted eye trackers and is robust to calibration-based variations like translation or rotation of the eye. The proposed approach reached 96,35% accuracy for a realistic and challenging data set and in real-time even on low-end devices, rendering the proposed method suited for pervasive eye tracking.