Feature extraction from faces using deformable templates
International Journal of Computer Vision
IEEE Transactions on Pattern Analysis and Machine Intelligence
Digital Image Processing
Morphological Image Analysis: Principles and Applications
Morphological Image Analysis: Principles and Applications
Detecting eye blink states by tracking iris and eyelids
Pattern Recognition Letters
Extraction and tracking of the eyelids
ICASSP '00 Proceedings of the Acoustics, Speech, and Signal Processing, 2000. on IEEE International Conference - Volume 04
Fast Boundary Detection: A Generalization and a New Algorithm
IEEE Transactions on Computers
A study on eyelid localization considering image focus for iris recognition
Pattern Recognition Letters
Toward Accurate and Fast Iris Segmentation for Iris Biometrics
IEEE Transactions on Pattern Analysis and Machine Intelligence
A highly accurate and computationally efficient approach for unconstrained iris segmentation
Image and Vision Computing
Classification of startle eyeblink metrics using neural networks
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
An Ensemble Method for Classifying Startle Eyeblink Modulation from High-Speed Video Records
IEEE Transactions on Affective Computing
Statistical models of appearance for eye tracking and eye-blink detection and measurement
IEEE Transactions on Consumer Electronics
Computer Methods and Programs in Biomedicine
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Using the positions of the eyelids is an effective and contact-free way for the measurement of startle induced eye-blinks, which plays an important role in human psychophysiological research. To the best of our knowledge, no methods for an efficient detection and tracking of the exact eyelid contours in image sequences captured at high-speed exist that are conveniently usable by psychophysiological researchers. In this publication a semi-automatic model-based eyelid contour detection and tracking algorithm for the analysis of high-speed video recordings from an eye tracker is presented. As a large number of images have been acquired prior to method development it was important that our technique is able to deal with images that are recorded without any special parametrisation of the eye tracker. The method entails pupil detection, specular reflection removal and makes use of dynamic model adaption. In a proof-of-concept study we could achieve a correct detection rate of 90.6%. With this approach, we provide a feasible method to accurately assess eye-blinks from high-speed video recordings.