Biomedical Applications Based on Natural and Artificial Computing, p.223-231
Details
- Autor(en) / Beteiligte
- Titel
- Deconvolutional Neural Network for Pupil Detection in Real-World Environments
- Ist Teil von
- Biomedical Applications Based on Natural and Artificial Computing, p.223-231
- Ort / Verlag
- Cham: Springer International Publishing
- Link zum Volltext
- Quelle
- Alma/SFX Local Collection
- Beschreibungen/Notizen
- Eyelid identification provides key data that can be used in several application such as controlling gaze-based HMIs (human machine interfaces), the design of new diagnostic tools for brain diseases, improving driver safety, drowsiness detection, research on advertisement, etc. We propose a novel eyetracking algorithm by learning a deep deconvolutional neural network. To train and test our method, we use several data sets with hand-labeled eye images from real-world tasks. Our method outperforms previous eye tracking methods, improving the results of the current state of the art in a 19%.
- Sprache
- Englisch
- Identifikatoren
- ISBN: 9783319597720, 3319597728ISSN: 0302-9743eISSN: 1611-3349DOI: 10.1007/978-3-319-59773-7_23Titel-ID: cdi_springer_books_10_1007_978_3_319_59773_7_23
- Format
- –
- Schlagworte
- Convolutional Neural Network, Fine Network, Human Machine Interface, Pixel Error, Pupil Center