Abstract
In this study, we present a novel application of sketch gesture recognition on eye-movement for biometric identification and estimating task expertise. The study was performed for the task of mammographic screening with simultaneous viewing of four coordinated breast views as typically done in clinical practice. Eye-tracking data and diagnostic decisions collected for 100 mammographic cases (25 normal, 25 benign, 50 malignant) and 10 readers (three board certified radiologists and seven radiology residents), formed the corpus for this study. Sketch gesture recognition techniques were employed to extract geometric and gesture-based features from saccadic eye-movements. Our results show that saccadic eye-movement, characterized using sketch-based features, result in more accurate models for predicting individual identity and level of expertise than more traditional eye-tracking features.
Original language | English |
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Pages (from-to) | 123-138 |
Number of pages | 16 |
Journal | Lecture Notes in Computer Science |
Volume | 10284 11th International Conference, AC 2017, Held as Part of HCI International 2017, Vancouver, BC, Canada, July 9-14, 2017,... |
DOIs | |
State | Published - 2017 |
Event | 11th International Conference on Augmented Cognition, AC 2017, held as part of the 19th International Conference on Human-Computer Interaction, HCI 2017 - Vancouver, Canada Duration: Jul 9 2017 → Jul 14 2017 |
Funding
F.T. Alamudun—This manuscript has been authored by UT-Battelle, LLC under Contract No. DE-AC05-00OR22725 with the U.S. Department of Energy. The United States Government retains and the publisher, by accepting the article for publication, acknowledges that the United States Government retains a non-exclusive, paid-up, irrevocable, world-wide license to publish or reproduce the published form of this manuscript, or allow others to do so, for United States Government purposes. The Department of Energy will provide public access to these results of federally sponsored research in accordance with the DOE Public Access Plan ( http://energy.gov/downloads/doe-public-access-plan ). This material is based upon work supported by the U.S. Department of Energy and the Office of Science under contract number DE-AC05-00OR22725. The authors also thank Kathleen B. Hudson, MD, and Garnetta Morin-Ducote, MD for contributions during data collection.
Keywords
- Biometrics
- Eye-tracking
- Mammography
- Sketch recognition