Geometry and Gesture-Based Features from Saccadic Eye-Movement as a Biometric in Radiology Conference Paper

abstract

• Springer International Publishing AG 2017. 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.

authors

• Hammond, Tracy

published proceedings

• AUGMENTED COGNITION: NEUROCOGNITION AND MACHINE LEARNING, AC 2017, PT I

author list (cited authors)

• Alamudun, F. T., Hammond, T., Yoon, H., & Tourassi, G. D.

citation count

• 4

complete list of authors

• Alamudun, Folami T||Hammond, Tracy||Yoon, Hong-Jun||Tourassi, Georgia D

publication date

• May 2017

publisher

• Springer Nature  Publisher

published in

• Lecture Notes in Artificial Intelligence  Journal

keywords

• Biometrics
• Eye-tracking
• Mammography
• Sketch Recognition

Digital Object Identifier (DOI)

• 10.1007/978-3-319-58628-1_11

International Standard Book Number (ISBN) 13

• 978-3-319-58627-4

start page

• 123

end page

• 138

volume

• 10284