Ocular motion classification for mobile device presentation attack detection

As a practical pursuit of quantified uniqueness, biometrics explores the parameters that make us who we are and provides the tools we need to secure the integrity of that identity. In our culture of constant connectivity, an increasing reliance on biometrically secured mobile devices is transforming them into a target for bad actors. While no system will ever prevent all forms of intrusion, even state of the art biometric methods remain vulnerable to spoof attacks. As these attacks become more sophisticated, ocular motion based presentation attack detection (PAD) methods provide a potential deterrent. This dissertation presents the methods and evaluation of a novel optokinetic nystagmus (OKN) based PAD system for mobile device applications which leverages phase-locked temporal features of a unique reflexive behavioral response. Background is provided for historical and literary context of eye motion and ocular tracking to provide context to the objectives and accomplishments of this work. An evaluation of the improved methods for sample processing and sequential stability is provided with highlights for the presented improvements to the stability of convolutional facial landmark localization, and automated spatiotemporal feature extraction and classification models. Insights gleaned from this work are provided to elucidate some of the major challenges of mobile ocular motion feature extraction, as well as additional future considerations for the refinement and application of OKN motion signatures as a novel mobile device based PAD method.

Name thesis:degree_name

Ph.D. (Doctor of Philosophy)

Level thesis:degree_level

Doctoral

Discipline thesis:degree_discipline

Electrical and Computer Engineering (UMKC)

Grantor

University of Missouri--Kansas City

Year dc:date.issued

2020

Author dc:creator

• Lowe, Jesse

Advisor dc:contributor.advisor

• Derakhshani, Reza

Handle dc:identifier.uri

https://hdl.handle.net/10355/80792

OAI identifier oai:identifier

oai:mospace.umsystem.edu:10355/80792