Date of Graduation

2015

Document Type

Thesis

Degree Type

MS

College

Statler College of Engineering and Mineral Resources

Department

Lane Department of Computer Science and Electrical Engineering

Committee Chair

Roy Nutter

Committee Co-Chair

Katerina Goseva-Popstojanova

Committee Member

Afzel Noore

Abstract

This thesis describes and assesses a series of subtle digital eye modification techniques and their impact on automated face detection and recognition. The techniques involve altering the relative positioning of a person's eyes in a photograph using a variety of horizontal and vertical movements local to the eye regions. Testing with Eigenfaces, Fisherfaces, and Circular Local Binary Pattern face recognition algorithms on a database of 40 subjects and over 4000 modified images shows these subtle geometric changes to the eyes can degrade automated face recognition accuracy by 40% or more. Certain modifications even lower the chance a face is detected at all by about 20%. The combined effect of particular eye modifications resulted in subjects being both detected and recognized less than 20% of time. These results indicate that nearly imperceptible modifications made to one or more key facial features may foil face recognition algorithms.

Download

Share

COinS