Date of Graduation
Statler College of Engineering and Mineral Resources
Lane Department of Computer Science and Electrical Engineering
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.
Poster III, Domenick, "Digital Eye Modification A Countermeasure to Automated Face Recognition" (2015). Graduate Theses, Dissertations, and Problem Reports. 6438.