Date of Award
Spring 2009
Degree Type
Dissertation
Degree Name
Doctor of Philosophy (PhD)
Graduate Group
Computer and Information Science
First Advisor
Jonathan Smith
Abstract
High-resolution digital cameras are becoming ever-larger parts of our daily lives, whether as part of closed-circuit surveillance systems or as part of portable digital devices that many of us carry around with us. Combining the broadening reach of these cameras with automatic face recognition technology creates a sensor network that is ripe for abuse: our every action could be recorded and tagged with our identities, the date, and our location as if we each had an investigator tasked only with keeping each of us under constant surveillance. Adding the continually falling cost of data storage to this mix, and we are left with a situation where the privacy abuses don't need to happen today: the stored imagery can be mined and re-mined forever, while the sophistication of automatic analysis continues to grow.
The MASKS project takes the first steps toward addressing this problem. If we would like to be able to de-identify faces before the images are shared with others, we cannot do so with ad hoc techniques applied identically to all faces. Since each face is unique, the method of disguising that face must be equally unique. In order to hide or reduce those critical identifying characteristics, we are delivering the following foundational contributions toward characterizing the nature of facial information:
- We have created a new pose-controlled, high-resolution database of facial images.
- The most prominent anatomical markers on each face have been marked for position and shape, establishing a new gold standard for facial segmentation.
- A parameterized model of the diversity of our subject population was built based on statistical analysis of the annotations. The model was validated by comparison with the performance of a standard set of artificial disguises.
Recommended Citation
Alexander, James Michael, "Masks: Maintaining Anonymity by Sequestering Key Statistics" (2009). Publicly Accessible Penn Dissertations. 32.
https://repository.upenn.edu/edissertations/32