Journal of Computer Security and Usability

February 21, 2016

Face Recognition on Mobile Devices

As originally published in 2015 IEEE International Conference on Cyber Security and Cloud Computing, co-authors Dr. Xiaojiang Du, Dr. Haibin Ling, and Michael J. Mayhew.

One of the most logical applications of face recognition for authentication is on mobile handset devices. However, face recognition still faces challenges in providing environment tolerance: being able to compensate for changes in light conditions within an environment where authentication is occurring, due to users carrying their mobile handset devices to different locations with varying and unpredictable sources of illumination.

Existing face recognition systems operate by finding fiduciary points relative to the area of the entire face, which becomes their weakness when they are not used in applications where light conditions are fixed and controlled.

The topic of this research investigates Local Binary Patters, an image encoding technique whose origins lie in texture analysis, in order to overcome the problems faced by existing face recognition systems and provide tolerance to variable light conditions. This research aims to utilize LBP on modern mobile handset devices that are "off the shelf": utilizing only the most basic and widely available onboard imaging hardware and processing capability provided by mobile handset devices of the present era.

This research initiative was sponsored by the Air Force Research Laboratory, the United States Department of Defense, Temple University, and the Griffis Institute of New York.

Read the full research here.

Bibliographic Information (IEEE Xplore)