Face Recognition with Environment Tolerance on a Mobile Device

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. This research investigates Local Binary Patterns (LBP), 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 device hardware that is "off-the-shelf": utilizing only the most basic and widely available onboard imaging hardware and processing capability provided on mobile handset devices of the present day. We have performed rigorous experimentation with LBP both on large databases of images of human faces, as well as developing mobile handset software that was deployed to real users and tested in a field environment. Our experimentation indicates that LBP is capable of being used to develop face recognition systems that provide environment tolerance, potentially finding practical use as a component of mobile device authentication applications.