Face recognition relies on the ability to detect traits in the face that uniquely set individuals apart from one another. Lighting, camera back light control, skin tone, texture all play a role in any particular algorithm’s ability to differentiate faces. When unique features are hidden by shadows and occlusions, face algorithms have fewer data points from which to make their decisions. Occlusions can include image distortions (reflectance on materials such as windows, windshields or even light on the camera’s lens), to environmental conditions (strong directional lighting causing dark cast shadows, lack of lighting at night time or in poorly light indoor locations).
All of these situations result in some uniquely identifying traits in the face from being captured and used in the biometric identification process.
Matching faces located in vehicles presents unique challenges including motion blur, in-cabin lighting challenges, and reflectance/haze on windows and other artifacts related to UV filtering present vehicle glass. For motorcycles, protective helmets provide visibility challenges.
Proper camera selection, placement, environmental control measures, and selection of a face recognition algorithm developed to deal with the challenges of matching faces in the wild all contribute to improving face recognition accuracy.
RealNetworks and the SAFR team is ready to work with customers to address wide ranging challenges and assure optimal performance of the SAFR face recognition platform for our valued customers.