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The Five Biggest Challenges To Facial Recognition


1) Age: Years take a toll on the face. The more time that has passed between two photos of the same subject, the more likely the jawline will have changed or the nose bloomed. Any number of other features can also lose their tell-tale similarities with age. 

2) Pose: Most matching algorithms compare the distance between various features—the space separating the eyes, for example. But a subject turned away from the camera can appear to have wildly different relative measurements.

3) Illumination: Dim lighting, heavy shadows, or even excessive brightness can have the same adverse effect, robbing algorithms of the visual detail needed to spot and compare multiple features.

4) Expression: Whether it’s an open-mouthed yell, a grin, or a pressed-lip menace, if a subject’s expression doesn’t match the one in a reference shot, key landmarks (such as mouth size and position) may not line up.

5) Resolution: Most facial-recognition algorithms are only as good as the number of pixels in a photograph. That can be a function of everything from camera quality to the subject’s distance from the lens (which dictates how much zooming is needed to isolate the face).


(Popular Science)


About danbowen

Educational technology learning and teaching consultant, support, training, change management, innovation and all things ICT and educational, father of two, guitarist, welsh rugby follower,

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