Facial recognition software does much the same thing, relying on the geometry of the face to identify an individual. Different technologies excel under different conditions; while taking a picture of the subject using a conventional (video) camera may work acceptably under ideal circumstances, 3D imaging extrapolates those key characteristics to allow for improved identification from an angle. Surface texture analysis takes the key characteristics approach and applies it to the skin, enhancing the confidence of a match achieved via normal or 3D imaging. Like voice-recognition systems, many facial recognition systems also require training with photos taken under different conditions before they can reliably identify an individual. (See this RAND briefing for a more in-depth discussion of biometric identification as well as facial recognition.)
One long-term possibility for this technology is “autotagging”, automatic assignment of names to photos as they are taken. For those taking lots of photos or uploading into photo management software, such a feature could turn out to be very useful. A technical hurdle to be overcome is the processing power required for face recognition, but a limited reference set and continual increases in computing power may help to make this a reality sooner rather than later. So could a recent face recognition development that allows for identification of faces with minimal training.