Face recognition / Reconnaissance du visage

Face recognition uses mainly the following techniques:

La reconnaissance du visage (ou reconnaissance faciale) utilisent principalement les techniques suivantes:

Facial thermogram requires an (expensive) infrared camera to detect the facial heat patterns that are unique to every human being. Technology Recognition Systems worked on that subject in 1996-1999. Now disappeared.
Le thermogramme du visage requiert une caméra infrarouge (chère) pour détecter la répartition de température du visage particulière à chaque être humain. Technology Recognition Systems travailla sur ce thème vers 1996-1999, maintenant disparu.

"Face Recognition in Hyperspectral Images" (Zhihong Pan, Glenn Healey, Manish Prasad, Bruce Tromberg, 2003) is an article describing a variant using several wavelentghs.

In 2002, Infrared Identification (IRID) got a NIST project using Mikron Infrared cameras.

The Stony Brook university system relies on probing the characteristic pattern of muscles beneath the skin of the face.
Guan takes two snaps of a person in quick succession, asking subjects to smile for the camera. He then uses a computer to analyse how the skin around the subject's mouth moves between the two images. The software does this by tracking changes in the position of tiny wrinkles in the skin, each just a fraction of a millimetre wide.
The data is used to produce an image of the face overlaid with tiny arrows that indicate how different areas of skin move during a smile. This movement is controlled by the pattern of muscles under the skin, and is not affected by the presence of make-up or the size of the subject's smile. The system is sensitive enough to produce such a map from muscle twitches even when people are trying to keep their expression unchanged.
Note: tested on 4 subjects (!).

In A Dynamic Approach to Face Recognition paper, a new method for face recognition is proposed, which is based on dynamic instead of static facial features. They track the motion of certain features on the face during a facial expression and obtain a vector field that characterizes the deformation of the face.

In the training process, two high-resolution images of an individual, one with a neutral expression and the other with a facial expression, like a subtle smile, are taken to obtain the deformation field.

This is a Joint Project between Stony Brook University (SUNY) and New York State Strategic Center for Port and Maritime Security, SUNY Maritime.

Il s'agit de détecter les déformations du visage liées à une expression du visage.