We present an approach to identify non-cooperative individuals at a distance from a sequence of images using 3D face models.
Most biometric features (such as fingerprints, hand shape, iris or retinal scans) require cooperative subjects in close proximity to the biometric system. Even top 2D face recognition systems today are only reliable at a distance when presented with frontal images. Our approach is accomplished by processing images acquired with an ultra-high resolution video camera, inferring the location of the subjects' head, using this information to crop the region of interest, building a 3D face model, and using this 3D model to perform biometric identification. To build the 3D model, we use an image sequence, as natural head and body motion provides enough viewpoint variation to perform stereo-motion for 3D face reconstruction. The 3D face model allows the use of true shape invariants for recognition against 3D galleries. Also the 3D model can assist in matching non-frontal faces against 2D galleries by rotating and rendering the 3D reconstruction into a 2D frontal image when only a 2D gallery is available. This approach constitutes a considerable step forward in solving the challenging problem of biometric identification at a distance. (Publisher abstract provided)