Technological disruption in object understanding can become an
effective instrument to address urgent computer vision tasks. One
of such paramount tasks is optimization of automated facial
recognition algorithms. Recent approaches utilizing head pose
and gaze estimation add up much to this subject matter.
As of today, there are two viable appearance based systems, CLM
and GAVAM, that can enhance HCI by pose localization and eye
tracking methods. Unlike conventional ways when IR sources are
used, which can jeopardize human eye health, novel systems
instead employ RGB-D sensors for robust 3D mapping and object
recognition. In this case no additional hardware is required, since
the given models employ automatic image processing that
involves as much iterations as required to distinguish between
faces and non-facial objects, to locate and track face images.
The very identification procedure can be divided into two
commensurable phases, namely head pose and gaze estimation,
the latter is often being called FOA analysis. Put simply, head
pose information makes it easier to find eye centers to get a
holistic picture of a scene. Once being located these centers are
subjected to further geometric analysis and 3D reconstruction to
detect gaze direction and coordinates.
The presented model can boast of quick and precise deliverables
in terms of automated facial recognition and be of wide use in
various applications. Thus, it can greatly streamline HCI while
defining medical diagnoses, analyzing customer behavior or
preventing crime and fraud issues and much, much more.