Automated facial recognition by means of head pose and gaze estimation

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.

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