Exploiting perception for face analysis: Image abstraction for head pose estimation

Abstract

We present an algorithm to estimate the pose of a human head from a single, low resolution image in real time. It builds on the fundamentals of human perception i.e. abstracting the relevant details from visual cues. Most images contain far too many cues than what are required for estimating human head pose. Thus, we use non-photorealistic rendering to eliminate irrelevant details like expressions from the picture and accentuate facial features critical to estimating head pose. The maximum likelihood pose range is then estimated by training a classifier on scaled down abstracted images. The results are extremely encouraging especially when compared with other recent methods.Moreover the algorithm is robust to illumination, expression, identity and resolution. © 2012 Springer-Verlag.