  Estimating the actual head orientation from 2D images, with regard to its
three degrees of freedom, is a well known problem that is highly significant
for a large number of applications involving head pose knowledge. Consequently,
this topic has been tackled by a plethora of methods and algorithms the most
part of which exploits neural networks. Machine learning methods, indeed,
achieve accurate head rotation values yet require an adequate training stage
and, to that aim, a relevant number of positive and negative examples. In this
paper we take a different approach to this topic by using fractal coding theory
and particularly Partitioned Iterated Function Systems to extract the fractal
code from the input head image and to compare this representation to the
fractal code of a reference model through Hamming distance. According to
experiments conducted on both the BIWI and the AFLW2000 databases, the proposed
PIFS based head pose estimation method provides accurate yaw/pitch/roll angular
values, with a performance approaching that of state of the art of
machine-learning based algorithms and exceeding most of non-training based
approaches.
