  Research on human face processing using eye movements has provided evidence
that we recognize face images successfully focusing our visual attention on a
few inner facial regions, mainly on the eyes, nose and mouth. To understand how
we accomplish this process of coding high-dimensional faces so efficiently,
this paper proposes and implements a multivariate extraction method that
combines face images variance with human spatial attention maps modeled as
feature- and pattern-based information sources. It is based on a unified
multidimensional representation of the well-known face-space concept. The
spatial attention maps are summary statistics of the eye-tracking fixations of
a number of participants and trials to frontal and well-framed face images
during separate gender and facial expression recognition tasks. Our
experimental results carried out on publicly available face databases have
indicated that we might emulate the human extraction system as a pattern-based
computational method rather than a feature-based one to properly explain the
proficiency of the human system in recognizing visual face information.
