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Research on human face processing using eye movements has provided evidencethat we recognize face images successfully focusing our visual attention on afew inner facial regions, mainly on the eyes, nose and mouth. To understand howwe accomplish this process of coding high-dimensional faces so efficiently,this paper proposes and implements a multivariate extraction method thatcombines face images variance with human spatial attention maps modeled asfeature- and pattern-based information sources. It is based on a unifiedmultidimensional representation of the well-known face-space concept. Thespatial attention maps are summary statistics of the eye-tracking fixations ofa number of participants and trials to frontal and well-framed face imagesduring separate gender and facial expression recognition tasks. Ourexperimental results carried out on publicly available face databases haveindicated that we might emulate the human extraction system as a pattern-basedcomputational method rather than a feature-based one to properly explain theproficiency of the human system in recognizing visual face information.

Is human face processing a feature- or pattern-based task? Evidence using a unified computational method driven by eye movements