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The structure of the human face allows it to signal a wide range of useful information about a person's gender, identity, mood, etc. We show empirically that facial identity information is conveyed largely via mechanisms tuned to horizontal visual structure. Specifically observers perform substantially better at identifying faces that have been filtered to contain just horizontal information compared to any other orientation band. We then show, computationally, that horizontal structures within faces have an unusual tendency to fall into vertically co-aligned clusters compared with images of natural scenes. We call these clusters "bar codes" and propose that they have important computational properties. We propose that it is this property makes faces "special" visual stimuli because they are able to transmit information as reliable spatial sequence: a highly constrained one-dimensional code. We show that such structure affords computational advantages for face detection and decoding, including robustness to normal environmental image degradation, but makes faces vulnerable to certain classes of transformation that change the sequence of bars such as spatial inversion or contrast-polarity reversal.
Dakin et al. (Wed,) studied this question.