Faces are among the most informative stimuli we ever perceive: Even a split-second glimpse of a person's face tells us his identity, sex, mood, age, race, and direction of attention. The specialness of face processing is acknowledged in the artificial vision community, where contests for face-recognition algorithms abound. Neurological evidence strongly implicates a dedicated machinery for face processing in the human brain to explain the double dissociability of face- and object-recognition deficits. Furthermore, recent evidence shows that macaques too have specialized neural machinery for processing faces. Here we propose a unifying hypothesis, deduced from computational, neurological, fMRI, and single-unit experiments: that what makes face processing special is that it is gated by an obligatory detection process. We clarify this idea in concrete algorithmic terms and show how it can explain a variety of phenomena associated with face processing.