A cognitive template for human face detection

Faces are highly informative social stimuli, yet before any information can be accessed, the face must first be detected in the visual field. A detection template that serves this purpose must be able to accommodate the wide variety of face images we encounter, but how this generality could be achieved remains unknown. In this study, we investigate whether statistical averages of previously encountered faces can form the basis of a general face detection template. We provide converging evidence from a range of methods—human similarity judgements and PCA-based image analysis of face averages (Experiment 1–3), human detection behaviour for faces embedded in complex scenes (Experiment 4 and 5), and simulations with a template-matching algorithm (Experiment 6 and 7)—to examine the formation, stability and robustness of statistical image averages as cognitive templates for human face detection. We integrate these findings with existing knowledge of face identification, ensemble coding, and the development of face perception.