A key goal of computer vision researchers is to create automated face recognition systems that can equal, and eventually surpass, human performance. To this end, it is imperative that computational researchers know of the key findings from experimental studies of face recognition by humans. These findings provide insights into the nature of cues that the human visual system relies upon for achieving its impressive performance and serve as the building blocks for efforts to artificially emulate these abilities. In this paper, we present what we believe are 19 basic results, with implications for the design of computational systems. Each result is described briefly and appropriate pointers are provided to permit an in-depth study of any particular result.

Recognition as a function of available spatial resolution

Result 1: Humans can recognize familiar faces in very low-resolution images.

Result 2: The ability to tolerate degradations increases with familiarity.

Result 3: High-frequency information by itself is insufficient for good face recognition performance.

The nature of processing: Piecemeal versus holistic

Result 4: Facial features are processed holistically.

Result 5: Of the different facial features, eyebrows are among the most important for recognition.

Result 6: The important configural relationships appear to be independent across the width and height dimensions. The nature of cues used:

Pigmentation, shape and motion

Result 7: Face-shape appears to be encoded in a slightly caricatured manner.

Result 8: Prolonged face viewing can lead to highlevel aftereffects, which suggest prototype-based encoding.

Result 9: Pigmentation cues are at least as important as shape cues.

Result 10: Color cues play a significant role, especially when shape cues are degraded.

Result 11: Contrast polarity inversion dramatically impairs recognition performance, possibly due to compromised ability to use pigmentation cues.

Result 12: Illumination changes influence generalization.

Result 13: View-generalization appears to be mediated by temporal association.

Result 14: Motion of faces appears to facilitate subsequent recognition.

Developmental progression

Result 15: The visual system starts with a rudimentary preference for face-like patterns.

Result 16: The visual system progresses from a piecemeal to a holistic strategy over the first several years of life.

Neural underpinnings

Result 17: The human visual system appears to devote specialized neural resources for face perception.

Result 18: Latency of responses to faces in inferotemporal (IT) cortex is about 120 ms, suggesting a largely feedforward computation.

Result 19: Facial identity and expression might be processed by separate systems

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