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Detection of Mooney faces is robust to image asymmetries produced by illumination
Face detection relies on the visual features that are shared across different faces. An important component of the basic spatial configuration of a face is symmetry around the vertical midline. Although human faces are structurally symmetrical, they can be asymmetrical in an image due to the directi...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
The Association for Research in Vision and Ophthalmology
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10615147/ https://www.ncbi.nlm.nih.gov/pubmed/37883106 http://dx.doi.org/10.1167/jov.23.12.9 |
Sumario: | Face detection relies on the visual features that are shared across different faces. An important component of the basic spatial configuration of a face is symmetry around the vertical midline. Although human faces are structurally symmetrical, they can be asymmetrical in an image due to the direction of lighting or the position of the face. In the experiments presented here, we examined how face detection from simple contrast patterns that occur across the face is affected by the image asymmetries associated with variations in the horizontal lighting direction. We presented observers with two-tone images of faces (Mooney faces) that isolated the unique pattern of contrast in the shading and shadows on a face, illuminated from a wide range of horizontal directions. In two experiments, we found that face detection is surprisingly robust to these lighting changes, with sensitivity in discriminating between face and non-face patterns reduced only at the most extreme lighting directions. This tolerance to changes in the horizontal lighting direction depended partly on the orientation of the face, vertical lighting direction, and contrast polarity. Our results provide insight into how contrast cues produced by shading and shadows occurring across the facial surface are utilized by the visual system to detect human faces. |
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