A feature-based model of symmetry detection

Renata Scognamillo, Gillian Rhodes, Concetta Morrone, David Burr

Research output: Contribution to journalArticlepeer-review


Symmetry detection is important for many biological visual systems, including those of mammals, insects and birds. We constructed a symmetry-detection algorithm with two stages: location of the visually salient features of the image, then evaluating the symmetry of these features over a long range, by means of a simple Gaussian filter. The algorithm detects the axis of maximum symmetry for human faces (or any arbitrary image) and calculates the magnitude of the asymmetry. We have evaluated the algorithm on the dataset of Rhodes et al. (1998 Psychonom. Bull. Rev. 5, 659-669) and found that the algorithm is able to discriminate small variations of symmetry created by computer-manipulating the symmetry levels in individual faces, and that the values measured by the algorithm correlate well with human psycho-physical symmetry ratings.

Original languageEnglish
Pages (from-to)1727-1733
Number of pages7
JournalProceedings of the Royal Society B: Biological Sciences
Issue number1525
Publication statusPublished - Aug 22 2003


  • Faces
  • Local energy
  • Symmetry
  • Visual perception

ASJC Scopus subject areas

  • Agricultural and Biological Sciences(all)
  • Agricultural and Biological Sciences (miscellaneous)


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