Spatial-frequency cutoff requirements for pattern recognition in central and peripheral vision

Mi Young Kwon, Gordon E. Legge

Research output: Contribution to journalArticlepeer-review

19 Scopus citations


It is well known that object recognition requires spatial frequencies exceeding some critical cutoff value. People with central scotomas who rely on peripheral vision have substantial difficulty with reading and face recognition. Deficiencies of pattern recognition in peripheral vision, might result in higher cutoff requirements, and may contribute to the functional problems of people with central-field loss. Here we asked about differences in spatial-cutoff requirements in central and peripheral vision for letter and face recognition. The stimuli were the 26 letters of the English alphabet and 26 celebrity faces. Each image was blurred using a low-pass filter in the spatial frequency domain. Critical cutoffs (defined as the minimum low-pass filter cutoff yielding 80% accuracy) were obtained by measuring recognition accuracy as a function of cutoff frequency (in cycles per object). Our data showed that critical cutoffs increased from central to peripheral vision by 20% for letter recognition and by 50% for face recognition. We asked whether these differences could be accounted for by central/peripheral differences in the contrast sensitivity function (CSF). We addressed this question by implementing an ideal-observer model which incorporates empirical CSF measurements and tested the model on letter and face recognition. The success of the model indicates that central/peripheral differences in the cutoff requirements for letter and face recognition can be accounted for by the information content of the stimulus limited by the shape of the human CSF, combined with a source of internal noise and followed by an optimal decision rule.

Original languageEnglish (US)
Pages (from-to)1995-2007
Number of pages13
JournalVision Research
Issue number18
StatePublished - Sep 15 2011

Bibliographical note

Funding Information:
We thank Susana Chung and Bosco Tjan for sharing their CSF data with us. We thank Meredith Sherritt and Jennifer Mamrosh for helping with screening face images. We also thank Rachel Gage for help with data collection. This work was supported by NIH Grant R01 EY002934.


  • Faces
  • Ideal observer
  • Letters
  • Pattern recognition
  • Peripheral vision
  • Spatial-frequency bandwidth

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