Computational perspectives on the other-race effect

@article{Otoole2013ComputationalPO,
  title={Computational perspectives on the other-race effect},
  author={Alice J. O’toole and Vaidehi S. Natu},
  journal={Visual Cognition},
  year={2013},
  volume={21},
  pages={1121 - 1137},
  url={https://api.semanticscholar.org/CorpusID:16105417}
}
Computational studies of own- versus other-race face processing show that to produce an other- race effect computationally, biased experience or learning must intervene during the process of feature selection, which implicates the critical importance of “developmental” learning in theother-race effect.
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24 Citations
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54 References

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These findings support a new explanation for the cross-race (CR) recognition deficit based on feature coding differences between CR and SR faces, and appear incompatible with similarity-based models of face categories.

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A computer vision algorithm for Bayesian face recognition is used to study how the acquisition of experience in face-space and the presence of race categories affect performance for own and other-race faces.

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State-of-the-art face recognition algorithms, like humans, struggle with “other-race face” recognition and this work reports results for a Western algorithm made by fusing eight algorithms from Western countries and an East Asian algorithm made with five algorithms from East Asian countries.

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Abstract The hypothesis that subjects would show superior memory for faces of members of their own ethnic group compared with those of a different ethnic group was tested in a cross-cultural…
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