People recognize faces of their own race more accurately than faces of other races. The "contact" hypothesis suggests that this "other-race effect" occurs as a result of the greater experience we have with own-versus other-race faces. The computational mechanisms that may underlie different versions of the contact hypothesis were explored in this study. We replicated the other-race effect with human participants and evaluated four classes of computational face recognition algorithms for the presence of an other-race effect. Consistent with the predictions of a developmental contact hypothesis, "experience-based models" demonstrated an other-race effect only when the representational system was developed through experience that warped the perceptual space in a way that was sensitive to the overall structure of the model's experience with faces of different races. When the model's representation relied on a feature set optimized to encode the information in the learned faces, experience-based algorithms recognized minority-race faces more accurately than majority-race faces. The results suggest a developmental learning process that warps the perceptual space to enhance the encoding of distinctions relevant for own-race faces. This feature space limits the quality of face representations for other-race faces.