3D Facial similarity: Automatic assessment versus perceptual judgments
We develop algorithms that seek to assess the similarity of 3D faces, such that similar and dissimilar faces may be classified with high correlation relative to human perception of facial similarity. To obtain human facial similarity ratings, we conduct a subjective study, where a set of human subjects rate the similarity of pairs of faces. Such similarity scores are obtained from 12 subjects on 180 3D faces, with a total of 5490 pairs of similarity scores. We then extract Gabor features from automatically detected fiducial points on the range and texture images from the 3D face and demonstrate that these features correlate well with human judgements of similarity. Finally, we demonstrate the application of using such facial similarity ratings for scalable face recognition.