Targeting specific facial variation for different identification tasks.

A conceptual framework that allows faces to be studied and compared objectively with biological validity is presented. The framework is a logical extension of modern morphometrics and statistical shape analysis techniques. Three dimensional (3D) facial scans were collected from 255 healthy young adults. One scan depicted a smiling facial expression and another scan depicted a neutral expression. These facial scans were modelled in a Principal Component Analysis (PCA) space where Euclidean (ED) and Mahalanobis (MD) distances were used to form similarity measures. Within this PCA space, property pathways were calculated that expressed the direction of change in facial expression. Decomposition of distances into property-independent (D1) and dependent components (D2) along these pathways enabled the comparison of two faces in terms of the extent of a smiling expression. The performance of all distances was tested and compared in dual types of experiments: Classification tasks and a Recognition task. In the Classification tasks, individual facial scans were assigned to one or more population groups of smiling or neutral scans. The property-dependent (D2) component of both Euclidean and Mahalanobis distances performed best in the Classification task, by correctly assigning 99.8% of scans to the right population group. The recognition task tested if a scan of an individual depicting a smiling/neutral expression could be positively identified when shown a scan of the same person depicting a neutral/smiling expression. ED1 and MD1 performed best, and correctly identified 97.8% and 94.8% of individual scans respectively as belonging to the same person despite differences in facial expression. It was concluded that decomposed components are superior to straightforward distances in achieving positive identifications and presents a novel method for quantifying facial similarity. Additionally, although the undecomposed Mahalanobis distance often used in practice outperformed that of the Euclidean, it was the opposite result for the decomposed distances.