Grid-Based Likelihood Ratio Classifiers for the Comparison of Facial Marks

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Facial marks have been studied before, either as a complement to face recognition systems or for their suitability as a single biometric modality. In this paper, we use a subset of the FRGCv2 data set (12307 images and 568 subjects) to study the properties of facial marks, their spatial patterns, and classifiers acting upon these patterns. We observe differences between age and ethnic groups in the number of facial marks. Also, facial marks tend to be clustered. We present six forensically relevant aspects with respect to the design and evaluation of classifiers. These aspects help to systematically study factors that influence performance characteristics (discriminating power and calibration loss) of these classifiers. Calibration loss is of particular forensic importance; it essentially measures how well the classifier output can be used as strength of evidence in a court of law. We use various facial mark grids to which the facial mark spatial patterns are assigned. We find that a classifier that utilizes the facial mark grid of a specific subject outperforms all other classifiers. We also observe that the calibration loss of such subject-based classifier indicates that small grid cell sizes should be avoided.