Facial recognition methodologies, widely used today in everything from automatic
passport controls at airports to unlocking devices on mobile phones, has developed
greatly in recent years. The methodologies vary from feature based landmark
comparisons in 2D and 3D, utilising Principal Component Analysis (PCA) to
surface-based Iterative Closest Point Algorithm (ICP) analysis and a wide variety of
techniques in between. The aim of all facial recognition software (FCS) is to find or
match a target face with a reference face of a known individual from an existing
database. FCS, however, faces many challenges including temporal variations due to
development/ageing and variations in facial expression. To determine any
quantifiable heritability of facial morphology using this resource, one has to look for
faces with enough demonstrable similarities to predict a possible genetic link, instead
of the ordinary matching of the same individual’s face in different instances. With
the exception of identical twins, this means the introduction of many more variables
into the equation of how to relate faces to each other. Variation due to both
developmental and degenerative aging becomes a much greater issue than in
previous matching situations, especially when comparing parents with children.
Additionally, sexual dimorphism is encountered with cross gender relationships, for
example, between mothers and sons. Non-inherited variables are also encountered
such as BMI, facial disfigurement and the effects of dental work and tooth loss.
For this study a Trimmed Iterative Closest Point Algorithm (TrICP) was applied to
three-dimensional surfaces scans, created using a white light scanner and Flexscan
3D, of the faces of 41 families consisting of 139 individuals. The TrICP algorithm
produced 7176 Mesh-to-mesh Values (MMV) for each of seven sections of the face
(Whole face, Eyes, Nose, Mouth, Eyes-Nose, Eyes-Nose-Mouth, and Eyes-Nose-
Mouth-Chin). Receiver Operated Characteristic (ROC) analysis was then conducted
for each of the seven sections of the face within 11 predetermined categories of
relationship, in order to assess the utility of the method for predicting familial
relationships (sensitivity/specificity). Additionally, the MMVs of three single
features, (eyes, nose and mouth) were combined to form four combination areas
which were analysed within the same 11 relationship categories.
Overall the relationship between sisters showed the most similarity across all areas
of the face with the clear exception of the mouth. Where female to female
comparison was conducted the mouth consistently negatively affected the results.
The father-daughter relationship showed the least similarity overall and was only
significant for three of the 11 portions of the face. In general, the combination of
three single features achieved greater accuracy as shown by Areas Under the Curve
(AUC) than all other portions of the face and single features were less predictive
than the face as a whole