4D Analysis of Facial Ageing Using Dynamic Features

Validation of low-cost mobile phone applications and comparison with professional imaging systems for three-dimensional facial imaging: A pilot study

PURPOSE

The objective of this study was to investigate the accuracies of three-dimensional (3D) facial scanning mobile phone applications as compared to professional 3D facial imaging systems.

MATERIALS AND METHODS

A manikin head model was used as the subject for comparing six 3D facial imaging systems which comprised three professional 3D scanners (3dMDface, Artec Eva and Vectra H2) and three mobile phone applications (Bellus3D, ScandyPro and Hedges). For each system, five scans were taken to analyse (1) linear accuracy using 9 measurements (2) global and (3) regional 3D accuracy of the scanned surface by root mean square (RMS) and colour map analysis. Another set of five scans was repeated by a second operator to evaluate the inter-operator reproducibility for each system.

RESULTS

All the facial imaging systems had absolute errors lesser than 1.0 mm for the linear measurements. The technical error of measurement (TEM) for inter-examiner and intra-examiner linear measurements were within acceptable limits. Artec Eva, Vectra H2 and Scandy Pro had poor global 3D trueness (RMS > 1.0 mm) but good 3D regional trueness (RMS < 1.0 mm). 3dMDface, Bellus3D Face App and Heges had good global and regional 3D trueness. All the facial imaging systems had good global and regional 3D precision and reproducibility (RMS < 1.0 mm).

CONCLUSION

This study demonstrated that mobile phone 3D scanning applications had comparable trueness, precision and reproducibility to professional systems. Colour map analysis supplemented the use of the RMS value to demonstrate facial regions of significant deviation. Clinicians should also consider the specific area or region of inaccuracies for each system to determine whether the chosen system is appropriate for the clinical condition or procedure.

CLINICAL SIGNIFICANCE

Mobile phone 3D facial imaging applications may be as accurate as 3D professional facial scanning systems for craniomaxillofacial purposes. However, the choice of the system may vary depending on the specific area of interest.

An exploration of adolescent facial shape changes with age via multilevel partial least squares regression

BACKGROUND AND OBJECTIVES

Multilevel statistical models represent the existence of hierarchies or clustering within populations of subjects (or shapes in this work). This is a distinct advantage over single-level methods that do not. Multilevel partial-least squares regression (mPLSR) is used here to study facial shape changes with age during adolescence in Welsh and Finnish samples comprising males and females.

METHODS

3D facial images were obtained for Welsh and Finnish male and female subjects at multiple ages from 12 to 17 years old. 1000 3D points were defined regularly for each shape by using "meshmonk" software. A three-level model was used here, including level 1 (sex/ethnicity); level 2, all "subject" variations excluding sex, ethnicity, and age; and level 3, age. The mathematical formalism of mPLSR is given in an Appendix.

RESULTS

Differences in facial shape between the ages of 12 and 17 predicted by mPLSR agree well with previous results of multilevel principal components analysis (mPCA); buccal fat is reduced with increasing age and features such as the nose, brow, and chin become larger and more distinct. Differences due to ethnicity and sex are also observed. Plausible simulated faces are predicted from the model for different ages, sexes and ethnicities. Our models provide good representations of the shape data by consideration of appropriate measures of model fit (RMSE and R2).

CONCLUSIONS

Repeat measures in our dataset for the same subject at different ages can only be modelled indirectly at the lowest level of the model at discrete ages via mPCA. By contrast, mPLSR models age explicitly as a continuous covariate, which is a strong advantage of mPLSR over mPCA. These investigations demonstrate that multivariate multilevel methods such as mPLSR can be used to describe such age-related changes for dense 3D point data. mPLSR might be of much use in future for the prediction of facial shapes for missing persons at specific ages or for simulating shapes for syndromes that affect facial shape in new subject populations.

Multilevel principal components analysis of three-dimensional facial growth in adolescents

BACKGROUND AND OBJECTIVES

The study of age-related facial shape changes across different populations and sexes requires new multivariate tools to disentangle different sources of variations present in 3D facial images. Here we wish to use a multivariate technique called multilevel principal components analysis (mPCA) to study three-dimensional facial growth in adolescents.

METHODS

These facial shapes were captured for Welsh and Finnish subjects (both male and female) at multiple ages from 12 to 17 years old (i.e., repeated-measures data). 1000 "dense" 3D points were defined regularly for each shape by using a deformable template via "meshmonk" software. A three-level model was used here, namely: level 1 (sex/ethnicity); level 2, all "subject" variations excluding sex, ethnicity, and age; and level 3, age. The technicalities underpinning the mPCA method are presented in Appendices.

RESULTS

Eigenvalues via mPCA predicted that: level 1 (ethnicity/sex) contained 7.9% of variation; level 2 contained 71.5%; and level 3 (age) contained 20.6%. The results for the eigenvalues via mPCA followed a similar pattern to those results of single-level PCA. Results for modes of variation made sense, where effects due to ethnicity, sex, and age were reflected in modes at appropriate levels of the model. Standardised scores at level 1 via mPCA showed much stronger differentiation between sex and ethnicity groups than results of single-level PCA. Results for standardised scores from both single-level PCA and mPCA at level 3 indicated that females had different average "trajectories" with respect to these scores than males, which suggests that facial shape matures in different ways for males and females. No strong evidence of differences in growth patterns between Finnish and Welsh subjects was observed.

CONCLUSIONS

mPCA results agree with existing research relating to the general process of facial changes in adolescents with respect to age quoted in the literature. They support previous evidence that suggests that males demonstrate larger changes and for a longer period of time compared to females, especially in the lower third of the face. These calculations are therefore an excellent initial test that multivariate multilevel methods such as mPCA can be used to describe such age-related changes for "dense" 3D point data.

What's in a Smile? Initial Analyses of Dynamic Changes in Facial Shape and Appearance

Single-level principal component analysis (PCA) and multi-level PCA (mPCA) methods are applied here to a set of (2D frontal) facial images from a group of 80 Finnish subjects (34 male; 46 female) with two different facial expressions (smiling and neutral) per subject. Inspection of eigenvalues gives insight into the importance of different factors affecting shapes, including: biological sex, facial expression (neutral versus smiling), and all other variations. Biological sex and facial expression are shown to be reflected in those components at appropriate levels of the mPCA model. Dynamic 3D shape data for all phases of a smile made up a second dataset sampled from 60 adult British subjects (31 male; 29 female). Modes of variation reflected the act of smiling at the correct level of the mPCA model. Seven phases of the dynamic smiles are identified: rest pre-smile, onset 1 (acceleration), onset 2 (deceleration), apex, offset 1 (acceleration), offset 2 (deceleration), and rest post-smile. A clear cycle is observed in standardized scores at an appropriate level for mPCA and in single-level PCA. mPCA can be used to study static shapes and images, as well as dynamic changes in shape. It gave us much insight into the question “what’s in a smile?”.