Initial Results of Multilevel Principal Components Analysis of Facial Shape

• Published in: Medical Image Understanding and Analysis Vol. 723; pp. 674 - 685
• Main Authors: Farnell, D. J. J., Galloway, J., Zhurov, A., Richmond, S., Perttiniemi, P., Katic, V.
• Format: Book Chapter
• Language: English
• Published: Switzerland Springer International Publishing AG 2017; Springer International Publishing
• Series: Communications in Computer and Information Science
• Subjects: Active shape models; Facial shape; Multilevel principal components analysis
• ISBN: 9783319609638; 3319609637
• ISSN: 1865-0929; 1865-0937
• DOI: 10.1007/978-3-319-60964-5_59

Traditionally, active shape models (ASMs) do not make a distinction between groups in the subject population and they rely on methods such as (single-level) principal components analysis (PCA). Multilevel principal components analysis (mPCA) allows one to model between-group effects and within-group effects explicitly. Three dimensional (3D) laser scans were taken from 250 subjects (38 Croatian female, 35 Croatian male, 40 English female, 40 English male, 23 Welsh female, 27 Welsh male, 23 Finnish female, and 24 Finnish male) and 21 landmark points were created subsequently for each scan. After Procrustes transformation, eigenvalues from mPCA and from single-level PCA based on these points were examined. mPCA indicated that the first two eigenvalues of largest magnitude related to within-groups components, but that the next eigenvalue of largest magnitude related to between-groups components. Eigenvalues from single-level PCA always had a larger magnitude than either within-group or between-group eigenvectors at equivalent eigenvalue number. An examination of the first mode of variation indicated possible mixing of between-group and within-group effects in single-level PCA. Component scores for mPCA indicated clustering with country and gender for the between-groups components (as expected), but not for the within-group terms (also as expected). Clustering of component scores for single-level PCA was harder to resolve. In conclusion, mPCA is viable method of forming shape models that offers distinct advantages over single-level PCA when groups occur naturally in the subject population.