Shape Analysis of 3D Head Scan Data for U.S. Respirator Users

Abstract

and-face anthropometric survey of diverse, civilian respirator users. Of the 3,997 subjects measured using traditional anthropometric techniques, surface scans and 26 three-dimensional (3D) landmark locations were collected for 947 subjects. The objective of this study was to report the size and shape variation of the survey participants using the 3D data. Generalized Procrustes Analysis (GPA) was conducted to standardize configurations of landmarks associated with individuals into a common coordinate system. The superimposed coordinates for each individual were used as commensurate variables that describe individual shape and were analyzed using Principal Component Analysis (PCA) to identify population variation. The first four principal components (PC) account for 49% of the total sample variation. The first PC indicates that overall size is an important component of facial variability. The second PC accounts for long and narrow or short and wide faces. Longer narrow orbits versus shorter wider orbits can be described by PC3, and PC4 represents variation in the degree of ortho/prognathism. Geometric Morphometrics provides a detailed and interpretable assessment of morphological variation that may be useful in assessing respirators and devising new test and certification standards.

A 3D registration methodology to evaluate the goodness of fit at the individual-respiratory mask interface

“…This is an indication that the anthropometric standards by which these masks were manufactured might be insufficient in describing the female face shapes across the population. This issue has been previously addressed in studies analysing the goodness of fit for respiratory protective equipment (Zhuang and Bradtmiller 2005;Zhuang et al 2010). Indeed the current use of limited anthropometric standards e.g.…”

Learning Race from Face: A Survey

“…Among which, L. Farkas was the known pioneer for establishing an index of 42 discriminative anthropometric facial points (ranked by mean and standard deviation) from a consolidation list of 155 cranio-facial anthropometric proportions [65], [66]. More systematic review of inter-ethnic variability in facial dimensions [67] and recent survey results in American [14], [15], [16] and Chinese demographic data [17], [18], [19] also confirmed statistically significant variances in facial anthropometric dimensions between all race groups. The highlights of these variations in inter-ethnic phenotypic differences pave the way of anthropometry based automatic race recognition.…”

“…Korean subjects. illumination (16). JAFEE [169] 216 (10) Subjects watch emotion-inducing photos and Japanese subjects.…”

“…For example, psychologists have studied behavior correlates of race perception such as otherrace-effect (ORE) and attention model (e.g. [1], [7], [11], [12], [13]), which show existence of racially-discriminative facial features such as eye corners or nose tip (further anthropometric survey confirmed those areas help to discriminate racial groups [14], [15], [16], [17], [18], [19]). Neurophysiologists have shown how race perception influences and regulates cognitive processes such as affection [20], [21], [22], [23] and stereotype [24], [25].…”

Layered Pentahedral Mesh Generation for Biomechanical Geometries with Unclosed Surfaces

“…We consider the problem of meshing the volume defined by a surface and a list of control points. A facial surface extracted from a medium size headform is used [28]. De Greef et al [7] showed a database of Caucasian facial soft tissue thickness using a large-scale in-vivo study.…”