NISTIR 7460

Facial Respirator Shape 

Analysis using 3D 

Anthropometric Data

Riley Fenlon
ii

AugustAugu 

NISTIR 7460

Facial Respirator Shape 

Analysis using 3D 

Anthropometric Data

Riley Fenlon

Information Technology Laboratory 

Information Access Division

August 2007

U.S. Department of Health and Human Services U.S. Department of Commerce 

Mike Leavitt, Secretary Carlos M. Gutierrez, Secretary 

Centers for Disease Control and Prevention Technology Administration 

Julie Louise Gerberding, Director Robert Cresanti, Under Secretary for Technology 

National Institute of Occupational Safety and Health National Institute of Standards and Technology 

John Howard, Director William Jeffrey, Director 
iii

ABSTRACT 

For firefighters and first response crews, the facial respirator is the first and last defense 

against hazardous and potentially harmful airborne agents. That defense is crippled if the 

seal of the respirator sprouts a leak, exposing the wearer to the dangers of the surrounding 

environment. Due to the inherent limitations of traditional anthropometric measures in 

modeling the nuanced and complex human face, fit testing standards for respirators 

remain imprecise. A recent review of the National Institute for Occupational Safety and 

Health (NIOSH) anthropometric survey called for 3D facial data to try and remove some 

of this uncertainty. 

Using the Civilian American and European Surface Anthropometry Resource (CAESAR) 

database finished in 2000, 3D facial models were created to test the rigid body fit of a 

half-mask respirator across different demographic groups. A good fit, in this study, is 

defined by a relatively small total distance between the face and the landmark points of 

the mask. Employing a best fit algorithm, the average face generated for Asians 

surprisingly accrued the least amount of gap while the male face had the greatest gap. 

When the landmark points of the mask were superimposed on a contour plot of the 

standard deviation in the face of each group, the plot revealed that the mask extended into 

areas of high facial variance. Upon analysis of the gap distance at each landmark, it was 

found that the sides of the mask were the areas with greatest separation from the face. 

Further investigation of wider than average faces confirmed little to no correlation 

between head breadth and total gap distance. 
iv

ACKNOWLEDGEMENTS 

The funding and support for this report was made possible by the Systems Integration for 

Manufacturing Applications (SIMA) and the Summer Undergraduate Research 

Fellowship (SURF) at the National Institute of Standards and Technology (NIST). The 

author would like to gratefully acknowledge the assistance of his advisor, Afzal Godil, 

who provided guidance and insight at every juncture. The author would also like to 

acknowledge the help of Kathryn Butler in providing the mask used in the analysis and 

reviewing this paper. Thanks are also given to Damien Bertot and Charles McLean for 

performing the 3D scan of the mask. 

DISCLAIMER 

Commercial products are cited in reference to the gap analysis procedures. The 

identification of such products is neither a recommendation nor endorsement by the 

National Institute of Standards and Technology, but is included for the purpose of 

experimental reproducibility. 
v

TABLE OF CONTENTS 

ABSTRACT...............................................................................................................iii 

ACKNOWLEDGEMENTS.......................................................................................iv 

DISCLAIMER ...........................................................................................................iv 

INTRODUCTION .....................................................................................................1 

PREPARATION OF FACIAL MODELS.................................................................2 

PREPARATION OF RESPIRATOR MASK............................................................3 

RIGID BODY ANALYSIS PROCEDURE...............................................................5 

QUANTITATIVE AND QUALITATIVE GAP ANALYSIS RESULTS ................8 

 Results: Total Gap by Demographic Analysis...............................................8 

 Results: Analysis of Gap at Parts of the Respirator.......................................9 

 Results: Correlation between Head Breadth and Fit......................................10 

 Results: Facial Variation................................................................................11 

CONCLUDING REMARKS.....................................................................................12 

REFERENCES ..........................................................................................................13 
1

INTRODUCTION 

For firefighters and first response crews, the facial respirator is the first and last 

defense against hazardous and potentially harmful airborne agents. At the crux of this 

defense is the seal of the respirator, which prevents harmful gases and particulates from 

gaining access to the wearer’s respiratory system. Respirators must be tough enough to 

withstand stress from the often laborious (or physically taxing) working conditions, yet 

malleable enough to maintain a proper seal during elevated breathing rates and facial 

motion from speaking—a difficult line to balance for manufacturers. If a gap or leak 

occurs in the seal, then the rest of the respiratory protective equipment is essentially 

rendered useless, leaving the wearer vulnerable to airborne threats. Although the 

importance of respirators is universally recognized, the fit test panels to certify respirators 

remain imprecise due to their dependence on an antiquated source survey. 

The Los Alamos National Laboratory (LANL) fit test panel developed in the 1970’s 

is based on an anthropometric survey conducted in 1967 of Air Force personnel and still 

the standard for today’s respirator fit tests. Subjects of the survey were generally young, 

physically fit individuals with limited diversity—a sample not representative of today’s 

civilian population. Considering the age of the survey and the dramatic change in the 

composition of the U.S. population, the National Institute for Occupational Safety and 

Health (NIOSH) conducted a new survey in 2001 entitled the NIOSH Head-and-Face 

Anthropometric Survey of U.S. Respirator Users to produce a more accurate picture of 

the civilian workforce. Subsequent analysis of the survey revealed that the LANL panels 

were in fact not representative of most respirator users in the U.S. Out of the 3997 

respirator users in the survey, 15.3 % of them were outside of the LANL fit test panel. 

Although the fit test panel is in the process of being updated, the core of these fit tests 

is still traditional anthropometric measures, which oversimplify the nuances and 

complexity of the human face. The contours, textures, and flexibility that make each face 

unique are largely ignored when using traditional measures with calipers. NIOSH 

understood this and had 3D facial scans initially implemented with the survey. However, 

the surveyors noticed discrepancies between traditional measures and the values 

extrapolated from the 3D scans. As a result, the 3D scans were not included in the final 

version of the revised NIOSH fit test panel, despite being essential to a full picture of the 

face. 

Previous studies in facial respirators have used 3D spatial landmarks to model the 

face, but this again is a large simplification of the geometry of the face. Recognizing this 

shortcoming, this study used the Civilian American and European Surface 

Anthropometry Resource (CAESAR) database finished a year prior to the NIOSH study 

to create 3D facial models to test the rigid body fit of a half-mask respirator across 

different demographic groups. It has been shown that a respirator offers its best 

protection when the seal fits a face properly. In light of this, a lower rigid body total gap 

between the face and the respirator landmarks is assumed to correlate to a better fit. 

Results from the preliminary total gap analysis led to an additional study into the 

correlation of head breadth and fit. 
2

PREPARATION OF FACIAL MODELS 

Before any investigation into the face can begin, 3D scan data must be uncompressed 

into a readable form and then transformed into a universal orientation for analysis. All of 

the calculations and algorithms were coded and compiled using Java based MATLAB 

7.3.0 (R2006). The CAESAR scans were saved in a .ply file format and were converted 

into two files: one containing the Cartesian coordinates of the points and the 

corresponding triangular connectivity and the other containing color information. 

With the scan points in a readable form, the full body needed to be trimmed and 

oriented uniformly to allow for deviations and average values to be extrapolated in a 

reasonably accurate manner. Varying heights of subjects and the inconsistent tilt of the 

face during the scan scattered the desired facial grid points in Cartesian space, requiring a 

series of affine transformations to bring the faces to the same location. Scaling was not 

performed on the faces because both size and shape of the face are relevant to a fit test of 

a mask. To achieve uniform agreement of the faces, the sellion landmark was placed at 

the origin (0,0,0) with the length of the face along the y-axis, the width of the face along 

the x-axis, and the depth of the face along the z-axis. Fig.1 shows the seated full body 

scan of a subject with all of the landmark points labeled. 

Fig. 1: Seated CAESAR subject with landmark points labeled
3

Not needing any points below the neck, the points below right and left clavicle 

landmarks were immediately cut to make subsequent transformations quicker. Then, 

using a common 3D translation matrix, the head was translated so that the sellion 

landmark was at the origin. A rotation around the z-axis aligned the supramenton and 

sellion along the x=0 plane. Then, a rotation around the x-axis aligned the supramenton 

directly below the sellion on the y-axis. After that, a rotation along the y-axis adjusted 

the left and right infraorbitale to give them the same depth. Fig. 2 b) illustrates the data 

manipulation up until this point. Finally, the back of the head was cut off using the left 

and right tragion landmark as the threshold. MATLAB interpolated a uniform mesh 

overtop the face and estimated the appropriate z value from the surrounding grid points. 

The final product is a 3D surface of the front of a face looking directly up the z-axis. Fig. 

2 c) shows the final interpolated surface of a subject’s face. 

Fig. 2 a) plot of all original, unchanged scan points for a full body scan in the CAESAR database 

 b) view from top of a correctly oriented head of grid points 

 c) plot of facial surface looking up z axis after cutting and interpolation 

PREPARATION OF THE RESPIRATOR MASK 

The mask used in the study is a medium (comes in medium and large sizes) half face 

air-purifying respirator (APR). Firefighters typically use a full face self-contained 

breathing apparatus (SCBA) in immediately dangerous to life or health (IDLH) 

environments, but the half mask provides insight around the chin and nose—areas where 

leaks are very common. Further, half mask respirators are similar to the nosepiece within 
4

the full face SCBA used by firefighters. Therefore, a half mask respirator was chosen as 

an effective model despite the fact that it is not directly used by firefighters. 

The 3D scan data of the respirator mask was gathered using the Konica Minolta Vivid 

9i at the Manufacturing Engineering Laboratory at NIST. Rendering and data writing 

were performed using Geomagic software. Fig. 3 a) displays the rendering of the 3D 

scan data of the respirator mask in Geomagic. As the respirator did not come with any 

landmark points, four points were arbitrarily chosen; northernmost, westernmost, 

easternmost, and southernmost. These landmarks were chosen to mimic the four 

landmarks used in orientation of the face with the northernmost acting as the sellion, the 

southernmost acting as the supramenton, and the westernmost and easternmost acting as 

the left and right infraorbitale respectively. Orientation can be seen in Fig. 3 b). 

Fig. 3 a) rendering of the mask performed in Geomagic 

 b) plot of the grid points of the mask after orientation 

 c) interpolated surface of the contact area of the mask 

 d) chosen landmark points superimposed on top of contact surface 

The mask was oriented using the same process performed on the face—the 

northernmost point was translated to the origin with the southernmost point directly 

below it on the y-axis. However, the mask was not as easy to interpolate. If multiple z 

values exist at a given x and y coordinate, then the interpolation function uses the average 

of the z values in creating the surface. Only the surface of the mask that comes into 

contact with the face is of interest in this study. As a result, the outside of the mask was 
5

neglected and the minimum z value for each x and y coordinate was used for 

interpolation. Fig. 3 c) displays the contact area of the mask that was used in analysis. In 

the interest of saving time while performing gap analysis, perfect symmetry of the mask 

was assumed and only the right side was considered. Seventeen landmark points were 

chosen on the final surface. These landmarks were the minimum z values for seventeen 

evenly spaced rows from the top to the bottom of the contact surface because it was 

believed that these minimum z values were the most likely to be the areas of first contact 

with the face. Fig 3 d) displays the contact surface with the landmark points 

superimposed on top. These seventeen landmark points would represent the entire mask 

in subsequent best fit and gap analysis procedures. In general, the mask landmarks near 

the center of the face represented the mask well, but the points near the side of the face 

were generally too far—a discrepancy created most likely because the sides of the mask 

were intended to be pulled to the side of the face by the respirator harness. 
6

RIGID BODY ANALYSIS PROCEDURE 

Of particular interest to facial anthropometry is differences based on race and gender. 

Understanding the inherent differences in facial structure between races and genders, 

respirator manufacturers tailor their products to better fit the population subset that is 

most likely to use their product. This study used broader ethnic classifications than the 

ones provided by the CAESAR database because the American survey size consisted of 

only 2400 subjects. The final groups considered in the study are: Asian/ Pacific Islander, 

White, Black, Hispanic, Other/Mixed, Male, and Female. Further classification of the 

subjects produced sets that were too small for proper analysis. 

With the demographic separations drawn, the average face for each group was 

generated. A simple calculation of the average z value at each x and y coordinate in the 

mesh for every subject would not suffice because the result is an amorphous face with 

poorly defined features that do not resemble that of a person. As a result, the traditional 

anthropometric measures provided in the database were used to identify the subjects that 

were the closest to the “average face” for each group. The subjects that were within the 

40th and 60th percentile in head breadth and 40th and 60th percentile in face length for each 

group were considered to be representative of the “average face”. Then, using this 

smaller representative group, the average z value at each x and y coordinate was 

calculated; the result was an average face with much more human features. When the 

time came to investigate the correlation of head breadth and fit, the thinner face for each 

demographic group was generated from subjects in the 5th to 25th percentile in head 

breadth and 40th to 50th percentile in face length and the wider face for each demographic 

was generated from subjects in the 75th to 95th percentile for head breadth and 40th to 50th

percentile in face length. Fig. 4 shows the side by side comparison of the wide face, 

average face, and skinny face. 

Fig. 4 shows the male wider face (left), average face (center), and skinnier face (right) 

With the face for each group generated and the landmark points of the mask chosen, 

the final step was to create a best fit algorithm to define a unique best fit for each face. 

This study assumed a rigid body analysis, which neglected flexing or deformation of the 

face or mask. Under these constraints, the best fit was defined as the arrangement of the 
7

landmark points that accrued the smallest total distance between the face and the 

landmark points of the mask. The gap at each landmark was defined as the minimum 

Cartesian distance ( d = √ ( (x1-x2)

2

 + (y1-y2)

2

 + (z1-z2)

2

) ) between the mask landmark 

and the face surface. Fig. 5 illustrates the results of the minimum distance function by 

plotting the landmark point and its corresponding closest point on the face. 

Fig. 5 is a diagram illustrating the minimum distance algorithm. The numbered mask landmarks are 

identified by a magenta X and the corresponding nearest points on the face are blue circles. 

The best fit algorithm developed was an iterative process consisting of three affine 

transformations: a translation along the z axis brought all of the landmark points just 

outside the surface of the face; a translation along the y axis moved the mask up and 

down the face around the bridge of the nose; and a rotation around the x axis tilted the 

mask away and toward the bridge of the nose. If, during the course of the iteration, the 

program found a transformation that produced a lower total gap than the current position 

of the landmarks, then the current position of the landmarks would automatically update 

itself. Appropriate transformation limits were set to prevent the mask from finding a best 

fit that was unrealistic. For instance, a best fit that placed the top of the mask on the 

forehead of a face would not be allowed under the limits. At no time would a 

transformation that placed a landmark point inside the surface of the face be accepted as 

the best fit. After each iteration, the transformations became finer until the algorithm 

converged or reached 10 iterations, whichever came first. 
8

QUANTITATIVE AND QUALITATIVE GAP ANALYSIS RESULTS 

Results: Total Gap by Demographic Analysis 

Running the best fit algorithm on the average face for each demographic yielded total 

gap data as shown in Fig. 6. As can be seen from the graph, the Asian/ Pacific Islander 

group had by far the smallest total gap with 78.32 mm; the next closest demographic was 

the average Black face with 101.22 mm. Whites, Hispanics, Other/Mixed, and females 

finished in the middle of the pack, but the demographic with the worst total gap was the 

average male face with 133.00 mm. When looking at labor force statistics for the United 

States, the results appear counterintuitive. As of June 2007, Whites have the largest labor 

force by race with 125.9 million compared to Asians with a mere 7.1 million in the labor 

force. Further, males have 83.0 million people 16 years or over in the labor force while 

females have 71.2 million people 16 years or over as of June 2007. Asians, as a 

relatively small segment of the U.S. labor force, appear to have garnered the least total 

gap across all the races. One possible explanation for this peculiarity is that the medium 

sized respirator was designed to better fit smaller faces. Traditional measures provided 

by the CAESAR database seem to support this claim because Asians have the smallest 

median head circumference and males have the largest median head circumference. 

Best Fit Total Gap Distance

0

20

40

60

80

100

120

140

White Black Asian Hispanic Other Male Female

Race/Gender

Distance (mm)

Fig. 6 shows the total gap for the respirator best fit on the average face for each demographic 
9

Results: Analysis of Gaps at Parts of the Respirator 

In light of this oddity in the data, a more detailed analysis of the parts of the mask 

was performed. Rather than considering the total gap for each demographic, the gap at 

each landmark was recorded. As can be seen from Fig. 7, landmarks 5-9 generate the 

greatest gap for the faces with values reaching almost 16 mm. All of the other landmark 

points accrue a negligible gap in comparison with values barely exceeding 9 millimeters. 

The mask landmarks 5-9 appear near the side of the face by the steep contours of the 

cheeks as can be seen in Fig. 5. The appearance of this could be attributed to the 

selection of the landmark points on the mask. If landmarks 5-9 were chosen to be closer 

to the center of the face as indicated by the blue circles on the face in Fig. 5, then the gap 

would logically be significantly smaller. Regardless of the cause of the trend, the results 

of the parts analysis compelled an investigation into the correlation between head breadth 

and fit. 

Fig. 7 is a plot of the gap distance at each landmark of the mask for the average face in each demographic 

group. Notice the large gaps around landmarks 5 through 9. Distance is in millimeters. mm 
10

Results: Correlation between Head Breadth and Fit 

The wider face for each demographic was taken from the average z values of the 

subjects inside the 40th to 60th percentile in face length and 75th to 95th percentile in head 

breadth; the skinnier face for each demographic was the average z values of the subjects 

inside the 40th to 60th percentile in face length and the 5th to 25th percentile in head 

breadth. It was hypothesized that a wider face would compliment the relatively flat 

contours of the respirator and fill the void around landmarks 5-9, resulting in a lower total 

gap. In the case of males, this relationship proved to be quite true with each wider face 

bringer a lower total gap. However, the reverse trend appeared in the Other face with gap 

actually increasing as width increased. Across the board, Fig. 8 shows the apparent 

dissimilarity between head breadth and total gap, further showing the difficulty of 

traditional anthropometric measures to predict the fit of a respirator. 

Total Gap Distance with Varying Head Breadth

0

20

40

60

80

100

120

140

160

180

Male Female White Asian Other Hispanic Black

Demographic Group

Total Gap (mm)

Skinnier

Average

Wider

Fig. 8 is a plot of the total gap distance across all demographics with a varying head breadth 

Despite the lack of a concrete correlation between head breadth and fit across 

demographic groups, this inquiry supported some trends that arose in the initial 

investigation; females continued to have a lower total gap than males, Asians continued 

to be near the bottom, and Whites consistently finished near the top. The fact that these 

trends continued in spite of the aforementioned labor force statistics is interesting. Once 
11

again this trend could be explained by the selection of the mask landmark points and the 

target market of the medium sized mask. 

Results: Facial Variation 

One final study involved the qualitative observation in the variation of the face for 

each demographic group. A contour plot of the standard deviation in the z value at each 

x and y coordinate created a visualization of which areas in the face for each 

demographic have the most variation. Due to the way all of the faces were oriented, 

considerable bias exists at the bridge of the nose and the chin where variations are lowest. 

During orientation, all of the sellion landmarks of the face are brought to the origin and 

the supramenton is brought directly below the sellion on the y axis. As a result, the 

sellion and supramenton are very close across all of the faces, resulting in low variations 

around the bridge of the nose and the chin. The outer edges of the face have the largest 

deviations typically. Superimposing the best fit landmarks on the contour plots reveals 

that the sides of the mask venture into areas with great variation in the front view as 

shown in Fig. 9a. This also indicates that the landmark points 7-9 were probably chosen 

to be too wide. A profile view of each face provides another view of the face and more 

detail about the cheeks of the face which is lacking from the front view. Fig. 9b shows 

how the best fit mask stays fairly flat on the face and enters areas of substantial variation 

below the chin and near the eyes. 

A) B) 

Fig. 9 shows the interpolated contour plot of the standard deviation at each point in the face. The best fit 

landmarks are shown as magenta circles. Areas of red indicate high deviations and areas of blue are low 

deviations. Surrounding white areas are deviations in excess of 16. a) is the front view of the female 

variation and b) is the profile view of the female variation. 
12

CONCLUDING REMARKS 

Perhaps the most surprising result of the experiment is how well the respirator fit the 

Asian face at all widths when considering the small number of Asians in the U.S. labor 

force. Granted, these labor statistics may not align precisely with the targeted respirator 

user as identified by the manufacturer for the medium sized half mask, but it is still 

significant to note that the Asian face fit well in all categories and the male face fit 

poorly. 

It is also prudent to note that some of the assumptions used in this study may account 

for some degree of inaccuracy. First of all, the landmark points were chosen in an 

arbitrary manner by assuming that the minimum z value for 17 evenly spaced rows would 

best simulate the contact line for each face. However, the variation analysis proved that 

some landmarks, particularly 7-9 were impractical and appeared in areas of the face with 

substantial variation. Moreover, the decision to use landmark points instead of a full 

surface for the contact area of the mask may have oversimplified the mask. Every face 

creates a unique area of contact with the respirator; and, by using landmarks this study 

observes only one of a seemingly infinite number of arrangements. Perhaps a future 

investigation into the contact area/line between the face and the mask for each individual 

would identify areas of interest for the face and the mask. 

The analysis of the head breadth also offered a glimpse into the difficulty of modeling 

a fit test using only single dimension measures. Principle Component Analysis, which 

combines several single dimension measures has shown success in dealing with other 

areas of the body, but has yet to show a reasonable degree of success in respirator fit 

tests. Attention to 3D scans of the face seems necessary if fit testing for respirators is to 

advance any further. 

Unfortunately, rigid body analysis in this study does not take into account any of the 

malleable features of the face or the mask. In fact, it is not yet certain that a good fit 

identified in a rigid body analysis even correlates to a good fit in a test panel. Future 

studies with finite element analysis to model the stretching and deformation in the skin of 

the face and rubber of the respirator will undoubtedly shed more light on the matter and 

help 3D anthropometry realize its true potential in fit test modeling. 
13

REFERENCES 

CAESAR: Civilian American and European Surface Anthropometry Resource web site: 

http://www.hec.afrl.af.mil/cardlab/CAESAR/index.html. 

Coffey, Christopher C., Lawrence, Robert B., Zhuang, Ziqing, Campbell, Donald L., 

Jensen, Paul A., and Myers, Warren R.: Comparison of five methods for fit-testing 

N95 filtering-facepiece respirators. App. Occup. Environ. Hyg.: 17:10, 723 – 730 

(2002). 

Han D.-H., Rhi, J., and Lee J.: Development of prototypes of half-mask facepieces for 

Koreans using the 3D digitizing design method: a pilot study. Ann. Occup. Hyg.; 48: 

707-14 (2004). 

Institute of Medicine (IOM). 2007. Assessment of the NIOSH Head-and-Face 

Anthropometric Survey of U.S. respirator users. Washington, DC: The National 

Academies Press. 

Kim, K., and Kim, H.: Three-dimensional shape analysis of commercial half-facepiece 

respirators for Koreans: structural approach. J. Int. Soc. Respir. Protect. 23:89 (2006). 

Los Alamos Scientific Laboratory of the University of California: Selection of Respirator 

Test Panels Representative of U.S. Adult Facial Sizes by A.L. Hack, E.C. Hyatt, B.J. 

Held, et al. (LA5488). Los Alamos, N.M.: Los Alamos Scientific Laboratory of the 

University of California, 1973. 

Oestenstad RK, Dillion HK, Perkin LL. Distribution of faceseal leak sites in a half-mask 

respirator and their association with facial dimensions. Am Ind Hyg Assoc J. 51: 285-

290 (1990). 

U.S. Bureau of Labor Statistics: Employment situation June 2007. Washington, D.C.: 

Bureau of Labor Statistics, 2007. 

Zhuang, Z., and Bradtmiller, B.: Head-and-face anthropometric survey of U.S. respirator 

users. J. Occup. Environ. Hyg.; 2: 567-576 (2005).