NISTIR 7921

Camera Recognition

Michelle Steves

Brian Stanton

Mary Theofanos

Dana Chisnell

Hannah Wald

http://dx.doi.org/10.6028/NIST.IR.7921
NISTIR 7921

Camera Recognition

Michelle Steves

Brian Stanton

Mary Theofanos

Information Access Division

Information Technology Laboratory

Dana Chisnell

Usability Works

Boston, MA

Hannah Wald

Booz Allen Hamilton

McLean, VA

http://dx.doi.org/10.6028/NIST.IR.7921

March 2013

U.S. Department of Commerce 

Rebecca Blank, Acting Secretary

National Institute of Standards and Technology 

Patrick D. Gallagher, Under Secretary of Commerce for Standards and Technology and Director 
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Table of Contents

1 INTRODUCTION......................................................................................................................................2

2 METHOD....................................................................................................................................................3

2.1 Participants .........................................................................................................................................3

2.2 Materials.............................................................................................................................................5

2.3 Equipment...........................................................................................................................................5

2.4 Procedure ............................................................................................................................................6

2.4.1 Demographic questionnaire ...........................................................................................................6

2.4.2 Part 1: Identifying cameras ............................................................................................................7

2.4.3 Part 2: Categorizing devices...........................................................................................................7

2.4.4 Part 3: Debrief................................................................................................................................7

3 RESULTS....................................................................................................................................................8

3.1 Responses to “Is this a camera?” question..........................................................................................8

3.1.1 Overall accuracy of participant responses......................................................................................8

3.1.2 Results for image capture devices..................................................................................................9

3.1.3 Results for non-image capture devices.........................................................................................15

3.2 Card Sorting......................................................................................................................................20

3.2.1 Number of sorting categories.......................................................................................................20

3.2.2 Sorting category labels.................................................................................................................21

3.2.3 Category features and attributes...................................................................................................22

3.3 Debrief..............................................................................................................................................25

3.3.1 Previous experience with cameras ...............................................................................................25

3.3.2 How participants identified cameras............................................................................................25

4 DISCUSSION ...........................................................................................................................................26

4.1 Identifying Devices...........................................................................................................................26

4.1.1 Image capture devices (cameras) .................................................................................................26

4.1.2 Non-image capture devices (not cameras) ...................................................................................27

4.1.3 Phones..........................................................................................................................................29

4.1.4 Uncertainty (the “Miscellaneous” class)......................................................................................29

4.1.5 Primary camera attributes ............................................................................................................29
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4.2 Issues affecting the study..................................................................................................................30

4.2.1 Environment and circumstances...................................................................................................30

4.2.2 Demographics ..............................................................................................................................31

4.2.3 Technology exposure ...................................................................................................................32

5 CONCLUSION.........................................................................................................................................32

6 REFERENCES .........................................................................................................................................34

APPENDIX A: DEMOGRAPHIC QUESTIONS ...........................................................................................35

APPENDIX B: DEVICE IMAGES USED IN THE STUDY .........................................................................36

APPENDIX C: DATA COLLECTION SHEET .............................................................................................39

APPENDIX D: CAMERA STUDY SCRIPT...................................................................................................41

APPENDIX E: WHERE ARE VISITORS TO THE US COMING FROM?...............................................45

APPENDIX F: TECHNOLOGY ADOPTION BY COUNTRY ....................................................................48
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List of Figures

FIGURE 1: A TYPE OF CAMERA USED BY US-VISIT TO CAPTURE FACIAL IMAGES...................................................3

FIGURE 2: SELF-REPORTED ETHNICITY FOR THE STUDY POPULATION .....................................................................4

FIGURE 3 IS THIS A CAMERA? APPLICATION .............................................................................................................6

FIGURE 4: PERCENTAGE OF CORRECT IDENTIFICATIONS PER IMAGE CAPTURE DEVICE..........................................14

FIGURE 5: PERCENTAGE OF CORRECT IDENTIFICATIONS PER NON-IMAGE CAPTURE DEVICE..................................19

FIGURE 6: NUMBER OF CATEGORIES USED BY PARTICIPANTS DURING THE CARD SORT EXERCISE .........................20

FIGURE 7: FROM LEFT TO RIGHT, A PHOTOGRAPHY CAMERA, A WEB CAMERA, AND A SURVEILLANCE CAMERA ...27

FIGURE 8: NON-IMAGE CAPTURE DEVICES THAT PARTICIPANTS FREQUENTLY MISTOOK FOR CAMERAS................28

List of Tables

TABLE 1: STUDY PARTICIPANTS' SELF-REPORTED COUNTRIES OF ORIGIN................................................................4

TABLE 2: SUMMARY OF CAMERA IDENTIFICATION RESULTS – TRUE AND FALSE POSITIVES AND NEGATIVES ..........9

TABLE 3: IMAGE CAPTURE DEVICE IDENTIFICATION FOR INDIVIDUAL DEVICES ....................................................10

TABLE 4: NON-IMAGE CAPTURE DEVICE IDENTIFICATION FOR INDIVIDUAL DEVICES............................................15

TABLE 5: CARD SORT CATEGORY LABELS, NUMBER OF TIMES USED, AND BROAD CLASSES ..................................21

TABLE 6: PARTICIPANTS’ REPORTED EXPERIENCE WITH DIFFERENT TYPES OF CAMERAS......................................25

TABLE 7: TECHNOLOGY ADOPTION STATISTICS FOR THE US AND TOP 15 VISITING COUNTRIES ............................50
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EXECUTIVE SUMMARY

The Department of Homeland Security’s (DHS) United States Visitor and Immigrant Status 

Indicator Technology (US-VISIT) program is a biometrically-enhanced identification system 

primarily situated at border points of entry such as airports and seaports. The US-VISIT 

program’s goal is to advance the security of the United States and worldwide travel through 

information sharing and biometric solutions to facilitate identity management. The 

biometrics currently captured at US-VISIT primary inspection are fingerprints and a facial 

image. For the purposes of our study, we are interested in the latter.

In a 2004 assessment of the quality of facial images captured by US-VISIT, the National 

Institute of Standards and Technology (NIST) discovered a widespread problem: many 

subjects were 1) not directly facing the camera and 2) had a pose angle of greater than 10 

degrees [2]. The findings of NIST’s subsequent follow-up studies suggest that the camera 

used to capture facial images of travelers should look as much as possible like a traditional 

camera [4]. Knowing where to look will help the subjects being photographed orient 

themselves in such a way that they are frontal to the camera – thus improving picture quality.

This study explored whether participants could discern image capture devices (i.e., cameras) 

from other types of technology, and the attributes they relied upon to make that distinction. 

In a controlled environment, we presented participants with 50 images of small (hand-held) 

devices and asked participants to indicate whether or not a given device was a camera. We 

then asked participants to group the devices into 2 to 5 categories and list the attributes they 

had used as a basis for assigning devices to each group. 

We did not attempt to simulate the environment or context in which US-VISIT cameras are 

normally used – specifically, a busy, active, visually and aurally crowded port of entry. 

However, if individuals in a lower-stress environment were not able to identify image 

capture devices, it is unlikely that people in a more stressful situation will be able to do so.

Our findings indicate that the more an image capture device resembles a traditional 

photography camera – generally rectangular with a round, projecting lens and a flash – the 

easier it is for individuals to notice that device and recognize it. Once this recognition occurs, 

it is then expected that individuals could more confidently position themselves properly to 

have their pictures taken.
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1 INTRODUCTION

The Department of Homeland Security’s (DHS) United States Visitor and Immigrant Status 

Indicator Technology (US-VISIT) program is a biometrically-enhanced identification system 

primarily situated at border points of entry such as airports and seaports. The US-VISIT 

program’s goal is to advance the security of the United States and worldwide travel through 

information sharing and biometric solutions to facilitate identity management. The 

biometrics currently captured at US-VISIT primary inspection are fingerprints and a facial 

image. The fingerprint component of the system uses automated matching along with manual 

match verification. The face image capture process does not include automated face 

recognition but relies on human verifiable traveler history.

A face image quality assessment of airport ports of entry performed in 2004 found a number 

of quality issues in approximately 1.5 million facial images captured by US-VISIT [2]. One 

issue was that only 5% of the time were the subjects being photographed directly facing the 

camera, that is, they were frontal to it, and approximately 70% of facial images had a pose 

angle of greater than 10 degrees.

As the result of this assessment US-VISIT has embarked on a program for face image quality 

improvement. One aspect of this effort is the identification of usability and human factors 

issues that may impact face image capture. The National Institute of Standards and 

Technology’s (NIST) usability and biometrics team was asked to identify any usability and 

human factors considerations that may improve the capture of face images at the airports.

This study, which is part of that effort, focuses specifically on the camera used to take 

pictures of travelers at ports of entry. In a previous study, we recommended that the camera 

used to capture facial images of travelers should look as much as possible like a traditional 

camera [4]. We suspect that the device currently used by US-VISIT – a webcam on the end 

of a flexible gooseneck, as shown in Figure 1 – may not be recognizable to some travelers as 

a camera, which makes them unsure of where to look when having their picture taken. 

Knowing where to look will help the subjects being photographed orient themselves in such 

a way that they are frontal to the camera – thus improving picture quality.
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In this between participants study1, we explored how well 

participants could recognize cameras from an array of devices. 

We also attempted to identify what features make a particular 

device recognizable as a camera. In essence, our study was 

designed to answer two questions: 

1. In a visually and aurally quiet environment (i.e., one 

with no distractions), can participants distinguish camera 

devices from other pieces of technology?

2. What are the attributes of image capture devices that 

make them recognizable as cameras?

By answering these questions, we hope to provide 

recommendations that will inform the design and/or selection 

of cameras to be used by the US-VISIT program for capturing 

facial images at ports of entry. We also anticipate that an 

easily recognizable camera will be an important part of any “self-service” facial image 

capture solution that US-VISIT may implement in the future.

2 METHOD

2.1 Participants

Researchers solicited for volunteer participants for this study in the DC area. There were a 

total of 86 participants in the camera recognition study. 42 were male and 44 were female. 

The average age of the participants was 39 years, with the youngest participant being 20 

years old, and the oldest being 69 years old. Participants also reported their ethnicity on the 

demographic survey; Figure 2 displays the demographic breakdown of the study population.

 1 Between subjects design is a study design where every participant is subjected to a single treatment. It is also 

referred to as an independent measures design.

Figure 1: A type of camera used 

by US-VISIT to capture facial 

images
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Figure 2: Self-reported ethnicity for the study population

Our participants also reported their countries of origin. As shown in Table 1, the vast 

majority were American (76 of 86, or 88.37%), but there were a few participants from other 

countries as well.

Table 1: Study participants' self-reported countries of origin

Country Count %

USA 76 88.37

Vietnam 2 2.33

Canada 1 1.16

China 1 1.16

Columbia 1 1.16

El Salvador 1 1.16

Gabon 1 1.16

Haiti 1 1.16

Kyrgyzstan 1 1.16

Republic of the Congo 1 1.16

48.84%

27.91%

5.81%

12.79%

4.65%

Self-reported ethnicity

White

Black

Asian

Hispanic/Latino

other
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Finally, participants were asked if they had had their biometrics captured before and whether 

that included facial image capture. Fifty-two (or 60.47%) said they had biometrics captured 

previously and 8 (or 9.30%) reported that this biometrics data included facial image data. 

2.2 Materials

The following materials were used in this study:

• Informed consent form explaining the study to participants and how the data would 

be used

• Demographic questionnaire asking participants about their age, ethnicity, occupation,

and previous experience with biometrics data capture, contained in Appendix A: 

Demographic Questions

• Script for the researcher that stipulated what the researcher should say, directions for 

moving from station to station in the lab space, and pointers for data collection, 

contained in Appendix D: Camera Study Script

• Data collection sheets on which the researcher tracked participants' behaviors and 

comments, and recorded observations, contained in Appendix C: Data Collection 

Sheet

2.3 Equipment

During the study, participants used a personal computer with an application that presented 

them with images of 50 devices2, one at a time. Appendix B: Device Images used in the 

Study contains the images. For each image, participants were prompted to answer the 

question “Is this a camera?” Participants could respond “Yes” or “No” by clicking the 

appropriate checkbox. A sample screenshot from this application can be seen in Figure 3.

 2 The use of certain commercial equipment in this study is not intended to imply recommendation or 

endorsement by National Institute of Standards and Technology, nor is it intended to imply that the 

equipment shown is necessarily the best available for the purpose.
NISTIR 7921 Page 6 3/2013

Figure 3 Is this a Camera? application

Twenty-nine of the 50 devices (or 58%) were actually cameras. The application would record 

participants’ positive (“Yes”) or negative (“No”) responses to each image, as well as whether 

participants were correct in identifying the device shown as a camera (or not).

In addition, participants were issued 50 laminated cards measuring 5.1 cm by 7.6 cm (2 in.

by 3 in.), each bearing one of the images previously used in the “Is this a camera?” 

application. Participants sorted these cards into categories and labeled the categories using 

sticky notes (Section 2.4.3 contains further details).

2.4 Procedure

2.4.1 Demographic questionnaire

Each participant signed an informed consent form and filled out a demographic questionnaire 

(Appendix A: Demographic Questions) before proceeding with the study as described in the 

following subsections. The demographic questionnaire asked participants for their age, 

gender, ethnicity, and profession. It also asked participants if they had previous experience 

with biometric data capture, and if so, what type of data was captured, e.g., fingerprints, 

facial image, and so on.

Is this a camera? Yes □ No □
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2.4.2 Part 1: Identifying cameras

Participants used a computer-based application to view 50 images of devices. Each was 

accompanied by the question “Is this a camera?” The participants would answer “Yes” or 

“No” by clicking in the corresponding check box on the screen. The application recorded 

their choices. It also recorded whether or not a participant correctly identified a device as a 

camera or something else, i.e., whether they entered “Yes” for a camera device or “No” for a 

non-camera device.

2.4.3 Part 2: Categorizing devices

After participants completed the computer-based portion of the study, they were given 50 

cards, each measuring 5.1 cm by 7.6 cm (2 in. by 3 in.) and each bearing the image of one of 

the devices they had seen on the computer. The researcher instructed participants to sort the 

cards into 2 to 5 categories of their choosing. Participants were given five minutes for this 

task, but could take more time if they wished.

Once the participants finished sorting their cards, the researcher gave them a sticky note pad 

and pen and asked them to make a label for each category. Then the researcher asked 

participants to list the features or attributes of each group. The researcher recorded the 

participants’ categories and associated attributes on the data sheet.

2.4.4 Part 3: Debrief

After participants completed parts 1 and 2, the researcher debriefed them about their previous 

experience with cameras (e.g., whether or not they had ever operated various types of 

cameras). In addition, the researcher asked participants about their experiences with 

biometric data capture (as in, being the subject of biometric data capture, not the operator of 

the biometric device) and whether that included a facial image capture. The researcher used 

the checklist at the bottom of the data sheet to record participant responses. The researcher 

then inquired how the participant identified a particular device as a camera. Finally, the 

researcher thanked the participants for taking part in the study.
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3 RESULTS

Researchers collected the following data from participants:

• Results of “Is this a camera?” application – whether the participant answered 

positively or negatively, and whether they correctly identified a given device as a 

camera (or not).

• Card sorting category labels and features/attributes

• Participants’ previous experience with cameras

• How participants identified a particular device as a camera

The information collected is described in detail in the following subsections.

3.1 Responses to “Is this a camera?” question

When participants used the computer application that presented them with images of 50 

devices, they classified each device as a camera or non-camera (which we refer to as “image 

capture” or “non-image capture” devices). This section describes the results of that exercise.

3.1.1 Overall accuracy of participant responses

Table 2 is a matrix showing participants’ overall accuracy when identifying image capture or 

non-image capture devices while using the “Is this a camera?” application. The top row 

shows the percentage of participant responses that were “True Positive” or “True Negative,” 

meaning that they were accurate in regards to whether the device presented was a camera or 

not. The bottom row shows the percentage of false responses, or cases in which participants 

incorrectly identified a non-image capture device as an image capture device (or vice-versa).
NISTIR 7921 Page 9 3/2013

Table 2: Summary of camera identification results – true and false positives and negatives

Positives 

(“Yes”)

Negatives 

(“No”)

True (participant response was correct) 88.69% 68.05%

False (participant response was incorrect) 11.31% 31.95%

The data show that participants were fairly accurate when positively identifying devices that 

functioned as cameras – nearly 89% of their positive responses to the “Is this a camera?” 

question were correct. Participants only gave false positive responses (in which they 

identified non-image capture devices as image capture devices) approximately 11% of the 

time. However, they were somewhat less accurate when identifying non-image capture 

devices; when presented with these devices by the computer application, they only identified 

non-image capture devices as such (through a true negative response) approximately 68% of 

the time, and mistook the non-image capture devices for image capture devices almost 32% 

percent of the time. That is, 32% of the time that subjects were presented with images of 

actual cameras, they did not recognize them as such.

3.1.2 Results for image capture devices

Table 3 provides the count and corresponding percentage of correct and incorrect 

identification responses for each image capture device shown in the study (on the “Is this a 

camera?” application and later on the 5.1 cm by 7.6 cm cards). Of the 50 devices presented to 

participants, 29 were actually image capture devices (i.e., cameras). 

Note that some devices received the same number of correct identification responses – for 

example, the first eleven devices shown in Table 3 were correctly identified 98.84% of the 

time. No additional ranking within a particular response count (i.e., among those eleven 

devices) is intended by the tabular and graphical representations of the data – all are 

equivalent. Figure 4, following the table, provides a visual reference for the percentage of 

correct identifications (that is, “Yes” responses to the “Is this a camera?” question) for each 

device depicted in the table.
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Table 3: Image capture device identification for individual devices

Correct Responses Incorrect Responses

Device 

Reference 

Number

Device Image # % # %

Ref 1 85 98.84% 1 1.16%

Ref 2 85 98.84% 1 1.16%

Ref 3 85 98.84% 1 1.16%

Ref 4 85 98.84% 1 1.16%

Ref 5 85 98.84% 1 1.16%

Ref 6 85 98.84% 1 1.16%

Ref 7 85 98.84% 1 1.16%

Ref 8 85 98.84% 1 1.16%
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Correct Responses Incorrect Responses

Device 

Reference 

Number

Device Image # % # %

Ref 9 85 98.84% 1 1.16%

Ref 10 85 98.84% 1 1.16%

Ref 11 85 98.84% 1 1.16%

Ref 12 84 97.67% 2 2.33%

Ref 13 84 97.67% 2 2.33%

Ref 14 83 96.51% 3 3.49%

Ref 15 83 96.51% 3 3.49%
NISTIR 7921 Page 12 3/2013

Correct Responses Incorrect Responses

Device 

Reference 

Number

Device Image # % # %

Ref 16 83 96.51% 3 3.49%

Ref 17 82 95.35% 4 4.65%

Ref 18 82 95.35% 4 4.65%

Ref 19 82 95.35% 4 4.65%

Ref 20 82 95.35% 4 4.65%

Ref 21 80 93.02% 6 6.98%

Ref 22 72 83.72% 14 16.28%
NISTIR 7921 Page 13 3/2013

Correct Responses Incorrect Responses

Device 

Reference 

Number

Device Image # % # %

Ref 23 69 80.23% 17 19.77%

Ref 24 63 73.26% 23 26.74%

Ref 25 63 73.26% 23 26.74%

Ref 26 61 70.93% 25 29.07%

Ref 27 52 60.47% 34 39.53%

Ref 28 39 45.35% 47 54.65%

Ref 29 33 38.37% 53 61.63%
NISTIR 7921 Page 14 3/2013

Figure 4: Percentage of correct identifications per image capture device

38.37%

45.35%

60.47%

70.93%

73.26%

73.26%

80.23%

83.72%

93.02%

95.35%

95.35%

95.35%

95.35%

96.51%

96.51%

96.51%

97.67%

97.67%

98.84%

98.84%

98.84%

98.84%

98.84%

98.84%

98.84%

98.84%

98.84%

98.84%

98.84%

0.00% 20.00% 40.00% 60.00% 80.00% 100.00%

Ref 29

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As mentioned in Section 3.1.1, participants were generally highly successful in positively 

identifying image capture devices. Participants had a success rate of over 90 percent when 

identifying 21 of the 29 devices: for 11 of those devices, the success rate was over 98%. For 

only 2 devices did participants have a success rate of less than 50%.

3.1.3 Results for non-image capture devices

Table 4 and Figure 5 below are similar to those in Section 3.1.2, except that they deal with 

non-image capture devices – of which there were 21 in the set of 50 presented to participants 

– and count correct negative (rather than positive) responses to the “Is this a camera?” 

question.

Table 4: Non-image capture device identification for individual devices

Correct Responses Incorrect Responses

Device 

Reference 

Number

Device Image # % # %

Ref 30 85 98.84% 1 1.16%

Ref 31 82 95.35% 4 4.65%

Ref 32 81 94.19% 5 5.81%

Ref 33 79 91.86% 7 8.14%
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Correct Responses Incorrect Responses

Device 

Reference 

Number

Device Image # % # %

Ref 34 77 89.53% 9 10.47%

Ref 35 68 79.07% 18 20.93%

Ref 36 67 77.91% 19 22.09%

Ref 37 67 77.91% 19 22.09%

Ref 38 64 74.42% 22 25.58%

Ref 39 63 73.26% 23 26.74%

Ref 40 62 72.09% 24 27.91%

Ref 41 61 70.93% 25 29.07%
NISTIR 7921 Page 17 3/2013

Correct Responses Incorrect Responses

Device 

Reference 

Number

Device Image # % # %

Ref 42 59 68.60% 27 31.40%

Ref 43 53 61.63% 33 38.37%

Ref 44 45 52.33% 41 47.67%

Ref 45 44 51.16% 42 48.84%

Ref 46 40 46.51% 46 53.49%

Ref 47 40 46.51% 46 53.49%
NISTIR 7921 Page 18 3/2013

Correct Responses Incorrect Responses

Device 

Reference 

Number

Device Image # % # %

Ref 48 39 45.35% 47 54.65%

Ref 49 32 37.21% 54 62.79%

Ref 50 21 24.42% 65 75.58%
NISTIR 7921 Page 19 3/2013

Figure 5: Percentage of correct identifications per non-image capture device

As seen in the chart above, participants had a success rate of greater than 90 percent when 

identifying 4 of the 21 non-image capture devices as such. For 7 devices in the non-image 

capture group, participants had a success rate of more than 70% but less than 90%. However, 

they had a success rate of only slightly over 50% with two devices, and less than 50% with 5 

24.42%

37.21%

45.35%

46.51%

46.51%

51.16%

52.33%

61.63%

68.60%

70.93%

72.09%

73.26%

74.42%

77.91%

77.91%

79.07%

89.53%

91.86%

94.19%

95.35%

98.84%

0.00% 10.00% 20.00% 30.00% 40.00% 50.00% 60.00% 70.00% 80.00% 90.00% 100.00%

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of them. For one device, the success rate was 24.42% – the lowest of any device shown to 

participants.

3.2 Card Sorting

The following subsections present data from the card-sorting exercise that participants 

performed after using the “Is this a camera?” application. The data include the number of 

categories into which participants sorted cards, how participants labeled those categories, and 

the attributes/characteristics participants used to distinguish between categories.

3.2.1 Number of sorting categories

Participants were asked to sort their cards into 2 to 5 categories. Figure 6 shows the 

distribution of the number of categories into which participants sorted their cards. As shown, 

many participants preferred to separate rather than consolidate: on average, participants used 

4.3 categories, with the majority of participants (65.12%) using five categories.

Figure 6: Number of categories used by participants during the card sort exercise

65.12%

23.26%

5.81%

5.81%

Number of categories used

5 4 3 2
NISTIR 7921 Page 21 3/2013

3.2.2 Sorting category labels

Participants were asked to label the categories they made using sticky notes. Participants 

created a total of 31 labels. In an effort to consolidate the data, we sorted participant labels 

into four broad classes, based on participant category labels. These four classes are: Cameras, 

Phones, Non-Cameras, and Miscellaneous. Categories in the “Miscellaneous” class have 

ambivalent or nonspecific labels (e.g., “Unknown/no clue,” “Miscellaneous,” “Camera-like 

device”): participants essentially used these categories for devices they could not place in 

their other categories, or devices they could not identify. Table 5 below displays the labels 

used, the number of times they were used, and the broad classes into which we sorted them.

Table 5: Card sort category labels, number of times used, and broad classes

Broad Classes Category Labels Number of 

Times used

Cameras

Camera/traditional camera 76

Web/computer camera 52

Monitoring/surveillance device 12

Image capture device (all) 8

Looks like a (it is a) camera - not traditional 2

Portable webcam 2

Non-traditional photography device 2

Watch camera 2

Robotic camera 1

Low quality image capture 1

Camcorder 2

Specialized / industrial (such as DMV or other ID) 1

Phones Cell phone/smart phone 67

Landline phone 1

Non-Cameras Portable digital media player/MP3 43
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Broad Classes Category Labels Number of 

Times used

No camera 14

Other electronic devices (including flash drives) 13

Digital storage 6

Radio 2

Scanner 2

No Lens 1

Bluetooth 1

Glucose meter 1

Camera accessory 1

Miscellaneous

Unknown/no clue 38

Miscellaneous 15

Could be a camera - possible lens 3

Handheld 4

Appearance did not suggest camera - may have had a 

lens/Not sure if it was a camera 2

Maybe a camera - couldn't find the lens 2

Camera-like device 1

Form over function 1

Recreational 1

TOTAL 31 Labels 380

3.2.3 Category features and attributes

This section lists the attributes participants most commonly used to categorize devices. We 

have divided the attributes into the broad classes used in Section 3.2.2.
NISTIR 7921 Page 23 3/2013

3.2.3.1 Cameras

When asked during the debriefing, 67 of the 86 participants (or 77.9%) reported that they 

associated the “lens” attribute with the categories in the Cameras class (see Section 3.3.2). 

“Lens” was, in fact, the attribute that participants most frequently associated with Camera 

categories. The other most commonly used attributes were:

• Shape3

• Buttons/lack of buttons

• Branding

• Has a base/stand/legs/swivels (specifically for Web or surveillance cameras)

• Flash 

Participants reported using other attributes as well, but with much less frequency than the 

ones listed above, reflecting the fairly broad set of participant-labeled categories for imagecapture devices. 

3.2.3.2 Phones

Participants provided a relatively short list of defining attributes for devices they categorized 

as phones. The four most common attributes associated with categories in this class are: 

• Buttons/keypad

• Screen

• Shape

• Lens

Participants used the “lens” attribute for devices in the Phone class because – as many of 

them observed – most newer cell phones have cameras in them. Some participants reported 

 3 Participants also commonly used the “shape” attribute for categories in the Phones and Non-Image Capture 

Device class: participants used it in the sense of “the shape of this device makes me think it is ______.”
NISTIR 7921 Page 24 3/2013

that they assumed cell phones contain a camera and classified them as image capture devices 

without looking for a lens feature.

3.2.3.3 Non-Cameras

Participants associated many different attributes with devices in the Non-Cameras class. The 

attributes they reported using most frequently were:

• No lens

• USB connector

• Buttons suggesting functionality other than a camera (e.g., those for an MP3 player: 

play, rewind, fast forward)

• Shape (suggesting some functionality other than a camera)

• Screen or display (or lack thereof) suggesting some functionality other than a camera

3.2.3.4 Miscellaneous

Participants’ attributes for devices in the Miscellaneous class typically reflected their 

ambivalence regarding the functionality of the devices, or whether or not the devices might 

have a lens that they were unable to see. Participants frequently reported associating the 

following attributes with devices in this class: 

• Don’t recognize/not sure

• No idea (what it is)

• Could be some something else entirely (than a camera device)

• No lens

• May have a lens (but couldn’t find it)
NISTIR 7921 Page 25 3/2013

3.3 Debrief

3.3.1 Previous experience with cameras

After participants completed the device identification and grouping exercises, the researcher 

asked them about their previous experience using various types of cameras. Participant 

responses are summarized in Table 6.

Table 6: Participants’ reported experience with different types of cameras

Usage Experience Number of participants reporting 

experience

“Point and Shoot” type camera 82

Phone camera 69

Video camera 56

Webcam 40

Single Lens Reflex (SLR) 29

No camera use 0

All 86 participants had experience with one or more types of cameras: 82 (95.35%) of 

participants had used a “traditional” point-and-shoot camera, 69 (80.23%) had used a cell 

phone camera, 56 (65.12%) had used a video camera, and 40 (46.51%) of them had used a 

webcam.

3.3.2 How participants identified cameras

During the debriefing, the researcher asked participants what criteria they used to determine 

whether a device was a camera or not. 67 of 86 participants (77.9%) reported that they used 

the presence or absence of a lens as an indicator of whether a particular device could capture 

an image.
NISTIR 7921 Page 26 3/2013

4 DISCUSSION

The goal of our study was to determine how people distinguish image capture devices from 

other kinds of devices. This section deals with our findings on how people distinguish (or fail 

to distinguish) between cameras and other devices. We also address issues affecting the 

study, such as differences between our study population and the foreign nationals who 

undergo the US-VISIT biometric collection process at US ports of entry.

4.1 Identifying Devices

4.1.1 Image capture devices (cameras)

When attempting to determine whether or not a particular device was a camera, nearly 78% 

of participants (as described in Section 3.3.2) reported that they looked for a lens. This 

assertion is borne out by the results of the card sorting exercise: the attribute most commonly 

associated with categories in the Cameras class (which had labels such as 

“Camera/traditional camera,” “Web/computer camera,” and “Monitoring/surveillance 

device”) was a lens (see Section 3.2.2).

Other attributes participants frequently associated with cameras were shape (in the sense of 

“the shape of this device makes me think it is a camera”); buttons; branding (as in “it has the 

name of a camera company on it”); the inclusion of a base or stand and/or the apparent 

capacity to swivel; and a flash.

By and large, the participants considered a lens to be the critical, defining element of a 

camera, and their high overall success rate when attempting to positively identify cameras –

almost 89%, as noted in Section 3.1.1 – indicates that the “look for a lens” strategy was a 

valid one. However, it is important to note that participants drew distinctions between 

different types of cameras based on the secondary attributes listed above: these distinctions 

are reflected in the three most common category labels participants created during the card 

sorting exercises. These labels were “Camera/traditional camera” (76 times), “Web/computer 

camera” (52 times), and “Monitoring/surveillance device” (12 times).

While all the devices in these categories obviously had common attributes participants 

associated with cameras (most likely the lens, since many participants regarded a lens as a 

defining attribute for an image capture device), they also had different attributes that 
NISTIR 7921 Page 27 3/2013

participants seemed to consider noteworthy. One likely example is the shape of the camera: 

photography cameras, whether digital or traditional, have a very different profile from most 

web cameras, and both differ in shape from surveillance cameras (as shown in Figure 7). The 

three types of cameras have different functions and tend to be used in different contexts.

Figure 7: From left to right, a photography camera, a web camera, and a surveillance camera

Finally, while 76 of the 86 study participants (or 88.37%) created a card sorting category for 

photography cameras (“Camera/traditional camera”), only 52 (60.47%) created a category 

for webcams (“Web/computer camera”). This indicates that when participants thought about 

cameras, the first thing that came to mind for most of them was a photography camera. It 

also seems to indicate that photography cameras were more easily recognizable to a larger 

number of participants than were web cameras. 

4.1.2 Non-image capture devices (not cameras)

Participants created a variety of categories with associated attributes for items in the NonCameras class, but all of these devices had one attribute in common: they lacked a lens. In 

fact, “no lens” was the attribute participants most frequently associated with device 

categories in this class. This is understandable, given that most participants appeared to think 

of a lens as the primary, defining attribute of a camera.

As with camera devices, participants considered other attributes when trying to identify or 

confirm the nature of the device they were examining. A number of participants considered 

certain kinds of screens/displays as an indicator that a particular device was a camera or not. 

If the device otherwise resembled a digital photography camera, some participants 

considered the lack of a screen or display to mean that it was something else. Finally, 
NISTIR 7921 Page 28 3/2013

participants frequently took the inclusion of a USB connector as an indication that a device 

was something other than a camera.

What is particularly interesting is that participants often noted two attributes that they also 

used to identify cameras: the shape of the device and any buttons on it. However, they 

believed these features suggested that a device was something other than a camera. For 

example, if the device was square and had “play,” “rewind,” and “fast-forward” buttons but 

no “record” button, it probably wasn’t a camera.

This indicates that while the presence or absence of a lens was – for our participants – the 

primary determinant of whether a device was or was not a camera, the shape of the device 

and the buttons on it were also important indicators.

However, participants were not as successful at correctly identifying non-cameras as they 

were at identifying cameras: they had a success rate of only 68.05%, as opposed to a success 

rate of 88.69% when positively identifying cameras. In fact they mistakenly identified nonimage capture devices as cameras 31.95% of the time. Device Ref 49 and Ref 50, shown in 

Figure 8 below, are examples of non-image capture devices that participants often mistook 

for cameras (62.79% and 75.58% of the time, respectively). This is because they each had a 

circular feature that could easily be mistaken for a lens and a shape typical of a point-andshoot camera device.

Figure 8: Non-image capture devices that participants frequently mistook for cameras
NISTIR 7921 Page 29 3/2013

4.1.3 Phones

Participants typically placed cellular phone devices into a category unto themselves, labeled 

either “Cell phone” or “Smart phone.” Most participants associated some combination of a 

few distinct attributes with cell phones: most often they noted the buttons or number keypad, 

some kind of screen or display, the shape of the device, and a lens. Many participants made 

an observation along the lines of “most cell phones now have cameras in them.”

As previously mentioned, most participants placed phones in a category by themselves, even 

if they appeared to have (or were assumed to have) image capture capabilities. Some 

participants, however, placed phones (or rather, devices they considered to be phones) 

without a discernible lens into a category all by themselves. A few other participants, 

assuming that all mobile phones have image capture capabilities, placed them in the same 

category with cameras. This “lumping” of phones with cameras was more the exception than 

the rule.

4.1.4 Uncertainty (the “Miscellaneous” class)

The final class of devices participants categorized during the card sorting exercise was the 

Miscellaneous class. We placed in this class device categories that were vague or reflected 

participants’ ambiguity about the devices (e.g., “Unknown/no clue,” “Miscellaneous”).

Since participants were trying to distinguish between cameras and other devices, the first 

thing many of them did was look for a lens. The devices participants put into Miscellaneous 

categories often had no lens or – in some cases – had features that participants thought were 

“maybe” or “possibly” a lens. This is reflected in some of the category labels: “Could be a 

camera – possible lens,” “Appearance did not suggest camera – may have had a lens,” and 

“Maybe a camera – couldn’t find the lens.”

Participants also tried to identify the function of these devices based on their shape and any 

visible buttons, but found these features so unfamiliar or confusing that they could not even 

attempt an educated guess. 

4.1.5 Primary camera attributes

Our findings strongly indicate that when our participants were presented with visually 

uncluttered images of various devices in a quiet, non-stressful environment, they were very 
NISTIR 7921 Page 30 3/2013

good at positively identifying cameras. Overall, they would look for a discernible lens on the 

device they were examining: if it had no lens, it could not be a camera. Even if a device’s 

other attributes (such as its shape and visible buttons) suggested that it might be a camera, 

participants were extremely reluctant to identify it as such without a lens.

Although the lens was the defining attribute of a camera, participants generally looked at the 

shapes of and visible buttons on devices as well when examining them. They used the 

specific nature and details of these features to try and identify the specific function and 

purpose of the devices they examined. This is an important consideration, because if a device 

appears to have a lens, its shape and buttons can serve to confirm whether it is a camera or 

something else (e.g., a cell phone). 

4.2 Issues affecting the study

This study is part of a larger effort to develop recommendations and best practices the 

Department of Homeland Security can implement to make the US-VISIT biometric 

collection process more usable for both foreign nationals who undergo it and the customs 

officers who oversee it. Our study could not perfectly replicate the user population (foreign 

nationals), environment (customs at US ports of entry), or circumstances of the actual USVISIT process. We address the dissimilarities between the study and real-world operational 

environment in the following subsections.

4.2.1 Environment and circumstances

Participants in this study performed their assigned tasks in a relatively quiet, low-stress 

environment. The actual environments in which US-VISIT cameras are deployed are 

considerably noisier and more cluttered, with a larger number of distractions to contend with 

than in a controlled laboratory environment. Foreign nationals undergoing the US-VISIT 

process may also suffer from travel fatigue, time pressure, and other stresses that affect their 

performance during the US-VISIT process – including how they perform when identifying 

the camera that will capture their facial images.

In addition, our study population may have been primed for their task by certain words used 

in the instruction sets, an influence that is not present in the experience of foreign nationals 

in real operational environments.
NISTIR 7921 Page 31 3/2013

Finally, if our study participants provide any reliable indication, foreign nationals may 

perceive having their picture taken for US-VISIT differently than they do having their 

picture taken for other identification purposes. While more than half of our study participants 

(52, or 64.47%) said that they had had their biometric data captured in the past, only 8 

(9.30%) reported having had their facial biometrics captured. This is a somewhat unexpected 

result, as most adults in the United State possess some form of picture identification that is 

kept on record with a government agency or place of employment (e.g., driver’s license, 

passport, employer facility ID). This discrepancy may be due to a semantic issue: it is 

possible that participants did not perceive their identification pictures as “biometrics.” 

4.2.2 Demographics

DHS collects and publishes demographic data on visiting foreign nationals. We use their data 

from 2009 (see Appendix E: Where are Visitors to the US Coming From?) as a basis for 

comparison between our study population and the population of individuals who undergo the 

US-VISIT process at ports of entry. While both populations are composed of almost equal 

numbers of males and females, they are dissimilar in other respects, such as age and 

(especially) individuals’ countries of origin.

According to DHS, 60% of all non-resident admissions in 2009 were of people aged 25 

through 54: another 21% of admissions were of people aged 55 or over. In our study 

population, 82.5% of participants were between 20 and 54 years of age (inclusive), and only 

17.5% of our participants were 55 or older. Our population was, on average, younger than 

that of foreign visitors to the US. 

As one might suspect, our study participants’ countries of origin (shown in Table 1) did not 

reflect that of visitors to the US – most of our participants were American, while foreign 

visitors, by definition, are not. DHS statistics indicate that “top visiting countries” in 2009 –

those from whom the greatest number of foreign visitors to the US originated – were the 

Mexico, United Kingdom, Japan, Germany, France, Italy, Brazil, Australia, the Netherlands, 

and Spain.
NISTIR 7921 Page 32 3/2013

4.2.3 Technology exposure

By combining the aforementioned DHS data with technology adoption data from the World 

Bank (see Appendix F: Technology Adoption by Country), we can compare visiting foreign 

nationals’ average level of technology exposure with that of the study population.

Most people from the “top visiting countries” (listed in Section 4.2.2) are on par with or 

better than individuals in the United States in terms of their access and exposure to 

technology (e.g., personal computers, the Internet, or telecom technology). However, 

residents of most other countries around the world have less access to personal computers 

and the Internet than a typical individual in the US does. Mobile phones are an exception: 

they have achieved a higher rate of technological penetration worldwide than personal 

computers or other telecommunications technologies (see the table in Appendix F: 

Technology Adoption by Country). We can gather from this that the vast majority of foreign 

visitors to the US would be able to recognize a mobile phone as easily as our study 

participants did, but may not be (on average) as familiar with web cameras.

5 CONCLUSION

This study explored whether participants could discern image capture devices from other 

types of technology and the attributes they relied upon to make that distinction. In a 

controlled environment, we presented participants with 50 images of small (hand-held) 

devices and asked participants to indicate whether or not a given device was a camera. We 

then asked participants to group the devices into 2 to 5 categories and list the attributes they 

had used as a basis for assigning devices to each group. 

We did not attempt to simulate the environment or context in which US-VISIT cameras are 

normally used – specifically, a busy port of entry, many stations with various pieces of 

equipment, a list of instructions for the exit procedure, and the time pressures and other 

stresses of travel. However, if individuals in a lower-stress environment were not able to 

identify image capture devices, it is unlikely that people in a more stressful situation will be 

able to do so.

Participants in our study were highly successful at correctly identifying image capture 

devices, but were somewhat less successful at correctly identifying non-image capture 
NISTIR 7921 Page 33 3/2013

devices. Most of the participants indicated that, when trying to determine whether a 

particular device was a camera, the first thing they would do was look for a lens. This 

assertion was borne out in the device categorizing exercise: participants tended to categorize 

devices as cameras if they had a discernible lens, and as something else if they did not have a 

lens. An exception was cell phones, which – as many participants observed – have cameras 

in them. Some participants put cell phones in the same category with cameras, but most put

them in a separate category.

Participants also considered a device’s shape and visible buttons. They used these attributes 

to try and draw inferences about the specific function of the device, whether they believed it 

was a camera or not. Unfamiliar shapes or buttons seemed to confuse participants to the point 

where they could not determine a device’s functionality.

This leads us to conclude that most individuals can easily recognize an image capture device 

if it has a prominent lens, but other features of the device are important as well. People will 

look closely at the shape of and controls on a device in order to determine its function. Also, 

the “secondary” attributes of a camera (such as its shape, buttons, and a flash) may make it 

especially distinctive, which is important in a high-stress environment where many 

distractions are present. A US-VISIT camera that strongly resembles a traditional 

photography camera – generally rectangular with a round, projecting lens and a flash –

should be not only easily recognizable to most people, but unmistakable. 
NISTIR 7921 Page 34 3/2013

6 REFERENCES

[1] EconStats, (2012). Personal computers (per 100 people). Data retrieved April 25, 

2012 from EconStats database. Retrieved from website: 

http://www.econstats.com/wdi/wdiv_597.htm

[2] Monger, R. and Barr, M. (2010, April) Nonimmigrant Admissions to the United 

States: 2009, Annual Flow Report, Office of Immigration Statistics. Retrieved from 

website: http://www.dhs.gov/xlibrary/assets/statistics/publications/ni_fr_2009.pdf

[3] Nadel, L. (2007, November). Approaches to Face Image Capture at US-VISIT Ports 

of Entry. Paper presented at NIST biometric quality workshop II, Gaithersburg, MD. 

Retrieved from 

http://biometrics.nist.gov/cs_links/quality/workshopII/proc/nadel_Approaches_to_Fa

ce_Image_Capture_at_US-VISIT_POEs.pdf 

[4] Theofanos, M., Stanton, B., Sheppard, C., Micheals, R., Libert, J., & Orandi, S. US 

Department of Commerce, Technology Administration, National Institute of 

Standards and Technology. (2008). Assessing face acquisition (NISTIR 7540). 

Retrieved from National Institute of Standards and Technology website: 

http://zing.ncsl.nist.gov/bioUSa/docs/face_IR-7540.pdf

[5] US Department of Homeland Security, (2009). Yearbook of immigration statistics: 

2009, supplemental table 1. Retrieved from website: 

http://www.dhs.gov/xlibrary/assets/statistics/yearbook/2009/nimsuptable1d.xls

[6] World Bank. (2012). Data retrieved April 20, 2012 from World Development 

Indicators Online (WDI) database. Retrieved from website: 

http://data.worldbank.org/data-catalog/world-development-indicators
NISTIR 7921 Page 35 3/2013

APPENDIX A: DEMOGRAPHIC QUESTIONS

Demographic Questions

1. Age: ____________

2. Gender: (circle one) male female

3. Ethnicity: _______________________________________

4. Profession: ______________________________________________ 

5. Have you ever had your biometrics captured before? (circle one) yes no 

If yes check all that apply:

___ Fingerprinted with ink/paper

___ Fingerprinted electronically

___ Palm Print

___ Eye Scan

___ Face Image 

___ Voice 

___ Hand geometry 
APPENDIX B: DEVICE IMAGES USED IN THE 

STUDY

Image Capture Devices

Ref # 1 Ref # 2 Ref # 3 Ref # 4 Ref # 5

Ref # 6 Ref # 7 Ref # 8 Ref # 9 Ref # 10

Ref # 11 Ref # 12 Ref # 13 Ref # 14 Ref # 15

Ref # 16 Ref # 17 Ref # 18 Ref # 19 Ref # 20

Ref # 21 Ref # 22 Ref # 23 Ref # 24 Ref # 25
Ref # 26 Ref # 27 Ref # 28 Ref # 29

Non-Image Capture Devices

Ref # 30 Ref # 31 Ref # 32 Ref # 33 Ref # 34

Ref # 35 Ref # 36 Ref # 37 Ref # 38 Ref # 39

Ref # 40 Ref # 41 Ref # 42 Ref # 43 Ref # 44

Ref # 45 Ref # 46 Ref # 47 Ref # 48 Ref # 49
Ref # 50
APPENDIX C: DATA COLLECTION SHEET

 Cameras identified Participant id: __________________

Researcher observations Participant quotes 

Questions

Comments 

Behaviors 

 Sorting complete 

Categories

1. _________________________

2. _________________________

3. _________________________

4. _________________________

5. _________________________

Attributes of categories

1 2 3 4 5

 Camera data complete
___ Has used at least one camera from the following:

____ point-and-shoot ____ video camera

____ SLR ____ uses phone as camera 

____ web cam ____ No camera or doesn't take picture
APPENDIX D: CAMERA STUDY SCRIPT

Arrival, getting started

[When the participant arrives, she’ll sign in at reception.]

Researcher introduces the session

[Researcher greets participant, escorts participant to the testing room.]

[Say:] Okay, please come this way. Have a seat.

Thank you for agreeing to participate in this study. Your participation in the study is 

confidential. The data you provide will not have your name on it.

[Have Informed Consent form available.]

Before we get started, we have some paperwork to do. This is an Informed Consent Form 

for the first part of the session. [Hand to participant.] Please read it. It explains:

• What we are studying

• What you will do

• How the information will be treated

• And so on.

Please read both sides.

When you have finished reading it, if you are comfortable with what it says, please sign it. 

[Wait while participant reads and signs.]

Do you have any questions at this point?

I’ll sign here now. My signature just says that I saw you read and sign the form.

Let’s move over here now. [Researcher settles participant in front of the PC with the 

images on it.]

[Researcher guides participants to demographic questionnaire.]

Have a seat in front of the computer and I’ll get us to a place where we can start. OK. For 

Subject Number, put in the number on your tag.

Demographic survey

Now, please fill in the questionnaire on the screen.

[Participant answers demographic questionnaire.]

(See Appendix A: Demographic Questions for contents of demographic questionnaire.)

Camera experiment
[Ideally, the participant will be settled at the PC, having filled in the demographic 

questionnaire.]

Researcher introduces camera affordances portion

[Researcher instructs participants to continue at the PC to identify images that are (and are 

not) cameras.]

[Say:]

Now we want to learn from you what makes something a camera. After you click Start, 

this PC will show you an image. You must answer the question at the bottom of the screen 

as quickly as possible to go to the next image. There will be 50 images. Answer the 

question for each image you are shown, again, as quickly as you can. When you’re done, 

please tell me.

[Participant identifies cameras]

 Cameras identified 

Researcher observations Participant quotes 
Questions

Comments 

Behaviors 

Image sorting

[Researcher explains sorting exercise.]

Thank you. Now I have another way for you to look at the images. Here is a stack of the 

same images you just looked at, on paper. Please sort the images into categories. You can 

have up to 5 categories, but you don’t have to have that many. Ready?

You have 5 minutes. Go ahead.

[Researcher gives participant decks of images to sort.]

[Participant sorts deck into 3-5 categories.]

[Researcher instructs participant to write a name for each category.]

Now, please use a sticky and write a name for each category.

[Participant names each category.]

[If participant does not sort based on camera attributes, say:] Thanks for doing that. 

Remember the last exercise where you answered whether the image you saw was a 

camera?

When you looked at those images, how did you decide what was a camera and what 

wasn’t?

What would you say are the three main things that make something a camera?

[TIME: If the participant is over 3 minutes and it appears that she’s going to take much 

longer, remind her at about 4:00 that she has 1:00 left. Say:]

You have one more minute.

[Researcher interviews participant about top 3 attributes of each category.]

Thank you for doing that. Please tell me about how you decided on the categories.

 Sorting complete 

Categories

1. _________________________
2. _________________________

3. _________________________

4. _________________________

5. _________________________

Attributes of categories

1 2 3 4 5

 Camera data complete

[If necessary, ask:] What are the three main things that distinguish each category?

[Researcher interviews participant about experiences related to cameras.]

Tell me about your experiences with cameras before today.

___ Has used at least one camera from the following:

____ point-and-shoot ____ video camera

____ SLR ____ uses phone as camera 

____ web cam ____ No camera or doesn't take picture

Thank you for that.

[Researcher interviews participant about how the participant approached the camera 

identification task.]

Thinking back to the task you completed on the computer with the 50 images, I am 

interested in knowing how you decided which images were of cameras and which were 

not cameras.

 Experience data complete

Great. Thank you for your participation.

[Researcher escorts the participant to reception.]
APPENDIX E: WHERE ARE VISITORS TO THE US

COMING FROM?

DHS has many classifications of “nonimmigrant,” including temporary visitors, aliens in 

transit, students, temporary workers, and exchange visitors. For purposes of this study, we 

focus on the non-resident visitors, which comprised 89.8% of the admissions in 2009, as 

reported by DHS [2]. These non-resident admissions were individuals who came to the US 

for a relatively brief period. Unknown (0.5%), e.g., not classified, short-term and long-term 

resident alien admission figures comprise 10.2% of the total admissions and are not 

discussed here.

In 2009, DHS recorded 32,544,098 admissions of temporary visitors, e.g., non-resident, 

nonimmigrant admissions.4 27,800,027 (or 85.4%) of those temporary visitors said they were 

visiting for pleasure, 4,390,888 (or 13.5%) said they were visiting for business, 346,695 (or 

1.1%) were classified as transient aliens, and the remaining 6,488 (or 0.1%) were listed as 

commuter students.

Of all non-resident admissions, 60% were of people aged 25-54. People age 55 and older 

made up another 21% of admissions. Temporary visitors were almost evenly split between 

males and females.

Nearly half of all admissions for non-residents came primarily from the following countries: 

Mexico 19.0%

United Kingdom 13.8%

Japan 9.6%

Germany 5.8%

Total 48.2%

The remaining admissions came from these countries: 

 4 Note that “admissions” refers to events, not people: some individuals could have been admitted to the US 

multiple times during 2009.
France 4.8%

Italy 3.0%

Brazil 2.7%

Australia 2.4%

Netherlands 2.2%

Spain 2.2%

Other 33.9%

Unknown 0.6%

Total 51.8%

Note that roughly a third of admissions are from the broad “Other” category. We have 

narrowed that category down to all the countries of origin from which DHS reported more 

than 100,000 admissions each in 2009. These 23 countries, and the number of admissions 

from each, are listed below [5]:

India 574,568

China 512,386

Venezuela 480,623

Colombia 456,778

South Korea 410,904

Argentina 355,504

Israel 323,523

Bahamas 265,075

Dominican Republic 231,861

Jamaica 216,978

Taiwan 201,126

Philippines 200,884
Guatemala 192,281

Peru 181,164

Ecuador 163,440

Costa Rica 165,330

Trinidad and Tobago 159,633

Russia 146,985

Poland 133,591

El Salvador 133,453

Chile 130,367

Honduras 124,777

Panama 103,330
APPENDIX F: TECHNOLOGY ADOPTION BY 

COUNTRY

The World Bank and the International Telecommunications Union track and publish 

statistics on rates of technology adoption in countries around the world [1][6].5 For our 

purposes, we are interested in three categories of technology adoption: personal computer 

ownership, Internet use, and mobile phone subscriptions. 

 5 Statistics on Internet use and mobile phone subscriptions comes directly from the World Bank’s World 

Development Indicators (WDI) database. Statistics on personal computer ownership come from the 

EconStatsTM database and were aggregated from World Bank and International Telecommunications Union 

data.
Table 7 below displays the number of each of these items (per 100 people) in the US and the 

15 countries from which the most temporary admissions to the US originate (see Appendix 

E: Where are Visitors to the US Coming From?). Using this data, we can draw some 

inferences about foreign visitors’ average level of exposure to technology, and how their 

level of exposure compares to that of our study population.

Some countries on the list are comparable to the US in certain categories of technology 

adoption (e.g., personal computer ownership). In such cases, the relevant cell is colored blue. 

Where a country’s level of technology adoption exceeds the US in a certain category, the 

relevant cell is colored green.
Table 7: Technology adoption statistics for the US and top 15 visiting countries

Country

2009 

Temporary

Admissions

Number of users per 100 residents

Personal 

Computers Internet Users Mobile Phone Subscribers

US 80.66 74.2 90.2

Mexico 6,168,774 14.4 31.1 80.6

United Kingdom 4,504,786 80.2 84.7 130.3

Japan 3,138,650 - 77.6 94.7

Germany 1,881,944 65.6 82.5 127.9

France 1,563,993 65.2 77.5 97.4

Italy 981,715 36.7 53.7 149.8

Brazil 869,310 16.1 40.7 104.1

Australia 775,885 - 75.9 100.9

Netherlands 715,023 91.2 90.7 115.4

Spain 713,310 39.3 65.8 112.0

India 574,568 3.37 7.5 61.4

China 512,386 5.7 34.4 64.2

Venezuela 480,623 9.38 35.9 96.7

Colombia 456,778 11.29 36.5 96.1

South Korea 410,904 57.6 82.5 103.9

 6 2006 data

7 2007 data

8 2005 data

9 2008 data
Only the Netherlands surpasses the US in personal computer, Internet, and phone use. Only 

the United Kingdom is on par with the US in terms of personal computer ownership, but it 

and a number of other countries – Japan, Germany, France, Australia, and South Korea –

surpass the US in Internet users. Finally, most countries on the list above surpass the US in 

terms of mobile phone subscriptions, except for Mexico, India, and China.