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Evaluating Protection Capability for
Visual Privacy Information
Yuta Nakashima, Member, IEEE, Tomoaki Ikeno, and Noboru Babaguchi, Senior Member, IEEE

Abstract
Images are now widely used for various applications like surveillance, entertainment, and communication. They
are often accompanied by sensitive information such as people’s faces, which can be privacy intrusive. The essential
remedy is to remove such information using an image-processing technique (e.g., blurring, blocking out). The
effectiveness of removal, however, highly depends on the relationship between viewers and subjects or on theconspicuousness of subjects; viewers might successfully guess subjects’ identities from privacy-protected images if
the viewers are familiar with the subjects or if the subjects are conspicuous. Through an extensive questionnaire
survey, we evaluated the capability of image processing techniques for visual privacy protection, considering thesefactors.
Index Terms
Privacy protection, image processing, evaluation.

I. I NTRODUCTION
Images captured with cameras are currently viewed as one of the most important media for wide range ofapplications, including surveillance, entertainment, and communication. Such images usually contain people’s facesthat can disclose their identities and thus are highly privacy sensitive. To remedy this privacy concern, television
programs, for example, apply image processing (e.g., blurring, blocking out) to filter out sensitive information andavoid violating others’ privacy. Recent computer vision technologies enable automatic privacy protection for some
applications like video surveillance.
As cameras become more ubiquitous and images become more available through various ways, the chance of suchsensitive information being exposed to many people including ones close to the subjects increases. For example, avast amount of images are now shared through the Internet, which makes these images available to the subjects’friends and family. Some attentive users, therefore, may apply privacy protection to the images manually or with
an automatic system available in YouTube [1], for example. Google Street View and video surveillance in a local
community (e.g., in a building) are in a similar situation, and privacy protection has been studied for them (e.g.,[2]).
The closeness between subjects and viewers can be a critical factor in privacy protection. Considerable priorknowledge on subjects’ appearances may make inference of their identities far easier for viewers who are close tothem, even if the image is partly filtered. For example, blurring filters out high-frequency components of the image,but sufficient cues for identification might still remain [3]. In addition, for automatic privacy protection, such as[4], [5], computer vision technologies might fail to precisely locate faces and/or bodies, and blocking out might
not fully cover sensitive regions. Those who are close to the subjects can successfully guess their identity fromsuch small uncovered regions. Yip and Sinha demonstrated that humans can recognize celebrities’ faces in very
low-resolution images [6]. Burton et al. showed that faces are more important cues for identification in surveillance
videos than gaits or bodies, and concluded that familiar faces, but not unfamiliar ones, can be easily identified [7].
These results imply a negative correlation between familiarity and the capability of visual privacy protection. A
good summary of these results can be found in [8].
This raises a crucial question: How well does visual privacy protection work when a subject and viewer sharea certain degree of familiarity? We tried to answer this question based on a survey of over 100 participants. This
This work was partly supported by the Japan Society for the Promotion of Science (JSPS) Grant-in-Aid for Scientific Research.
Y. Nakashima is with the Graduate School of Information Science, Nara Institute of Science Technology, Japan.
E-mail: n-yuta@is.naist.jp
N. Babaguchi is with the Graduate School of Engineering, Osaka University, Japan.

2Fig. 1. Specification of face image.

article focuses on visual privacy protection for facial regions using image-processing techniques often used in
automatic privacy protection systems. That is, considering several parameters for each image-processing techniqueor for possible failures in computer vision technologies, we evaluated the capability of image-processing techniques
for privacy protection, and sought to clarify the relationship between capability and familiarity. This article is anextended version of our previous work [9], and we added results of statistical tests for clearer interpretation of thesurvey and some discussion on the factors that affects the privacy protection capability.
II. I MAGE PROCESSING TECHNIQUES TO BE EVALUATED
In our survey, we targeted several image-processing techniques that are widely used for visual privacy protection.
• Resizing. Many computer vision-based privacy protection systems locate facial regions using backgroundsubtraction or face detection [4]. As these technologies are not always perfect, localization might fail, thereby
fully disclosing facial regions. In this case, the degree of privacy violation depends on the subject’s size,
direction, and so forth. Among these factors, resizing corresponds to manipulating the subject’s size. Here, we
employed bilinear interpolation, and parameter s ∈ {5, 10, 15, 20, 25, 30, 40, 50, 70, 100} specified the widthof the resized image.
• Blocking out facial regions. Blocking out facial regions covers facial regions with a single color. In an
automatic privacy protection scenario, computer vision technologies might fail in locating facial regions
precisely, resulting in only a partial block out. We sought to demonstrate the accuracy required from thecomputer vision technologies to adequately locate and block out faces. Letting wF and hF denote the widthand height of the facial region, defined as in Fig. 1, the parameters in this evaluation were vertical shiftnαwF and horizontal shift nαhF , where n ∈ {−3, −2, −1, 0, 1, 2, 3} and α = 0.125. Diagonal shifts (top-leftto bottom-right and top-right to bottom-left) are the combinations of horizontal and vertical shifts. Figure 2
shows an example facial images after being blocked out.
• Blocking out eye regions. Eyes and their surrounding regions are considered one of the most important cues
for identification among facial features [10]. Thus, we evaluated the eye-blocking strategy in the same way as
the face-blocking strategy. The parameters were vertical shift nβwE and horizontal shift nγhE , where β = 0.15
and γ = 0.25, of which examples are shown in Fig. 3. We used the same n as for facial regions.
• Blurring. Blurring is one of the most common image-processing techniques used for visual privacy protection(e.g. [11]). Blurring removes high-frequency components of images by applying a smoothing filter. Thisevaluation employed the Gaussian filter with parameter σ ∈ {1, 1.6, 2.2, 2.8, 3.4, 4}, which is the standard
deviation of the Gaussian function and controls the extent of high frequency component removal. Example
images are shown in Fig. 4.
III. Q UESTIONNAIRE
The degree of privacy violation, or the capability of privacy protection, depends on various factors. Among them,our main focus was (i) familiarity between viewers and subjects and (ii) conspicuousness of subjects, both of whichwere highly subjective factors and determined here using a questionnaire.

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Fig. 2. Example face images for blocking out facial regions. The value of n increases from left to right.

Fig. 3. Example face images for blocking out eye regions. The value of n increases from left to right.

Fig. 4. Example face images for blurring.

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TABLE ID ISTRIBUTION OF PARTICIPANTS BY AGE AND GENDER .

malefemale

20’s
12
14

30’s
14
13

40’s
14
14

50’s
13
14

Familiarity. As demonstrated in previous research (e.g., [6]), familiarity has considerable effects on ouridentification abilities. For example, the rate of correct identification of a blurred face is expected to behigh when viewers are familiar with the subject. In this evaluation, we assessed familiarity on a 5-point Likert
scale. Our questionnaire asked each subject if she/he knew the person, possibly through a television program,and provided textual descriptions as follows: “I have never seen this person before” for 1, “I have seen thisperson” for 3, and “I’m very familiar with this person” for 5. The original descriptions were in Japanese.
• Conspicuousness. Another important factor that can affect the degree of privacy violation is the extent to which
the subject is conspicuous. For example, a subject with pink-colored hair is very conspicuous, and might beeasily identified even by viewers who are not very familiar with the subject. This factor, which we referred
to as conspicuousness, was also assessed on a 5-point Likert scale. Each subject was asked the question, “doyou think the person is conspicuous (impressive hair style, facial features, etc., or easily memorable),” with
textual descriptions as follows: “Completely inconspicuous” for 1, “Neutral” for 3, and “Very conspicuous”
for 5.
One of the difficulties in this evaluation is how to measure privacy violation, or how to measure the capability ofprivacy protection, because the sense of privacy is highly subjective, varying from person to person and culture toculture. Thus, we required an objective criterion. For face recognition research in psychology, researchers often use
the rate of successful subject identification. Accordingly, we defined a similar criterion that we called identifiability.
Identifiability, with respect to each score for familiarity and conspicuousness, was evaluated as follows. First, wecollected facial images of subjects of various ages and genders to build a facial image dataset. In this survey, subjectsof varying familiarity and conspicuousness levels were required. Some research work used images of celebrities(e.g., [6]), but we consider that this is not appropriate for our survey because of (i) the right of publicity and (ii)difficulty in preliminarily selecting a set of celebrities that makes distributions of familiarity and conspicuousness
levels close to uniform distributions. We therefore used images of Japanese politicians on official business, whoseright of publicity is considered restricted. In addition, some famous politicians (e.g., Junichiro Koizumi, who is aformer prime minister of Japan) are known to most Japanese, and we can easily find a set of politicians suitablefor our survey. The images were collected from the Internet and appropriately cropped to fit to our facial image
specifications. The number of subjects was 20 (10 males and 10 females), with 20 images for each subject.
The questionnaire sessions were facilitated by a research company, which has a list of potential survey participants
who preliminarily and voluntarily registered through the company’s website with their occupation, gender, age, etc.
In the pre-screening process, the company randomly picked people in the list so that their age and gender distributed
uniformly, asking if they can participate in our survey, and recruited some of them. Our participants were in their
20s through 50s, with 53 males and 55 females, 108 in total, and were expected to be familiar with PC because
they used one for registration to the list. Table I summarizes the distribution of the participants. We asked theparticipants to assign familiarity and conspicuousness scores to each of our 20 subjects, providing a facial image
from our dataset and the Likert scales.
Each participant was then exposed to a sequence of stimuli. Each stimulus was a facial image that was randomly
selected from our dataset, excluding the image used for evaluating familiarity and conspicuousness. An imageprocessing technique was then applied to the selected facial image with one of the parameter values. For eachstimulus, we presented nine facial images, called candidates, one of which contained the subject in the stimulus(but not the same facial image as the stimulus), and the other contained eight randomly selected subjects. Eachparticipant was asked to select the subject in the stimulus from the candidates or answer “I dont know.” Figure 5
shows our PC-based interface, with the stimulus in the right pane and candidates with the “I don’t know” buttonin the left pane. For this survey, each participant provided 270 responses (plus 10 responses for another survey),which took about one hour in total.
•

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Candidates

Stimulus“I donʼt know”
ButtonFig. 5. PC-based interface used in our survey. Note that the facial images shown in this example are not from our dataset.

After the questionnaire sessions, we aggregated all responses. Let f and c in {1, 2, 3, 4, 5} denote the familiarity
and the conspicuousness scores, respectively. For a certain image-processing technique, g , with one of its parameters
θ, suppose we have Mfg (θ) responses for all subjects with familiarity score f , and Lgf (θ) responses correctly
identified the subject in the stimulus among them. We define the identifiability Ifg (θ) for f and g with θ as
Ifg (θ) = Lgf (θ)/Mfg (θ).

(1)

Similarly, the identifiability for the conspicuousness score c is defined as
Icg (θ) = Lgc (θ)/Mcg (θ).

(2)

Given these responses, we statistically test if faces are identifiable after image processing. Let us assume theidentifiability value of a certain type of visual privacy protection with a value of its parameter is constant regardlessof participants. Under this assumption, the number of correct responses L, either Lgf (θ) or Lgc (θ), follows the
binomial distribution given the number of all responses. Letting M be the number of all responses, i.e., either
Mfg (θ) or Mcg (θ), the probability of L is computed by!

Pr(L|M, µ) =M L
µ (1 − µ)M −L ,L

(3)

where µ is the probability of a correct response.
In other words, µ is the true identification value for the populationMfrom which the participants are selected. L is the binomial coefficient, which gives the number of combinations of
L responses from M responses. This equation enables us to compute the probability of L given M and µ. Using thisprobability, we can compute the so-called p-value for the one-tailed binomial test, where our null hypothesis is thatthe obtained identifiability value is µ. Under this null hypothesis, we can verify whether the obtained identifiability
value is smaller than µ.
So what is µ? In this survey, each question provided participants with ten options. If participants randomly choose
one of those ten options, 10% of responses identified correct subjects. If they randomly choose one out of ninecandidates (but not “I don’t know”), approximately 11% (i.e., 1/9) of trials are correct. From this observation, we
consider that the identifiability value of 1/10 is a reasonable choice for judging whether visual privacy is protectedby the given image processing technique. Therefore, our null hypothesis is that the identifiability value of visual
privacy protection is 1/10 given the score for one of the two factors, and its alternative is that the identifiability
value is larger than 1/10.

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Fig. 6. Distributions of familiarity and conspicuousness scores.

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Image widthFig. 7. Identifiability for resizing with respect to familiarity (left) and conspicuousness (right). Points with a circle are parameter values that
rejected our null hypothesis. Image width is specified by parameter s.

IV. R ESULTS
On the questionnaire for evaluating familiarity and conspicuousness, 108 participants assigned scores to all 20
subjects, which resulted in 2160 total responses. Figure 6 shows the distributions of scores. The distribution forfamiliarity was concentrated in scores 1, 3, and 5. This indicates that participants felt no need for intermediate
scores to represent their familiarity. In contrast, conspicuousness resulted in an almost uniform distribution.
A. Resizing
Figure 7 shows the identifiability values for resized facial images with respect to familiarity and conspicuousness,
respectively. Both curves spike from around s = 15. These identifiability values seem slightly lower than thosereported in [6]. One of the main factors might be the difference in the definitions for identification (choosing
one from a set of given candidates in our survey vs. assigning a subject’s name to a facial image based on theparticipant’s memory) and subjects used (politicians vs. celebrities). Our results indicate that identifiability shows
ceiling effects around s = 50 but does not reach 1 even for s = 100, because our dataset contained subjects who
resemble each other in their facial images. For lower familiarity and conspicuousness scores, the variation among
facial images of one subject maintained low identifiability. Even with the image size of 100 × 100 pixels, theidentifiability values for score 1 in both factors are around 0.8, which means the subjects with low familiarity orconspicuousness could not be accurately identified consistently. This result also acts as a baseline of identifiability
for each familiarity and conspicuousness score, as parameter value s = 100 is the original facial image size in ourdataset.
B. Blocking out facial regions
Identifiability for blocking out on facial regions is shown in Fig. 8. Interestingly, comparing horizontal and verticalshifts, the identifiability for the horizontal shift is almost symmetric with respect to n and increases suddenly when|n| ≥ 2, while that for the vertical shift is less symmetric and changes more gradually. The low identifiability values
for the vertical shift when n is larger are because our vertical shift only disclosed a subject’s forehead even for the71
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Fig. 8. Identifiability for blocking out facial regions with respect to familiarity (left) and conspicuousness (right). Amount of shift (horizontalaxis) is specified by parameter n.

largest n. The relatively low identifiability for n = −3 in the vertical shift indicates that subjects’ mouth regions
are not very informative cues compared to features around the eyes. Identifiability values even for n = 0 do notreach zero and increase with familiarity or conspicuousness scores, which may imply that participants could guess
identities based on the subject’s clothes.
C. Blocking out eye regions
Figure 9 shows the results for blocking out eye regions. Although this image-processing technique is sometimes
adopted in some applications, it hardly affected privacy protection capability in our survey. Even for the lowest
familiarity and conspicuousness scores, identifiability remains over 0.6 for most cases. A comparison between
horizontal and vertical shifts demonstrates that the difference in shift direction almost does not affect the results.
It is noteworthy, however, that the difference in conspicuousness appears to yield larger gaps in identifiability than

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Fig. 9. Identifiability for blocking out eye regions with respect to familiarity (left) and conspicuousness (right). Amount of shift is specified
by parameter n. No parameter value rejected the null hypothesis.

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Fig. 10. Identifiability for blurring with respect to familiarity (left) and conspicuousness (right). Parameter for Gaussian filter is specified
by σ. Our null hypothesis was rejected only when parameter σ is 4 and a conspicuousness score is 1.

does familiarity.
D. Blurring
The parameter σ for blurring almost linearly decreases identifiability, as shown in Fig. 10. Although blurring
is a widely used image processing technique, its privacy protection capability is not ideal. Subjects with highest
familiarity and conspicuousness scores obtained identifiability values around 0.6. Blurring also results in a substantial
separation among different conspicuousness scores.
V. D ISCUSSION
Here, we discuss the correlation between the two factors that affect identifiability and the statistical significanceof the results obtained in our survey.
A. Correlation between familiarity and conspicuousness
Before discussing the privacy protection capabilities of the image-processing techniques, we consider the relationship between familiarity and conspicuousness scores obtained in our survey. Observing the identifiability values
for each factor, we can hypothesize that familiarity and conspicuousness scores are highly correlated. To verify thisFamiliarity score

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Fig. 11. Conspicuousness-versus-familiarity histogram.

hypothesis, we generated the conspicuousness-versus-familiarity histogram (Fig. 11). This figure shows relatively
high numbers of responses in the diagonal elements, which indicates that many participants assigned the same
scores to familiarity and conspicuousness. This is significant for familiarity scores “1” and “5,” though the otherfamiliarity scores were relatively spread. In contrast, conspicuousness scores for the two extreme familiarity scoresappear distributed, but not as much for the other three familiarity scores. In sum, the familiarity and conspicuousness
scores appear to be strongly correlated, although responses with the two extreme familiarity scores are spread acrossdifferent conspicuousness scores.
This might support the wider separation of identifiability values between conspicuousness scores “1” and “5,”which is particularly noticeable for blocking out eye regions and blurring. Our evaluation method required participants to choose one face image out of ten options (nine candidates plus “I don’t know”) that appeared to contain thesame subject as the stimulus; thus, conspicuousness appears to more directly affect identifiability, and our survey
successfully captured this tendency.
B. Identifiability after image processing
In Figs. 7–10, points with a circle indicate that our null hypothesis is accepted for that parameters with asignificance level of 0.05. Other parameter values rejected the null hypothesis, indicating the identifiability value islarger than 1/10 and thus subjects were identifiable. Figure 7 implies that we must apply visual privacy protection
to face images larger than 10 × 10. This requirement might be slightly relaxed to 15 × 15 if we can assume thatthe subjects are not conspicuous or are complete strangers for expected viewers. From Fig. 8, acceptable errors
in automatically locating facial regions by computer vision technologies are, in most cases, less than 12.5% offace images. Even when blocking entire facial regions, viewers might accurately guess identities if the subjects areconspicuous. We might need to be more conservative about automatic visual privacy protection. Blocking out eye
regions is nearly useless; this technique hardly reduces identifiability values, as indicated in Fig. 9. Face images
after blurring, even with σ = 4, remain identifiable. For secure privacy protection, a blurring kernel at least larger
than σ = 4 is required.
The limitation of our survey is that the way of identifying subjects does not necessarily fit to our actual cognitive
process because facial images of candidates can provide prior information on possible subjects, which tends tofacilitate the identification. However, our results could bring a guideline to realize stricter protection of visual
privacy information.
VI. C ONCLUSION
This article discussed the relationship between visual privacy protection capabilities and two factors, familiarity
and conspicuousness. Our survey over more than 100 participants demonstrated that conspicuousness is a moresignificant factor than familiarity, although the difference is marginal. We also found that blocking out eye regions
and blurring are almost incapable of protecting privacy. The requirements for automatic visual privacy protection

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are drastic, such that computer vision technologies must find faces smaller than 15 × 15 and localization errors
must be smaller than 12.5% of the facial region. The results presented in this article are beneficial for designing
automatic privacy protection, and we hope this work helps to facilitate development in this field.
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surveillance,” in Proc. of Int’l Conf. on Image Processing (ICIP 2008), October 2008, pp. 1672–1675.
[3] C. Neustaedter, S. Greenberg, and M. Boyle, “Blur filtration fails to preserve privacy for home-based video conferencing,” ACM Trans.
Computer-Human Interaction, vol. 13, no. 1, pp. 1–36, 2006.
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[6] A. W. Yip and P. Sinha, “Contribution of color to face recognition,” Perception, vol. 31, no. 8, pp. 995–1003, 2002.
[7] A. M. Burton, S. Wilson, M. Cowan, and V. Bruce, “Face recognition in poor-quality video: Evidence from security surveillance,”
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[8] P. Sinha, B. Balas, Y. Ostrovsky, and R. Russell, “Face recognition by humans: nineteen results all computer vision researchers should
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[11] N. Babaguchi, T. Koshimizu, I. Umata, and T. Toriyama, “Psychological study for designing privacy protected video surveillancesystem: PriSurv,” in Protecting Privacy in Video Surveillance. Springer Verlag, 2009, pp. 147–164.

PLACE
PHOTO
HERE

Yuta Nakashima received the B.E. and M.E. degrees in communication engineering from Osaka University, Osaka,
Japan in 2006 and 2008, respectively, and the Ph.D. degree in engineering from Osaka University, Osaka, Japan, in
2012. He is currently an Assistant Professor at Graduate School of Information Science, Nara Institute of Science andTechnology (NAIST) and a visiting scholar at Carnegie Mellon University. His research interests include video content
analysis using probabilistic and statistical approaches. He is a member of the IEEE, the ACM, and the IEICE.
email: n-yuta@is.naist.jp

Tomoaki Ikeno received the B.E. from Osaka University, Osaka, Japan. He is currently with ALPS ELECTRIC CO.,LTD.
email: ikeno@nanase.comm.eng.osaka-u.ac.jp
PLACE
PHOTO
HERE

Noboru Babaguchi received the B.E., M.E., and Ph.D. degrees in communication engineering from Osaka University,
in 1979, 1981, and 1984, respectively. is currently a Professor of the Department of Information and Communications
Technology, Osaka University. From 1996 to 1997, he was a Visiting Scholar at the University of California, San
Diego. His research interests include image analysis, multimedia computing, and intelligent systems. He received Best
PLACE
Paper Award of 2006 Pacific-Rim Conference on Multimedia (PCM 2006), and Fifth International Conference on
PHOTOInformation Assurance and Security (IAS 2009). He is on the editorial board of Multimedia Tools and Applications,
HERE
Advances in Multimedia, and New Generation Computing. He served as a General Co-chair of the 14th International
MultiMedia Modeling Conference (MMM 2008), ACM Multimedia 2012, and so on. He has published over 200
journal and conference papers and several textbooks. He is a fellow of the IEICE, a senior member of the IEEE, anda member of the ACM, the IPSJ, the ITE and the JSA.
email: babaguchi@comm.eng.osaka-u.ac.jp