Calibration-free gaze sensing using saliency maps
Yusuke Sugano
The University of Tokyo
Tokyo, Japan, 153-8505

Yasuyuki Matsushita
Microsoft Research Asia
Beijing, P. R. China, 100080

Yoichi SatoThe University of Tokyo
Tokyo, Japan, 153-8505

sugano@iis.u-tokyo.ac.jp

yasumat@microsoft.com

ysato@iis.u-tokyo.ac.jp

Abstract
We propose a calibration-free gaze sensing method using
visual saliency maps. Our goal is to construct a gaze estimator only using eye images captured from a person watching a video clip. The key is treating saliency maps of thevideo frames as probability distributions of gaze points. To
efficiently identify gaze points from saliency maps, we aggregate saliency maps based on the similarity of eye appearances. We establish mapping between eye images togaze points by Gaussian process regression. The experimental result shows that the proposed method works wellwith different people and video clips and achieves 6 degreesof accuracy, which is useful for estimating a person’s attention on monitors.

Figure 1. Illustration of our method. Our method uses saliency
maps computed from video frames in bottom-up manner for automatically constructing a gaze estimator.

eyeball model for gaze estimation and performed automatic
calibration by fitting the model to appearance of a user’seye [19]. Sugano et al. proposed a method using input froma user’s mouse as exemplar data for calibration [17]. However, these approaches are restricted to specific scenarios.
Yamazoe et al.’s approach relies on a specific geometricmodel, and Sugano et al.’s approach can only be applied
to interactive environments with user inputs.
Apart from these gaze estimation studies, computationalmodels of visual saliency have been studied to analyze visual attention on an image. While gaze estimation approaches aim at determining where people’s eyes actually
look at, the visual saliency give us information about which
image region attracts more attention, as illustrated in Figure 1. Biologically, humans tend to gaze at an image region
with high saliency, i.e., a region containing more uniqueand distinctive visual features compared with the surrounding regions. Hence, by knowing the visual saliency mapof an image, the gaze point of a person looking at an image can be predicted. After Koch and Ullman proposed theoriginal concept [11] of visual saliency, many bottom-up
computational models of visual saliency maps have been
proposed [7, 15, 5, 4, 1]. It is experimentally shown that
there indeed exists a correlation between bottom-up visualsaliency and fixation locations [14].
Gaze estimation and visual saliency models are closely
related; nonetheless, not many previous studies relate thesetwo. Kienzle et al. [10, 9] proposed a method for learning
computational models of bottom-up visual saliency using
gaze estimation data. Judd et al. [8] followed the approach

1. Introduction
Gaze estimation is important for predicting human attention, and therefore can be used for various interactive
systems. There are a wide range of applications of gaze estimation including marketing analysis of online content anddigital signage, gaze-driven interactive displays, and manyother human-machine interfaces.
In general, gaze estimation is achieved by analyzing aperson’s eyes with an image sensor. Exact gaze points canbe determined by directly analyzing gaze directions fromobservations of eyes. Many implementations of camerabased gaze estimator have been proposed including commercial products (see [3] for a recent survey). One of
the limitations of camera-based gaze estimators is explicit
calibration for learning person-dependent parameters. Although the number of reference points for calibration canbe reduced using multiple light sources [18], or stereo cameras [12], it still requires a user to actively participate in
the calibration task. In some practical scenarios, the active
calibration is too restrictive because it interrupts natural interactions and makes the unnoticeable gaze estimation impossible.
To avoid active calibration, Yamazoe et al. used a simple
1

Figure 2. Proposed framework. Our method consists of three steps. Saliency extraction step computes saliency maps from the input video.
Saliency aggregation step combines saliency maps to produce gaze probability maps. Using the gaze probability maps and associated eye
images, the estimator construction step learns the mapping from an eye image to a gaze point.

with more features and a larger database. These approaches
learn accurate saliency models using gaze points. In contrast to these methods, our goal is to construct a gaze estimator from saliency maps. To our knowledge, this is the firstwork to use visual saliency as prior information for gazeestimation.
We propose a novel calibration-free gaze sensing method
using computational visual saliency. The key idea of our
method is generating a probability distribution of gazepoints using saliency maps. From eye images of a user
watching a video clip, we acquire learning datasets that consists of saliency maps and eye images under a fixed head
position. Gaze probability maps are generated by aggregating the saliency maps based on the similarity of eye appearances. Once the gaze probability maps are obtained, ourmethod learns the relationship between the gaze probability
maps and eye images. As a result, this leads to a completely
ambient gaze estimator that exempts users from active calibration.

2. Gaze estimation from saliency maps
Our goal is to construct a gaze estimator without any
explicit calibration stages. The inputs for our system are
N video frames {I1 , . . . , IN } and associated eye images{e1 , . . . , eN } of a person watching a video clip under afixed head position. In our setting, eye images and video
frames are synchronized; eye image ei is captured at thesame time when frame Ii is shown to the person. Using thisdataset {(I1 , e1 ), . . . , (IN , eN )}, our goal is to construct agaze estimator for estimating an unknown gaze point g froman eye image e.
Our method consists of three steps; saliency extraction,
saliency aggregation, and estimator construction as shownin Figure 2. Saliency extraction, is the step in whichsaliency maps from an input video are calculated. From thevideo clips, a visual saliency map that represents distinctive
visual features is extracted from each frame. Saliency aggregation combines all saliency maps to obtain a gaze probability map that has a peak around the true gaze point. This
step produces pairs of the average eye image and gaze probability map. The third step is the estimator construction.
Using the gaze probability maps and associated eye images,
the estimator construction step learns the mapping from an
eye image to a gaze point. The resulting gaze estimator outputs gaze points for any eye image from the person. Detailsof each step are described in the following sections.

2.1. Saliency extraction
This step extracts visual saliency maps {s1 , . . . , sN }from input video frames {I1 , . . . , IN }. We use graph-based
visual saliency [4] as a base saliency model. In our method,
we use commonly used feature channels, i.e., color, intensity, and orientations as static features, and flicker and motion are used as dynamic features. These features are detailed in, e.g., [6]. All feature maps are combined with thesame weight to form a low-level saliency map sl .
On top of the low-level saliency, we use a higher levelsaliency model. Humans tend to fixate on faces, especiallythe eyes, which are highly salient for humans. Cerf etal. [2] proposed a face channel-based saliency model usinga face detector. We follow this approach to produce reliable saliency maps using a facial feature detector (OKAO
Vision library developed by OMRON Corporation). The
face channel saliency map sf is modeled as 2-D Gaussian
circles with a fixed variance at the detected positions of thecenter between two eyes. When the detector detects only alikely to coincide with the true gaze point. Suppose we havea set of saliency maps that statistically have high saliency
scores around the true gaze point, with random saliency
scores at other regions. By aggregating the saliency maps, it
is expected that the image region around the true gaze point
has a vivid peak of saliency. The map can be used as theprobability distribution of the gaze point. This step aims at
producing such probability maps using the associated eye
images.
Our basic idea is to use a similarity of eye images for theaggregation. The similarity measure ws is defined as
ws (ei , ej ) = exp(−κs ||ei − ej ||2 ).

Figure 3. Examples of computed saliency maps. First row
shows input images I, second row shows corresponding low-levelsaliency sl , third row corresponds to face channel saliency sf , andthe bottom row shows combined saliency maps s.

face, e.g., when the face region is small, the facial saliency
is defined at the center of the facial region.
We combine the low-level saliency sl and face channel saliency sf after normalizing them to span in the samerange. We also take the temporal average of the aggregatedsaliency maps to compute the final saliency map s as
i
X
1
si =
(sl + sfj ),2(ns + 1) j=i−n j(1)swhere ns is the number of frames used for temporal smoothing. Since humans cannot instantly follow rapid scenechanges, only past frames are used for the smoothing to account for latency. As a result, synchronized pairs of saliency
maps and eye images Ds = {(s1 , e1 ), . . . , (sN , eN )} areproduced.
Figure 3 shows examples of the computed saliency maps.
From top to bottom are input images I, low-level saliency
sl , face channel saliency sf , and combined saliency mapss.

2.2. Saliency aggregation
Although saliency maps extracted in the previous step
accurately predict gaze points, the accuracy is still not goodenough to determine exact locations of gaze points.
The saliency maps s encode distinctive visual features ina video frame. While a saliency map does not provide exact gaze points, highly salient regions in a saliency map are(2)

When the gaze points of eye images ei and ej are close, theappearances are similar and ws becomes high.
In this step, we first eliminate unreliable eye images, e.g.,
images during blinking, from the learning set. Eye images
recorded during fixation are useful as a learning data. To
identify such eye images, we use a fixation measure of aneye image e defined as
wf (ei ) = exp(−κf Var(ei )),(3)

where Var(ei ) denotes the variance of eye images{ei−nf , . . . , ei+nf } averaged over pixels.
Since appearances of the eye images change rapidly during
fast movement, wf becomes lower when ei is captured during eye movement or blinking.
A subset
Ds0 = {(s1 , e1 ), . . . , (sN 0 , eN 0 )} is created from Ds byremoving eye images where wf scores are lower than apredefined threshold τf .
Since variation in the gaze points is limited in Ds0 , andthere can be many samples that share almost the same gazepoint, eye images are clustered according to similarity ws to
reduce redundancy and computational cost. Each eye image
e is sequentially added to the cluster whose average eye
image ē is the most similar to e. A new cluster is adaptively
created if the highest similarity among all existing clusters
is lower than the threshold τs . Finally, M clusters and theiraverage eye images {ē1 , . . . , ēM } are calculated.
After these steps, we compute the gaze probability map
p̄i as
PN 0
j ws (ēi , ej )(sj − s̄all )
,
(4)
p̄i =
PN 0
j ws (ēi , ej )where s̄all is the average of saliency maps in Ds0 . Manmade pictures usually have higher saliency at the center ofthe image, Hence, without normalization, the gaze probability map p̄i tends to have higher value at the center regardlessof ēi . The average saliency map s̄all is used to eliminate thiscentering bias in the gaze probability map. Each gaze probability map p̄i is normalized to a fixed range. Finally, we
obtain a dataset Dp = {(p̄1 , ē1 ), . . . , (p̄M , ēM )}.

1.0
0.8
0.6
0.4
0.2
0.0
0.0

Figure 4. Examples of gaze probability maps p̄ and corresponding
average eye images ē. Overlaid circles depict true gaze points of ē
to illustrate the correspondence between a gaze point and the peakin the gaze probability. The true gaze points are obtained using acalibration-based gaze estimator, and our method does not know
the true gaze points.

Figure 4 shows examples of the obtained gaze probability maps. The eye images shown at the top-left indicate corresponding average eye images ē. The six images are someexamples taken from six different people. The overlaid circles indicate true gaze points of ē. The true gaze points
are unknown in our method, and these are obtained using acalibration-based gaze estimator and placed as a reference.
Although the gaze probability maps p̄i are generated without knowing true gaze points, these highly correspond to thetrue gaze points.
Figure 5 shows the improvement of the correlation between the true gaze point and saliency maps by this aggregation step. The curves are drawn by changing saliency
threshold values from minimum to maximum. The horizontal axis indicates a false positive rate, i.e., rate of pixels ina map above a threshold. The vertical axis indicates a true
positive rate, i.e., rate of frames whose saliency value at thetrue gaze point is higher than the threshold. This plot is obtained using all data used in our experiment. The thin line
shows the average receiver operating characteristic (ROC)
curve (area under the curve (AUC) = 0.73) of the extracted
saliency maps before aggregation. After aggregation, theaccuracy is improved as shown by the bold line in Figure 5,
which shows the average ROC curve (AUC = 0.90) of all
the gaze probability maps.

0.2

0.4

0.6

0.8

1.0

Figure 5. ROC curves of raw saliency maps and gaze probability
maps. Horizontal axis indicates the false positive rate, i.e., pixel
rate above a threshold. Vertical axis indicates the true positive
rate, i.e., rate of frames which have a higher saliency value thana threshold at the true gaze point. Thin line (AUC = 0.73) indicates raw saliency maps extracted through process described in
Section 2.1. Bold line (AUC = 0.90) corresponds to the gaze probability maps described in Section 2.2.

2.3. Estimator construction
In the previous step, the average eye images{ē1 , . . . , ēM } and corresponding gaze probability maps{p̄1 , . . . , p̄M } are produced.
In a standard Gaussian process regression, amodel can be built to estimate the probability distribution P (g ∗ |e∗ , Dg ) of an unknown gaze pointg ∗ of an eye image e∗ , given labeled data points
Dg = {(g1 , ē1 ), . . . , (gM , ēM )}. However in our case, weonly know Dp = {(p̄1 , ē1 ), . . . , (p̄M , ēM )} where p̄i canbe treated as a probability map of gi .
Therefore, we re-formulate Gaussian process regression
using the probability maps as follows. By normalizing thegaze probability maps, we define probability distributionsasp̄(g)P (g|p̄) = P P
x

y p̄

,

(5)

where
P Pp̄(g) indicates the value of p̄ at the gaze point g, andxy p̄ indicates overall summation of p̄. Here, we describe the case of estimating one-dimensional scalar g to
simplify the notation; however, two regressors are independently built for each X- and Y-direction. Given Eq. (5),the target distribution P (g ∗ |e∗ , Dp ) can be obtained bymarginalizing over all possible gaze points {g1 , . . . , gM }asP (g ∗ |e∗ , Dp ) =
X
g1

···

X
gM

P (g ∗ |e∗ , Dg )P (Dg |Dp ), (6)can be estimated as a Gaussian distribution N (µ, σ 2 ) with

where
P (Dg |Dp ) =MYP (gi |p̄i ).

(7)

µ=

i

In Eq. (6), g ∗ indicates the unknown gaze point of the eye
image e∗ , and gi is the gaze point corresponding to ēi .
Because the integral (summation) of Eq. (6) is computationally expensive, we solve Eq. (6) by Monte Carloapproximation. We randomly produce ng sets of samng
(l)
(l)
(l)
ples Dg = {(g1 , ē1 ), . . . , (gM , ēM )}l=1
according to the(l)probability distribution defined by Eq. (5). Namely, gi
in the l-th set is generated according to the distribution
P (gi |p̄i ) defined by the i-th probability map. Because thegaze probability maps accurately predict true gaze points asshown in Figure 5, the low saliency values from the gazeprobability maps are cut off to reduce the number of samples in the approximation. We use a threshold τs to set theprobability to zero if p̄(x, y) is lower than the threshold.
Using these sets, Eq. (6) can be approximated as

ng

l=1

l=1

(12)

The variance σ 2 equals to σ12 , because σl2 of Eq. (11) is
independent of the index l. Therefore, σ 2 can be calculated
by taking σ12 .

2.4. Gaze estimation
Once we have matrices K, S and {G(1) , . . . , G(ng ) } in
Eqs. (10) and (11), a gaze point can be estimated by taking any eye image e as input. The estimated distributions
for each X- and Y-direction, N (µx , σx2 ) and N (µy , σy2 ),are converted to the display coordinates N (µ̂x , σ̂x2 ) andN (µ̂y , σ̂y2 ) asµ̂x = xo +HIWI
µx , µ̂y = yo +
µy ,
Ws
Hs

(13)

HI 2
WI 2
σx , σ̂y2 =
σ ,
Ws
Hs y

(14)

and

ng

1 X
P (g ∗ |e∗ , Dg(l) ).
P (g |e , Dp ) =
ng
∗

ng

1 X
1 X 2
µl , σ 2 =σl = σ12 .
ng
ng

∗

(8)

l=1

(l)

Finally, each P (g ∗ |e∗ , Dg ) can be estimated based on a
Gaussian process regression [16].
Gaussian process regression We assume a noisy observation model gi = f (ei ) + i , i.e., a gaze point gi is
given as a function of ei with the data-dependent noise term
i = N (0, ςi2 ). In standard methods, the noise variance ς 2 istreated as an unknown parameter that takes a constant value
across all data. In our case, because the sample distribution
is known, the noise variance ςi2 can be set to an actual vari(n )(1)ance of generated samples {gi , . . . , gi g }. It explicitly
assigns a higher noise variance for samples from ambiguous saliency maps with several peaks. f (ei ) is assumed to
be a zero-mean Gaussian process with a covariance functionk:k(ei , ej ) = α exp(−β||ei − ej ||2 ),(9)with parameters α and β.
With this assumption,(l)
P (g ∗ |e∗ , Dg ) is derived as a Gaussian distribution
N (µl , σl2 ) with
µl = K ∗ (K + S)−1 G(l) ,(10)σl2 = k(e∗ , e∗ ) − K ∗ (K + S)−1tK ∗ ,

(11)

and

where Kij = k(ēi , ēj ), Ki∗ = k(ēi , e∗ ), Sij = ςi2 δij and(l)(l)
Gi = gi 1 . As a result, the distribution P (g ∗ |e∗ , Dp )1 K ∈ RM ×M , K ∗ ∈ R1×M , S ∈ RM ×M and G(l) ∈ R1×M

σ̂x2 =

where Ws , Hs indicates the width and height of the saliency
maps, WI , HI indicates the actual width and height of thedisplayed images {I1 , . . . , IN }, and (xo , yo ) indicates thedisplay origin of the images. The average (µ̂x , µ̂y ) corresponds to the estimated gaze point g.

3. Experimental results
In this section, we show experimental results to evaluate our method. In the experiments, we used four video
clips from four films: A) 2001: A Space Odyssey, Stanley Kubrick, 1968, B) Dreams, Akira Kurosawa, 1990, C)
Nuovo Cinema Paradiso, Giuseppe Tornatore, 1988 and D)Forrest Gump, Robert Zemeckis, 1994. It is known that human gaze control is also strongly influenced by contexts andplots of films, however, such high-level attentions are notmodeled by the bottom-up saliency model we employed.
Hence, each film was shortened to a 10-minute video clip
without audio signal by extracting 2-second sequences atregular intervals to remove these effects. The video clips
were resized to a fixed dimension of 720 × 405, and thedisplay resolution was set to WI = 1920 and HI = 1080.
The video clips were shown at 15 fps; therefore, N = 9000
in the experiments. The saliency maps were calculated at asmaller resolution, Ws = 32 and Hs = 18.
Six novice test subjects s1 . . . s6 were asked to watch twovideo clips. The combinations of video clips and test subjects are defined so that every clip was tested as learning
data against three different subject persons as listed in Table 1. A chin rest is used to fix their head positions, andFigure 6. Estimation results. The estimation results are rendered as 2-D Gaussian circles. The corresponding input eye images are shownat the top-left corner. Overlaid circles are the ground truth gaze points obtained from a calibration-based gaze estimator.
Table 1. Combinations of video clips A to D and test subjects s1
to s6 . For example, person s1 watched clips A and B.

SourceA
B
C
D

Destination
B C D
s1 s2 s3
s1
s4 s5
s2 s4
s6
s3 s5 s6
A

a 22.0-inches WUXGA (473.8 × 296.1 mm) display wasplaced 400 mm in front of the subject when video clips
were shown. While the subjects were watching the clips,
their eyes were automatically detected and captured using
OMRON OKAO Vision library.
The ground truth calibration data were collected for eachuser by showing reference points in a separate stage. For
this, 16 × 9 points were shown at 120 × 120-pixel intervals
and eye images were captured in the same way. The groundtruth was used to quantitatively assess our method in comparison with the gaze estimation method that involves an
explicit calibration stage.
Throughout the experiment, the parameters were set
as follows; ns = 5, κs = 7.8 × 10−7 , τs = 0.4, nf = 5,
κf = 0.02, ng = 50, α = 50, β = 5.0 × 10−9 , and τs wasadaptively set to keep the top 20% of pixels and set remaining 80% to zero in each map. These parameter settings are
empirically obtained from our experiment. In our current
implementation, it took about 0.15 seconds per frame whenM ' 600 using a Core 2 Quad 2.66GHz with simple code
parallelization using OpenMP [13].

3.1. Gaze estimation results
Using the two clips × six subject people, we tested our
method in two scenarios. In Scenario 1, we assessed our
method using the learning dataset as a test dataset. Because
the true gaze points are not known in the learning dataset,
this experiment was designed to verify the performance of
the algorithm. In Scenario 2, evaluations were performed
using another dataset from the user as a test dataset to confirm the applicability of the trained gaze estimator to otherdatasets.
The ground truth gaze points of the datasets were obtained using a calibration-based gaze estimator. It wasachieved by a standard Gaussian process regression methodwith a labeled dataset. Namely, pairs of the ground-truth
gaze points and eye images were explicitly given to learn
the relationship between gaze points and eye images. The
same covariance function (Eq. (9)) was used, and α and β
were set to be the same values as our estimator. The noisevariance ς 2 was empirically set to zero under the assumption that the ground-truth dataset is noise-free.
Figure 6 shows examples of the estimation results. Outputs of the estimators are rendered as 2-D Gaussian circles centered at (µ̂x , µ̂y ) with variance (σ̂x2 , σ̂y2 ) given by
Eq. (12). The center coordinate (µ̂x , µ̂y ) corresponds to theestimated gaze point. The eye images shown at the topleft corner show input eye images for estimation, and theoverlaid circles represent true gaze points obtained from thecalibration-based estimator.
Table 2 summarizes the estimation results for each video
clips. Each row corresponds to the average of three subjects’ results where the corresponding video clip is used as
the training dataset (see Table 1). First two columns indicate AUCs of average ROC curves of the raw saliency mapss and gaze probability maps p̄. The rest of the columns indicate estimation errors in distance and angle represented
as average ± standard deviation. Distance errors are evaluated as the Euclidean distance between the estimated andground-truth gaze points, and angular errors are calculatedusing the distance between eyes and the display.
From these results, it is observed that the gaze estimation
accuracy depends on the accuracy of the gaze probability
maps. When the AUC of the gaze probability maps p̄ islower, the estimation error tends to become larger.
Table 3 lists the estimation error of each subject person.
Each row corresponds to average of results of the corresponding test subject with two different training datasets.
The columns show AUCs and estimation errors in the similar manner as in Table 2. In contrast to Table 2, subject
dependency of our method is not clearly observed.
The accuracy of our method has dependency on the distribution of learning samples. Figure 7 shows the spatial
distribution of average estimation errors. Each grid corresponds to a reference point that is used to capture the calibration data when producing the ground truth data. Usingeye images obtained from the ground truth dataset as input
to our method, we compute the errors of our method. Lower
intensity corresponds to the lower estimation error. From
this, the larger errors can be observed at edges of the display. Figure 8 shows the average saliency map and spatial
histogram of gaze points. The left image shows the averageof all raw saliency maps extracted from the four video clips
used in our experiment. The right image shows the spatial histogram of ground-truth gaze points obtained from theexperiment dataset. Higher intensity corresponds to larger
amount of gaze points given at the grid. Usually salient objects are located at the center of video frames, and the gazepoint also tends to concentrate at the center of the display.
Because of these reasons, the number of learning samples
at the display edges are limited, and these cause the bias ofthe estimation accuracy shown in Figure 7.

Figure 7. Spatial distribution of estimation errors in the display
coordinate. Lower intensity corresponds to the lower estimation
error as illustrated in the right bar.

Figure 8. Average saliency map and spatial histogram of gazepoints. Left image shows the average of all raw saliency maps extracted from four video clips used in the experiment. Right image
shows the spatial histogram of the ground-truth gaze points of experimental dataset. Higher intensity corresponds to larger countsof gaze points.

4. Conclusions
We proposed a novel calibration-free gaze estimation
framework using saliency maps. By only using a synchronized set of eye images and video frames, a gaze estimatorcan be constructed by treating saliency maps as probabilistic distributions of gaze points. To the best of our knowledge, this is the first work to use saliency maps as the key
for gaze estimation. Our method naturally avoids an explicit
and noticeable gaze calibration step that is often demandingfor users. In our experimental setting with fixed head positions, our method achieves the accuracy of about 6-degree
error.
The estimation accuracy of our method depends on the
raw saliency maps extracted from input video clips. The
mechanism of human gaze control has not been completely
investigated, and there is a wide range of possibilities ofmore advanced saliency models for accurately predicting
gaze. Our method can benefit from the further investigationof more accurate saliency models.

References[1] N. Bruce and J. Tsotsos. Saliency, attention, and visual
search: An information theoretic approach. Journal of Vision, 9(3):1–24, 2009.
[2] M. Cerf, J. Harel, W. Einhauser, and C. Koch. Predicting
human gaze using low-level saliency combined with face detection. In Proceedings of Advances in neural information

Table 2. Average error for each video clip. Two AUC columns indicates AUCs of the average ROC curves of raw saliency maps s and gazeprobability maps p̄. The rest of columns indicates distance and angular estimation errors (average ± standard deviation) in two estimation
scenarios.

Clip
A
B
C
D
Average

s
AUC
0.75
0.71
0.74
0.70
0.73

p̄
AUC
0.90
0.87
0.92
0.89
0.90

Scenario 1
error [mm] error [deg.]
38 ± 26
5.3 ± 3.6
60 ± 328.3 ± 4.4
31 ± 20
4.3 ± 2.7
36 ± 235.0 ± 3.1
41 ± 25
5.7 ± 3.5

Scenario 2
error [mm] error [deg.]
45 ± 28
6.3 ± 3.856 ± 32
7.9 ± 4.3
36 ± 195.0 ± 2.542 ± 25
5.9 ± 3.445 ± 26
6.3 ± 3.5

Table 3. Average error for each subject person. Columns indicate AUCs of the average ROC curves and estimation errors as in Table 2.

Subject
s1
s2
s3
s4
s5
s6

s
AUC
0.74
0.75
0.72
0.71
0.71
0.74

p̄
AUC
0.90
0.93
0.87
0.87
0.89
0.92

Scenario 1
error [mm] error [deg.]
48 ± 35
6.8 ± 4.830 ± 20
4.1 ± 2.742 ± 27
5.9 ± 3.643 ± 26
6.0 ± 3.5
51 ± 26
7.1 ± 3.6
33 ± 18
4.6 ± 2.5

processing systems (NIPS 2008), volume 20, pages 241–248,
2008.
[3] D. W. Hansen and Q. Ji. In the eye of the beholder: A surveyof models for eyes and gaze. IEEE Transactions on patternanalysis and machine intelligence, 2009.
[4] J. Harel, C. Koch, and P. Perona. Graph-based visualsaliency. In Proceedings of Advances in neural information
processing systems (NIPS 2007), volume 19, pages 545–552,
2007.
[5] L. Itti and P. F. Baldi. Bayesian surprise attracts human attention. In Advances in Neural Information Processing Systems,
Vol. 19 (NIPS 2005), pages 547–554, 2006.
[6] L. Itti, N. Dhavale, and F. Pighin. Realistic avatar eye andhead animation using a neurobiological model of visual attention. In Proc. SPIE 48th Annual International Symposium
on Optical Science and Technology, volume 5200, pages 64–
78, 2003.
[7] L. Itti, C. Koch, and E. Niebur. A model of saliency-based
visual attention for rapid scene analysis. IEEE Transactions
on pattern analysis and machine intelligence, 20(11):1254–
1259, 1998.
[8] T. Judd, K. Ehinger, F. Durand, and A. Torralba. Learning
to predict where humans look. In Proceedings of the TwelfthIEEE International Conference on Computer Vision (ICCV2009), 2009.
[9] W. Kienzle, B. Scholkopf, F. A. Wichmann, and M. O. Franz.
How to find interesting locations in video: a spatiotemporalinterest point detector learned from human eye movements.
In Proceedings of the 29th Annual Symposium of the German
Association for Pattern Recognition (DAGM 2007), pages
405–414, 2007.
[10] W. Kienzle, F. A. Wichmann, B. Scholkopf, and M. O. Franz.
A nonparametric approach to bottom-up visual saliency. In

Scenario 2
error [mm] error [deg.]
48 ± 366.7 ± 5.0
30 ± 194.2 ± 2.6
58 ± 278.1 ± 3.7
48 ± 27
6.7 ± 3.6
52 ± 287.2 ± 3.834 ± 18
4.8 ± 2.4

Proceedings of Advances in neural information processing
systems (NIPS 2006), volume 19, pages 689–696, 2006.
[11] C. Koch and S. Ullman. Shifts in selective visual attention:
towards the underlying neural circuitry. Human neurobiology, 4(4):219–227, 1985.
[12] T. Nagamatsu, J. Kamahara, T. Iko, and N. Tanaka. Onepoint calibration gaze tracking based on eyeball kinematics
using stereo cameras. In Proceedings of the 2008 symposium
on Eye tracking research & applications (ETRA ’08), pages
95–98, 2008.
[13] OpenMP. http://openmp.org/.
[14] D. Parkhurst, K. Law, and E. Niebur. Modeling the role ofsalience in the allocation of overt visual attention. Visionresearch, 42(1):107–123, 2002.
[15] C. Privitera and L. Stark. Algorithms for defining visualregions-of-interest: Comparison with eye fixations. IEEE
Transactions on pattern analysis and machine intelligence,22(9):970–982, 2000.
[16] C. E. Rasmussen and C. K. I. Williams. Gaussian processes
for machine learning. The MIT Press, 2006.
[17] Y. Sugano, Y. Matsushita, Y. Sato, and H. Koike. An incremental learning method for unconstrained gaze estimation.
In Proceedings of the 10th European Conference on Computer Vision (ECCV 2008), pages 656–667, 2008.
[18] A. Villanueva and R. Cabeza. A novel gaze estimation
system with one calibration point. IEEE Transactions
on Systems, Man, and Cybernetics, Part B: Cybernetics,
38(4):1123–1138, 2008.
[19] H. Yamazoe, A. Utsumi, T. Yonezawa, and S. Abe. Remote
gaze estimation with a single camera based on facial-feature
tracking without special calibration actions. In Proceedingsof the 2008 symposium on Eye tracking research & applications (ETRA ’08), pages 245–250, 2008.