1

MPIIGaze: Real-World Dataset and Deep
Appearance-Based Gaze Estimation

arXiv:1711.09017v1 [] 24 Nov 2017

Xucong Zhang, Yusuke Sugano∗ , Mario Fritz, Andreas Bulling
Abstract—Learning-based methods are believed to work well for unconstrained gaze estimation, i.e. gaze estimation from a
monocular RGB camera without assumptions regarding user, environment, or camera. However, current gaze datasets were collected
under laboratory conditions and methods were not evaluated across multiple datasets. Our work makes three contributions towards
addressing these limitations. First, we present the MPIIGaze dataset, which contains 213,659 full face images and corresponding
ground-truth gaze positions collected from 15 users during everyday laptop use over several months. An experience sampling
approach ensured continuous gaze and head poses and realistic variation in eye appearance and illumination. To facilitate
cross-dataset evaluations, 37,667 images were manually annotated with eye corners, mouth corners, and pupil centres. Second, we
present an extensive evaluation of state-of-the-art gaze estimation methods on three current datasets, including MPIIGaze. We study
key challenges including target gaze range, illumination conditions, and facial appearance variation. We show that image resolution
and the use of both eyes affect gaze estimation performance, while head pose and pupil centre information are less informative. Finally,
we propose GazeNet, the first deep appearance-based gaze estimation method. GazeNet improves on the state of the art by 22%
(from a mean error of 13.9 degrees to 10.8 degrees) for the most challenging cross-dataset evaluation.
Index Terms—Unconstrained Gaze Estimation, Cross-Dataset Evaluation, Convolutional Neural Network, Deep Learning

F

1

I NTRODUCTION

estimation is well established as a research topic in
computer vision because of its relevance for several applications, such as gaze-based human-computer interaction [1]
or visual attention analysis [2], [3]. Most recent learning-based
methods leverage large amounts of both real and synthetic training
data [4], [5], [6], [7] for person-independent gaze estimation.
They have thus brought us one step closer to the grand vision
of unconstrained gaze estimation: 3D gaze estimation in everyday
environments and without any assumptions regarding users’ facial
appearance, geometric properties of the environment and camera,
or image formation properties of the camera itself. Unconstrained
gaze estimation using monocular RGB cameras is particularly
promising given the proliferation of such cameras in portable
devices [8] and public displays [9].
While learning-based methods have demonstrated their potential for person-independent gaze estimation, methods have not
been evaluated across different datasets to properly study their
generalisation capabilities. In addition, current datasets have been
collected under controlled laboratory conditions that are characterised by limited variability in appearance and illumination and
the assumption of accurate head pose estimates. These limitations
not only bear the risk of significant dataset bias – an important
problem also identified in other areas in computer vision, such as
object recognition [10] or salient object detection [11]. They also
impede further progress towards unconstrained gaze estimation,
given that it currently remains unclear how state-of-the-art methods perform on real-world images and across multiple datasets.

G

•
•
•

AZE

X. Zhang, M. Fritz, and A. Bulling are with the Max Planck Institute for
Informatics, Saarland Informatics Campus, Saarbrücken, Germany.
E-mail: {xczhang, mfritz, bulling}@mpi-inf.mpg.de
Y. Sugano is with the Graduate School of Information Science and
Technology, Osaka University, Japan. E-mail: sugano@ist.osaka-u.ac.jp
∗ Work conducted while at the Max Planck Institute for Informatics

Input image

Eye image e
Generic face
model

3D face model
fitting

3D head rotation r
and
eye location t

Gaze angle
vector g

Data
normalisation
CNN model
training/test

Face and landmark
detection
Camera
parameters

Head angle
vector h

Fig. 1: Overview of GazeNet– appearance-based gaze estimation
using a deep convolutional neural network (CNN).

This work aims to shed light on these questions and make
the next step towards unconstrained gaze estimation. To facilitate
cross-dataset evaluations, we first introduce the MPIIGaze dataset,
which contains 213,659 images that we collected from 15 laptop
users over several months in their daily life (see Fig. 2). To
ensure frequent sampling during this time period, we opted for an
experience sampling approach in which participants were regularly
triggered to look at random on-screen positions on their laptop.
This way, MPIIGaze not only offers an unprecedented realism
in eye appearance and illumination variation but also in personal
appearance – properties not available in any existing dataset. Methods for unconstrained gaze estimation have to handle significantly
different 3D geometries between user, environment, and camera.
To study the importance of such geometry information, we groundtruth annotated 37,667 images with six facial landmarks (eye and
mouth corners) and pupil centres. These annotations make the
dataset also interesting for closely related computer vision tasks,
such as pupil detection. The full dataset including annotations is
available at https://www.mpi-inf.mpg.de/MPIIGaze.
Second, we conducted an extensive evaluation of several stateof-the-art methods on three current datasets: MPIIGaze, EYEDIAP [12], and UT Multiview [6]. We include a recent learning-

2

Fig. 2: Sample images from the MPIIGaze dataset showing the considerable variability in terms of place and time of recording, eye
appearance, and illumination (particularly directional light and shadows). For comparison, the last column shows sample images from
other current publicly available datasets (cf. Table 1): UT Multiview [6] (top), EYEDIAP [12] (middle), and Columbia [13] (bottom).

by-synthesis approach that trains the model with synthetic data
and fine-tunes it on real data [14]. We first demonstrate the
significant performance gap between previous within- and crossdataset evaluation conditions. We then analyse various challenges
associated with the unconstrained gaze estimation task, including
gaze range, illumination conditions, and personal differences. Our
experiments show these three factors are responsible for 25%,
35% and 40% performance gap respectively, when extending or
restricting the coverage of training data. These analyses reveal
that, although largely neglected in previous research, illumination
conditions represent an important source of error, comparable to
differences in personal appearance.
Finally, we propose GazeNet, the first deep appearance-based
gaze estimation method based on a 16-layer VGG deep convolutional neural network. GazeNet outperforms the state of the
art by 22% on MPIIGaze and 8% on EYEDIAP for the most
difficult cross-dataset evaluation. Our evaluations represent the
first account of the state of the art in cross-dataset gaze estimation
and, as such, provide valuable insights for future research on this
important but so far under-investigated computer vision task.
An earlier version of this work was published in [15]. Parts
of the text and figures are reused from that paper. The specific
changes implemented in this work are: 1) Extended annotation of
37,667 images with six facial landmarks (four eye corners and two
mouth corners) and pupil centres, 2) updated network architecture
to a 16-layer VGGNet, 3) new cross-dataset evaluation when training on synthetic data, 4) new evaluations of key challenges of the
domain-independent gaze estimation task, specifically differences
in gaze range, illumination conditions, and personal appearance,
and 5) new evaluations on the influence of image resolution, the
use of both eyes, and the use of head pose and pupil centre
information on gaze estimation performance.

2
2.1

R ELATED W ORK
Gaze Estimation Methods

Gaze estimation methods can generally be distinguished as modelbased or appearance-based [22]. Model-based methods use a
geometric eye model and can be further divided into cornealreflection and shape-based methods. Corneal-reflection methods
rely on eye features detected using reflections of an external
infrared light source on the outermost layer of the eye, the cornea.
Early works on corneal reflection-based methods were limited to
stationary settings [23], [24], [25], [26] but were later extended

to handle arbitrary head poses using multiple light sources or
cameras [27], [28]. Shape-based methods [29], [30], [31], [32]
infer gaze directions from the detected eye shape, such as the pupil
centres or iris edges. Although model-based methods have recently
been applied to more practical application scenarios [8], [33], [34],
[35], [36], their gaze estimation accuracy is still lower, since
they depend on accurate eye feature detections for which highresolution images and homogeneous illumination are required.
These requirements have largely prevented these methods from
being widely used in real-world settings or on commodity devices.
In contrast, appearance-based gaze estimation methods do not
rely on feature point detection but directly regress from eye images
to 3D gaze directions. While early methods assumed a fixed
head pose [37], [38], [39], [40], [41], [42], more recent methods
allow for free 3D head movement in front of the camera [43],
[44], [45], [46], [47]. Because they do not rely on any explicit
shape extraction stage, appearance-based methods can handle lowresolution images and long distances. However, these methods
require more person-specific training data than model-based approaches to cover the significant variability in eye appearance
caused by free head motion and were therefore mainly evaluated
for a specific domain or person. An open research challenge in
gaze estimation is to learn gaze estimators that do not make any
assumptions regarding the user, environment, or camera.
2.2

Person-Independent Gaze Estimation

The need to collect person-specific training data represents a fundamental limitation for both model-based and appearance-based
methods. To reduce the burden on the user, several previous works
used interaction events, such as mouse clicks or key presses, as a
proxy for users’ on-screen gaze position [48], [49]. Alternatively,
visual saliency maps [50], [51] or pre-recorded human gaze patterns of the presented visual stimuli [52] were used as probabilistic
training data to learn the gaze estimation function. However, the
need to acquire user input fundamentally limits the applicability
of these approaches to interactive settings.
Other methods aimed to learn gaze estimators that generalise
to arbitrary persons without requiring additional input. A large
body of works focused on cross-person evaluations in which the
model is trained and tested on data from different groups of participants. For example, Schneider et al. performed a cross-person
evaluation on the Columbia dataset [13] with 21 gaze points for
one frontal head pose of 56 participants [5]. Funes et al. followed

3

Participants

Head poses

Gaze
targets

Illumination
conditions

Face
annotations

Amount of
data

Collection
duration

3D
anno.

Villaneuva et al. [16]

103

1

12

1

1,236

1,236

1 day

No

TabletGaze [17]

51

continuous

35

1

none

1,428 min

1 day

No

1,474

continuous

continuous

daily life

none

2,445,504

1 day

No

Columbia [13]

56

5

21

1

none

5,880

1 day

Yes

McMurrough et al. [19]

20

1

16

1

none

97 min

1 day

Yes

Weidenbacher et al. [20]

20

19

2-9

1

2,220

2,220

1 day

Yes

OMEG [21]

50

3 + continuous

10

1

unknown

333 min

1 day

Yes

EYEDIAP [12]

16

continuous

continuous

2

none

237 min

2 days

Yes

UT Multiview [6]

50

8 + synthesised

160

1

64,000

64,000

1 day

Yes

MPIIGaze (ours)

15

continuous

continuous

daily life

37,667

213,659

9 days ∼
3 months

Yes

GazeCapture [18]

Table 1: Overview of publicly available appearance-based gaze estimation datasets showing the number of participants, head poses and
on-screen gaze targets (discrete or continuous), illumination conditions, images with annotated face and facial landmarks, amount of
data (number of images or duration of video), collection duration per participant, as well as the availability of 3D annotations of gaze
directions and head poses. Datasets suitable for cross-dataset evaluation (i.e. that have 3D annotations) are listed below the double line.

a similar approach, but only evaluated on five participants [4].
To reduce data collection and annotation efforts, Sugano et al.
presented a clustered random forest method that was trained on
a large number of synthetic eye images [6]. The images were
synthesised from a smaller number of real images captured using
a multi-camera setup and controlled lighting in a laboratory. Later
works evaluated person-independent gaze estimation methods on
the same dataset [53], [54]. Krafka et al. recently presented a
method for person-independent gaze estimation that achieved 1.71
cm on an iPhone and 2.53 cm on an iPad screen [18]. However, the
method assumed a fixed camera-screen relationship and therefore
cannot be used for cross-dataset gaze estimation.
2.3

Unconstrained Gaze Estimation

Despite significant advances in person-independent gaze estimation, all previous works assumed training and test data to come
from the same dataset. We were first to study the practically
more relevant but also significantly more challenging task of unconstrained gaze estimation via cross-dataset evaluation [15]. We
introduced a method based on a multimodal deep convolutional
neural network that outperformed all state-of-the-art methods by a
large margin. More recently, we proposed another method that, in
contrast to a long-standing line of work in computer vision, only
takes the full face image as input, resulting again in significant
performance improvements for both 2D and 3D gaze estimation [55]. In later works, Wood et al. demonstrated that large-scale
methods for unconstrained gaze estimation could benefit from
parallel advances in computer graphics techniques for eye region
modelling. These models were used to synthesise large amounts of
highly realistic eye region images, thereby significantly reducing
both data collection and annotation efforts [14]. Their latest model
is fully morphable [56] and can synthesise large numbers of
images in a few hours on commodity hardware [7].
2.4

Datasets

Several gaze estimation datasets have been published in recent
years (see Table 1 for an overview). Early datasets were severely
limited with respect to variability in head poses, on-screen gaze

targets, illumination conditions, number of images, face and facial
landmark annotations, collection duration per participant, and
annotations of 3D gaze directions and head poses [13], [16], [19],
[20]. More recent datasets are larger and cover the head pose and
gaze ranges continuously. The OMEG dataset includes 200 image
sequences from 50 people with fixed and free head movement
but discrete visual targets [21]. TabletGaze includes 16 videos
recorded from 51 people looking at different points on a tablet
screen [17]. The EYEDIAP dataset contains 94 video sequences of
16 participants who looked at three different targets (discrete and
continuous markers displayed on a monitor, and floating physical
targets) under both static and free head motion conditions [12].
The UT Multiview dataset also contains dense gaze samples of 50
participants and 3D reconstructions of eye regions that can be used
to synthesise images for arbitrary head poses and gaze targets [6].
However, all of these datasets were still recorded under controlled
laboratory settings and therefore only include a few illumination
conditions. While the recent GazeCapture dataset [18] includes
a large number of participants, the limited number of images
and similar illumination conditions per participant make it less
interesting for unconstrained gaze estimation. Even more importantly, the lack of 3D annotations limits its use to within-dataset
evaluations. Several large-scale datasets were published for visual
saliency prediction, such as the crowd-sourced iSUN dataset [57],
but their focus is on bottom-up saliency prediction, and input face
or eye images are not available.

3

T HE MPIIG AZE DATASET

To be able to evaluate methods for unconstrained gaze estimation,
a dataset with varying illumination conditions, head poses, gaze
directions, and personal appearance was needed. To fill this gap,
we collected the MPIIGaze dataset that contains a large number
of images from different participants, covering several months of
their daily life (see Fig. 2 for sample images from our dataset).
The long-term recording resulted in a dataset that is one order
of magnitude larger and significantly more variable than existing
datasets (cf. Table 1). All images in the dataset come with 3D
annotations of gaze target and detected eye/head positions, which

4

7%
6%
5%
4%
3%
2%
1%
0%

18%

18%

16%

16%

Percentage of images

Percentage of images

Percentage of images

8%

9%

14%
12%
10%
8%
6%
4%
2%

8%
6%
4%
2%
0%

0%

0

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23

14%
12%
10%

16 32 48 64 80 96 112 128 144 160 176 192 208 224 240 256

Time of day

Mean greyscale intensity

-140 -100 -120 -80 -60 -40 -20

0

20

40

60

80 100 120 140

Horizontal difference in mean greyscale

Fig. 3: Key characteristics of our dataset. Percentage of images collected at different times of day (left), having different mean greyscale intensities within the face region (middle), and having horizontally different mean grey-scale intensities between the left to right
half of the face region (right). Representative sample images are shown at the top.
50

50

50

25

25

25

0

0

0

-25

-25

-25

-50-50

-25

0

25

(a) h (MPIIGaze)

50 -50-50

-25

0

25

50 -50-50

(b) h (UT Multiview)
50

50

25

25

25

0

0

0

-25

-25

-25

-25

0

25

(d) g (MPIIGaze)

50 -50-50

-25

0

25

50 -50-50

(e) g (UT Multiview)

0

25

50

(c) h (EYEDIAP)

50

-50-50

-25

-25

0

25

50

(f) g (EYEDIAP)

Fig. 4: Distributions of head angle (h) and gaze angle (g )
in degrees for MPIIGaze, UT Multiview, and the screen target
sequences in EYEDIAP (cf. Table 1).

is required for cross-dataset training and evaluation. Our dataset
also provides manual facial landmark annotations on a subset of
images, which enables a principled evaluation of gaze estimation
performance and makes the dataset useful for other face-related
tasks, such as eye or pupil detection.
3.1

Collection Procedure

We designed our data collection procedure with two main objectives in mind: 1) to record images of participants outside of
controlled laboratory conditions, i.e during their daily routine,
and 2) to record participants over several months to cover a
wider range of recording locations and times, illuminations, and
eye appearances. We opted for recording images on laptops not
only because they are suited for long-term daily recordings but
also because they are an important platform for eye tracking
applications [1]. Laptops are personal devices, therefore typically
remaining with a single user, and they are used throughout the day
and over long periods of time. Although head pose and gaze range
are a bit limited compared to the fully unconstrained case due
to the screen size, they have a strong advantage in that the data
recording can be carried out in a mobile setup. They also come
with high-resolution front-facing cameras and their large screen
size allows us to cover a wide range of gaze directions. We further
opted to use an experience sampling approach to ensure images
were collected regularly throughout the data collection period [58].

We implemented custom software running as a background service on participants’ laptops, and opted to use the well-established
moving dot stimulus [59], to collect ground-truth annotations.
Every 10 minutes the software automatically asked participants to
look at a random sequence of 20 on-screen positions (a recording
session), visualised as a grey circle shrinking in size and with a
white dot in the middle. Participants were asked to fixate on these
dots and confirm each by pressing the spacebar exactly once when
the circle was about to disappear. If they missed this small time
window of about 500 ms, the software asked them to record the
same on-screen location again right after the failure. While we
cannot completely eliminate the possibility of bad ground truth,
this approach ensured that participants had to concentrate and look
carefully at each point during the recording.
Otherwise, participants were not constrained in any way, in
particular as to how and where they should use their laptops.
Because our dataset covers different laptop models with varying screen size and resolution, on-screen gaze positions were
converted to 3D positions in the camera coordinate system. We
obtained the intrinsic parameters from each camera beforehand
using the camera calibration procedure from OpenCV [60]. The
3D position of the screen plane in the camera coordinate system
was estimated using a mirror-based calibration method in which
the calibration pattern was shown on the screen and reflected to
the camera using a mirror [61]. Both calibrations are required
for evaluating gaze estimation methods across different devices.
3D positions of the six facial landmarks were recorded from
all participants using an external stereo camera prior to the data
collection, which could be used to build the 3D face model.
3.2

Dataset Characteristics

We collected a total of 213,659 images from 15 participants (six
female, five with glasses) aged between 21 and 35 years. 10 participants had brown, 4 green, and one grey iris colour. Participants
collected the data over different time periods ranging from 9 days
to 3 months. The number of images collected for each participant
varied from 1,498 to 34,745. Note that we only included images in
which a face could be detected (see Section 4.1). Fig. 3 (left) shows
a histogram of times of the recording sessions. Although there is
a natural bias towards working hours, the figure shows the high
variation in recording times. Consequently, our dataset also covers
significant variation in illumination. To visualise the different
illumination conditions, Fig. 3 (bottom) shows a histogram of
mean grey-scale intensities inside the face region. Fig. 3 (right)
further shows a histogram of the mean intensity differences from

5

(a) MPIIGaze gazing (0◦ , 0◦ )

(b) MPIIGaze gazing (15◦ , -15◦ )
(a)

(c) UT Multiview gazing (30◦ , 5◦ ) (d) EYEDIAP gazing (25◦ , 15◦ )

Fig. 5: Sample images from a single person for roughly the same
gaze directions from MPIIGaze with (a) and without (b) glasses,
UT Multiview (c), and EYEDIAP (d).

(b)

(c)

Fig. 6: We manually annotated 37,667 images with seven facial
landmarks: the corners of the left and right eye, the mouth corners,
and the pupil centres. We used a semi-automatic annotation
approach: (a) Landmarks were first detected automatically (in red)
and, (b) if needed, corrected manually post-hoc (in green). We
also manually annotated the pupil centre without any detection (c).
Note that this is only for completeness and we do not use the pupil
centre as input for our method later.

the right side to the left side of the face region, indicative of strong
directional light for a substantial number of images.
The 2D histograms in Fig. 4 visualise the distributions of head
and gaze angles h, g in the normalised space, colour-coded from
blue (minimum) to red (maximum), for MPIIGaze in comparison with two other recent datasets, EYEDIAP (all screen target
sequences) [12] and UT Multiview [6] (see Section 4.2 for a
description of the normalisation procedure). The UT Multiview
dataset (see Fig. 4b and 4e) was only recorded under a single
controlled lighting condition, but provides good coverage of the
gaze and pose spaces. For the EYEDIAP dataset, Fig. 4c and 4f
show distributions of 2D screen targets that are comparable to our
setting, yet gaze angle distributions do not overlap, due to different
camera and gaze target plane setups (see Fig. 4a and 4d). For our
MPIIGaze dataset, gaze directions tend to be below the horizontal
axis in the camera coordinate system because the laptop-integrated
cameras were positioned above the screen, and the recording setup
biased the head pose to a near-frontal pose. The gaze angles in our
dataset are in the range of [-1.5, 20] degrees in the vertical and
[-18, +18] degrees in the horizontal direction.
Finally, Fig. 5 shows sample eye images from each dataset
after normalisation. Each group of images was randomly selected
from a single person for roughly the same gaze directions. Compared to the UT Multiview and EYEDIAP datasets (see Fig. 5c
and 5d), MPIIGaze contains larger appearance variations even
inside the eye region (see Fig. 5b), particularly for participants
wearing glasses (see Fig. 5a).
3.3

Facial Landmark Annotation

We manually annotated a subset of images with facial landmarks
to be able to evaluate the impact of face alignment errors on gaze
estimation performance. To this end, we annotated the evaluation
subset used in [15] that consists of a randomly-selected 1,500 left
eye and 1,500 right eye images of all 15 participants. Because eye
images could be selected from the same face, this subset contains
a total of 37,667 face images.
The annotation was conducted in a semi-automatic manner. We first applied a state-of-the-art facial landmark detection
method [62], yielding six facial landmarks per face image: the
four eye and two mouth corners. We then showed these landmarks
to two experienced human annotators and asked them to flag those
images that contained incorrect landmark locations or wrong face
detections (see Fig. 6b). 5,630 out of 37,667 images were flagged

Percentage of images

100%
80%
60%
40%
20%
0%
0.00

Facial landmarks
Pupil centre

0.05

0.10
0.15
0.20
RMS error normalised by eye distance

0.25

0.30

Fig. 7: Percentage of images for different error levels in the
detection of facial landmarks (blue solid line) and pupil centres (red dashed line). The x-axis shows the root-mean-square
(RMS) distance between the detected and annotated landmarks,
normalised by the distance between both eyes.
for manual annotation in this process. Subsequently, landmark
locations for all of these images were manually corrected by the
same annotators. Since automatic pupil centre localisation remains
challenging [63], we cropped the eye images using the manuallyannotated facial landmarks and asked the annotators to annotate
the pupil centres (see Fig. 6c).
Fig. 7 shows the detection error for facial landmarks and pupil
centres when compared to the manual annotation. We calculated
the error as the average root-mean-square (RMS) distances between the detected and annotated landmarks per face image. As
can be seen from the figure, 85% of the images had no error in the
detected facial landmarks. 0.98% of the images had normalised
RMS error less than 0.3. This error roughly corresponds to the
size of one eye and indicates that in these cases the face detection
method failed to correctly detect the target face. For the pupil
centre (red line), the error for each eye image is the RMS between
the detected and annotated pupil centre normalised by the distance
between both eyes. A normalised RMS error of 0.01 roughly
corresponds to the size of the pupil, and 80% of the images had
lower pupil detection performance.

4

M ETHOD

Prior work performed person-independent gaze estimation using
2D regression in the screen coordinate system [17], [18]. Because
this requires a fixed position of the camera relative to the screen,
these methods are limited to the specific device configuration, i.e.
do not directly generalise to other devices. The recent success
of deep learning combined with the availability of large-scale

6

z

er

z

Rr

er

x

x

z

Rr

er

x

y

y

S

z

y
y

Fig. 8: Definition of the head coordinate system defined based
on the triangle connecting three midpoints of the eyes and mouth.
The x-axis goes through the midpoints of both while the y-axis is
perpendicular to the x-axis inside the triangle plane. The z-axis is
perpendicular to this triangle plane.
datasets, such as MPIIGaze, opens up promising new directions
towards unconstrained gaze estimation that was not previously
possible. In particular, large-scale methods promise to learn gaze
estimators that can handle the significant variability in domain
properties as well as user appearance. Fig. 1 shows an overview of
our GazeNet method based on a multimodal convolutional neural
network (CNN). We first use state-of-the-art face detection [64]
and facial landmark detection [62] methods to locate landmarks
in the input image obtained from the calibrated monocular RGB
camera. We then fit a generic 3D facial shape model to estimate
3D poses of the detected faces and apply the space normalisation
technique proposed in [6] to crop and warp the head pose and eye
images to the normalised training space. A CNN is finally used to
learn a mapping from the head poses and eye images to 3D gaze
directions in the camera coordinate system.
4.1

Face Alignment and 3D Head Pose Estimation

Our method first detects the user’s face in the image with a HOGbased method [64]. We assume a single face in the images and
take the largest bounding box if the detector returned multiple face
proposals. We discard all images in which the detector fails to find
any face, which happened in about 5% of all cases. Afterwards,
we use a continuous conditional neural fields (CCNF) model
framework to detect facial landmarks [62].
While previous works assumed accurate head poses, we use a
generic mean facial shape model F for the 3D pose estimation to
evaluate the whole gaze estimation pipeline in a practical setting.
The generic mean facial shape F is built as the averaged shape
across all the participants, which could also be derived from any
other 3D face models. We use the same definition of the face
model and head coordinate system as [6]. The face model F
consists of 3D positions of six facial landmarks (eye and mouth
corners, cf. Fig.1). As shown in Fig.8, the right-handed head
coordinate system is defined according to the triangle connecting
three midpoints of the eyes and mouth. The x-axis is defined as
the line connecting midpoints of the two eyes in the direction
from the right eye to the left eye, and the y-axis is defined to be
perpendicular to the x-axis inside the triangle plane in the direction
from the eye to the mouth. The z-axis is hence perpendicular to
the triangle, and pointing backwards from the face. Obtaining the
3D rotation matrix Rr and translation vector tr of the face model
from the detected 2D facial landmarks p is a classical Perspectiven-Point, problem which is estimating the 3D pose of an object
given its 3D model and the corresponding 2D projections in the
image. We fit F to detected facial landmarks by estimating the
initial solution using the EPnP algorithm [65] and further refine
the pose by minimising the Levenberg-Marquardt distance.

z

Cr
y

(a)

Perspective

ds
Cn

z

xx
y

Cr

x

(b)

Rr
x

y

R
Middle point of 3D
facial landmarks

z

warping

x

Normalised
eye image

Cr

(c)

(d)

Fig. 9: Procedure for eye image normalisation. (a) Starting from
the head pose coordinate system centred at one of the eye centres
er (top) and the camera coordinate system (bottom); (b) the
camera coordinate system is rotated with R; (c) the head pose
coordinate system is scaled with matrix S ; (d) the normalised eye
image is cropped from the input image by the image transformation matrix corresponding to these rotations and scaling.

4.2

Eye Image Normalisation

Given that our key interest is in cross-dataset evaluation, we
normalise the image and head pose space as introduced in [6].
Fundamentally speaking, object pose has six degrees of freedom,
and in the simplest case the gaze estimator has to handle eye
appearance changes in this 6D space. However, if we assume
that the eye region is planar, arbitrary scaling and rotation of the
camera can be compensated for by its corresponding perspective
image warping. Therefore, the appearance variation that needs
to be handled inside the appearance-based estimation function
has only two degrees of freedom. The task of pose-independent
appearance-based gaze estimation is to learn the mapping between
gaze directions and eye appearances, which cannot be compensated for by virtually rotating and scaling the camera.
The detailed procedure for the eye image normalisation is
shown in Fig.9. Given the head rotation matrix Rr and the
eye position in the camera coordinate system er = tr + eh
where eh is the position of the midpoint of the two eye corners
defined in the head coordinate system (Fig. 9 (a)), we need to
compute the conversion matrix M = SR for normalisation. As
illustrated in Fig. 9 (b), R is the inverse of the rotation matrix
that rotates the camera so that the the camera looks at er (i.e., the
eye position is located along the z -axis of the rotated camera),
the x-axis of the head coordinate system is perpendicular to
the y -axis of the camera coordinate system. The scaling matrix
S = diag(1, 1, dn /ker k) (Fig. 9 (c)) is then defined so that the
eye position er is located at a distance dn from the origin of the
scaled camera coordinate system.
M describes a 3D scaling and rotation that brings the eye
centre to a fixed position in the (normalised) camera coordinate
system, and is used for interconversion of 3D positions between
the original and the normalised camera coordinate system. If we
denote the original camera projection matrix obtained from camera
calibration as Cr and the normalised camera projection matrix as
Cn , the same conversion can be applied to the original image pixels via perspective warping using the image transformation matrix
W = Cn M Cr−1 (Fig. 9 (d)). Cn = [fx , 0, cx ; 0, fy , cy ; 0, 0, 1],
where f and c indicate the focal length and principal point of
the normalised camera, which are arbitrary parameters of the
normalised space. The whole normalisation process is applied
to both right and left eyes in the same manner, with er defined
according to the corresponding eye position.

7
fc6

fc7

Input image

fc8

h

Convolution
13 Convolutional layers

Regression

Fully
Fully
connected connected

Fig. 10: Architecture of the proposed GazeNet. The head angle h
is injected into the first fully connected layer. The 13 convolutional
layers are inherited from a 16-layer VGG network [66].

gaze estimation including differences in gaze ranges, illumination
conditions, and personal appearance. Finally, we studied other
closely related topics, such as the influence of image resolution,
the use of both eyes, and the use of head pose and pupil centre information on gaze estimation performance. GazeNet was
implemented using the Caffe library [70]. We used the weights
of the 16-layer VGGNet [66] pre-trained on ImageNet for all our
evaluations, and fine-tuned the whole network in 15,000 iterations
with a batch size of 256 on the training set. We used the Adam
solver [71] with the two momentum values set to β1 = 0.9 and
β2 = 0.95. An initial learning rate of 0.00001 was used and
multiplied by 0.1 after every 5,000 iterations.

This yields a set of an eye image I , a head rotation matrix
Rn = M Rr , and a gaze angle vector gn = M gr in the
normalised space. gr is the 3D gaze vector originating from er Baseline Methods
in the original camera coordinate system. The normalised head rotation matrix Rn is then converted to a three-dimensional rotation We further evaluated the following baseline methods:
angle vector hn . Since rotation around the z-axis is always zero
• MnistNet: The four-layer (two convolutional and two fully
after normalisation, hn can be represented as a two-dimensional
connected layers) MnistNet architecture [72] has been used
rotation vector (horizontal and vertical orientations) h. gn is also
as the first CNN-based method for appearance-based gaze esrepresented as a two-dimensional rotation vector g assuming a
timation [15]. We used the implementation provided by [70]
unit length. We define dn to be 600 mm and focal length fx
and trained weights from scratch. The learning rate was set
and fy of the normalised camera projection matrix Cn to be 960,
to be 0.1 and the loss was also changed to the Euclidean
so that it is compatible with the UT Multiview dataset [6]. The
distance between estimated and ground-truth gaze directions.
resolution of the normalised eye image is set to I in 60 × 36
pixels, and thus cx and cy are set to 30 and 18, respectively.
• Random Forests (RF): Random forests were recently
Eye images I are converted to grey scale and histogram-equalised
demonstrated to outperform existing methods for personafter normalisation to make the normalised eye images compatible
independent appearance-based gaze estimation [6]. We used
between different datasets, facilitating cross-dataset evaluations.
the implementation provided by the authors, and the same
parameters as in [6], and we resized input eye images to
4.3 GazeNet Architecture
15 × 9 according to the implementation in [6], which has
been optimised.
The task for the CNN is to learn a mapping from the input features
(2D head angle h and eye image e) to gaze angles g in the
• k -Nearest Neighbours (kNN): As shown in [6], a simple
normalised space. In the unconstrained setting, the distance to
kNN regression estimator can perform well in scenarios that
the target gaze plane can vary. The above formulation thus has
offer a large amount of dense training images. We used the
the advantage that training data does not have to consider the
same kNN implementation and also incorporated a training
angle of convergence between both eyes. As pointed out in [6],
images clustering in head angle space.
the difference between the left and right eyes is irrelevant in the
person-independent evaluation scenario: By flipping eye images
• Adaptive Linear Regression (ALR): Because it was origihorizontally and mirroring h and g around the horizontal direction,
nally designed for a person-specific and sparse set of training
both eyes can be handled using a single regression function.
images [41], ALR does not scale well to large datasets. We
Our method is based on the 16-layer VGGNet architecture [66]
therefore used the same approximation as in [4], i.e. we
that includes 13 convolutional layers, two fully connected layers,
selected five training persons for each test person with lowest
and one classification layer with five max pooling layers in
interpolation weights. We further selected random subsets of
between. Following prior work on face [62], [67] and gaze [41],
images from the neigbours of the test image in head pose
[68] analysis, we use a grey-scale single channel image as input
space. We used the same image resolution as for RF.
with a resolution of 60 × 36 pixels. We changed the stride of
the first and second pooling layer from two to one to reflect the
smaller input resolution. The output of the network is a 2D gaze
angle vector ĝ consisting of two gaze angles, yaw ĝφ and pitch ĝθ .
We extended the vanilla VGGNet architecture into a multimodal
model to also take advantage of head pose information [69]. To
this end we injected head pose information h into the first fully
connected layer (fc6) (see Fig. 10). As a loss function we used the
sum of the individual L2 losses measuring the distance between
the predicted ĝ and true gaze angle vector g .

5

E XPERIMENTS

We first evaluated GazeNet for cross-dataset and cross-person
evaluation. We then explored key challenges in unconstrained

•

Support Vector Regression (SVR): Schneider et al. used
SVR with a polynomial kernel under a fixed head pose [5].
We used a linear SVR [73] for scalability given the large
amount of training data. We also used a concatenated vector
of HOG and LBP features (6×4 blocks, 2×2 cells for HOG)
as suggested in [5]. However, we did not use manifold alignment since it does not support pose-independent training.

•

Shape-based approach (EyeTab): In addition to the
appearance-based methods, we evaluated one state-of-the-art
shape-based method that estimates gaze by fitting a limbus
model (a fixed-diameter disc) to detected iris edges [8]. We
used the implementation provided by the authors.

8

25
Mean error [degrees]

20

SVR [5]
ALR [4]

kNN [6]
RF [6]

MnistNet [15]
GazeNet

GazeNet+
GazeNet+ (syn)

15
10
5
0

16.5 16.4 16.2 15.4 13.9 10.8

9.8

MPIIGaze / MPIIGaze+

9.1

15.1 12.7 12.3 12.0 10.5

9.6

EYEDIAP

Fig. 11: Gaze estimation error for cross-dataset evaluation with
training on 64,000 eye images in UT Multiview and testing
on 45,000 eye images of MPIIGaze or MPIIGaze+ (left) and
EYEDIAP (right). Bars show mean error across all participants;
error bars indicate standard deviations.

Datasets
As in [15], in all experiments that follow, we used a random subset
of the full dataset consisting of 1,500 left eye images and 1,500
right eye images from each participant. Because one participant
only offered 1,448 face images, we randomly oversampled data
of that participant to 3,000 eye images. From now on we refer
to this subset as MPIIGaze, while we call the same subset with
manual facial landmark annotations MPIIGaze+. To evaluate the
generalisation capabilities of the proposed method, in addition to
MPIIGaze, we used all screen target sequences with both VGA
and HD videos of the EYEDIAP dataset for testing [12]. We did
not use the floating target sequences in the EYEDIAP dataset since
they contain many extreme gaze directions that are not covered by
UT Multiview. We further used the SynthesEyes dataset [14] that
contains 11,382 eye samples from 10 virtual participants.
Evaluation Procedure
For cross-dataset evaluation, each method was trained on UT
Multiview or SynthesEyes, and tested on MPIIGaze, MPIIGaze+
or EYEDIAP. We used the UT Multiview dataset as the training
set for each method because it covers the largest area in head
and gaze angle spaces compared to EYEDIAP and our MPIIGaze
datasets (see Fig.4). Note that SynthesEyes has the same head
and gaze angle ranges as UT Multiview dataset. For cross-person
evaluation, we performed a leave-one-person-out cross-validation
for all participants on MPIIGaze+.
5.1

(10.8 degrees on MPIIGaze, 9.6 degrees on EYEDIAP). This
represents a significant performance gain of 22% (3.1 degrees)
on MPIIGaze and 8% on EYEDIAP (0.9 degrees), p < 0.01
using a paired Wilcoxon signed rank test [74], over the state-ofthe-art method [15]. Performance on MPIIGaze and MPIIGaze+
is generally worse than on the EYEDIAP dataset, which indicates
the fundamental difficulty of the in-the-wild setting covered by our
dataset. We also evaluated performance on the different sequences
of EYEDIAP (not shown in the figure). Our method achieved
10.0 degrees on the HD sequences and 9.2 degrees on the VGA
sequences. This difference is most likely caused by differences in
camera angles and image quality. The shape-based EyeTab method
performs poorly on MPIIGaze (47.1 degrees mean error and 7%
misdetection rate), which shows the advantage of appearancebased approaches in this challenging cross-dataset setting.
The input image size for some baselines, like RF, kNN and
ALR, has been optimized to be 15×9 pixels, which was lower than
the 60 × 36 pixels used in our method. To make the comparison
complete, we also evaluated our GazeNet with 15 × 9 pixels
input images and achieved 11.4 degrees gaze estimation error on
MPIIGaze, thereby still outperforming the other baseline methods.
Compared to GazeNet, GazeNet+ uses the manually annotated
facial landmark locations MPIIGaze+ instead of the detected ones.
In this case the mean error is reduced from 10.8 degrees to 9.8
degrees, which indicates that the face detection and landmark
alignment accuracy is still a dominant error factor in practice.
Furthermore, GazeNet+ (syn) implements the strategy proposed
in [14]. That is, we first trained the model with synthetic data and
then fine-tuned it on the UT Multiview dataset. This approach
further reduced the gaze estimation error to 9.1 degrees. For
comparison, the naive predictor that always outputs the average
gaze direction of all training eye images in UT Multiview (not
shown in the figure) achieves an estimation error of 34.2 degrees
on MPIIGaze and 42.4 degrees on EYEDIAP.
While GazeNet achieved significant performance improvements for this challenging generalisation task, the results underline
the difficulty of unconstrained gaze estimation. They also reveal a
critical limitation of previous laboratory-based datasets such as UT
Multiview with respect to variation in eye appearance, compared
to MPIIGaze, which was collected in the real world. The learningby-synthesis approach presented in [14] is promising given that it
allows the synthesis of variable eye appearance and illumination
conditions. This confirms the importance of the training data
and indicates that future efforts should focus on addressing the
gaze estimation task both in terms of training data as well as
methodology to bridge the gap to the within-dataset scenario.

Performance Evaluation

We first report the performance evaluation for the cross-dataset
setting, for which all the methods were trained and tested on
two different datasets respectively, followed by the cross-person
evaluation setting, for which all methods were evaluated with
leave-one-person-out cross-validation.
5.1.1 Cross-Dataset Evaluation
Fig. 11 shows the mean angular errors of the different methods
when trained on UT Multiview dataset and tested on both MPIIGaze, or MPIIGaze+, and EYEDIAP datasets. Bars correspond
to mean error across all participants in each dataset, and error bars
indicate standard deviations across persons. As can be seen from
the figure, our GazeNet shows the lowest error on both datasets

5.1.2

Cross-Person Evaluation

Although results of the previous cross-dataset evaluation showed
the advantage of our GazeNet, they still fall short of the crossperson performance reported in [6]. To discuss the challenges
of person-independent gaze estimation within MPIIGaze, we performed a cross-person evaluation using a leave-one-person-out
approach. Fig. 12 shows the mean angular errors of this crossperson evaluation. Since the model-based EyeTab method has
been shown to perform poorly in our setting, we opted to instead
show a learning-based result using the detected pupil (iris centre)
positions. More specifically, we used the pupil positions detected
using [8] in the normalised eye image space as a feature for kNN
regression, and performed the leave-one-person-out evaluation.

9

Mean error [degrees]

8

RF [6]
MnistNet [15]
GazeNet
GazeNet+

6
4
2
0

8.7

6.6

7.9

7.2

6.7

6.3

5.5

5.4

Fig. 12: Gaze estimation error on MPIIGaze and MPIIGaze+ for
cross-person evaluation using a leave-one-person-out approach.
Bars show the mean error across participants; error bars indicate
standard deviations; numbers on the bottom are the mean estimation error in degrees. GazeNet+ refers to the result for MPIIGaze+.

As can be seen from the figure, all methods performed better
than in the cross-dataset evaluation, which indicates the importance of domain-specific training data for appearance-based gaze
estimation methods. Although the performance gain is smaller in
this setting, our GazeNet still significantly (13%) outperformed
the second-best MnistNet with 5.5 degrees mean error (p < 0.01,
paired Wilcoxon signed rank test). While the pupil position-based
approach worked better than the original EyeTab method, its
performance was still worse than the different appearance-based
methods. In this case there is dataset-specific prior knowledge
about gaze distribution, and the mean prediction error that always
outputs the average gaze direction of all training images becomes
13.9 degrees. Because the noise in facial landmark detections is
included in the training set, there was no noticeable improvement
when testing our GazeNet on MPIIGaze+ (shown as GazeNet+
in Fig. 12). It contradicts the observation with the previous crossdataset evaluation that testing on MPIIGaze+ can bring one degree
of improvement compared to MPIIGaze with detected facial
landmarks (from 10.8 to 9.8 degrees).
5.2

Key Challenges

The previous results showed a performance gap between crossdataset and cross-person evaluation settings. To better understand
this gap, we additionally studied several key challenges. In all
analyses that follow, we used GazeNet+ in combination with
MPIIGaze+ to minimise error in face detection and alignment.
5.2.1

12
10
8
6
4
2
0

Mean error [degrees]

Pupil Pos.
SVR [5]
ALR [4]
kNN [6]

10

Differences in Gaze Ranges

As discussed in [15] and [14], one of the most important challenges for unconstrained gaze estimation is differences in gaze
ranges between the training and testing domains. Although handling the different gaze angles has been researched by combining
geometric and appearance-based methods [75], it is still challenging for appearance-based gaze estimation methods. The first bar
in Fig. 13 (UT) corresponds to the cross-dataset evaluation using
the UT Multiview dataset for training and MPIIGaze+ for testing.
In this case, as illustrated in Fig. 4, the training data covers
wider gaze ranges than the testing data. The second bar (UT Sub)
corresponds to the performance of the model trained on a subset
of the UT Multiview dataset that consists of 3,000 eye images
per participant selected so as to have the same head pose and gaze
angle distributions as MPIIGaze+. If the training dataset is tailored

9.8

8.0

7.3

UT

UT Sub

Syn. Sub

5.4

2.5

MPIIGaze+ MPIIGaze+
(cross(personperson)
specific)

0.9

UT Sub
(personspecific)

Fig. 13: Gaze estimation error on MPIIGaze+ using GazeNet+
for different training strategies and evaluation settings. Bars show
the mean error across participants; error bars indicate standard
deviations; numbers on the bottom are the mean estimation error
in degrees. From left to right: 1) training on UT Multiview, 2)
training on UT Multiview subset, 3) training on synthetic images
targeted to the gaze and head pose ranges, 4) training on MPIIGaze+ with cross-person evaluation, 5) training on MPIIGaze+
with person-specific evaluation, and 6) training on UT Multiview
subset with person-specific evaluation.

to the target domain and the specific gaze range, we achieve about
18% improvement in performance (from 9.8 to 8.0 degrees).
The top of Fig. 14 shows the gaze estimation errors in horizontal gaze direction with training on UT Multiview, UT Multiview
subset, and MPIIGaze+, and testing on MPIIGaze+. The dots
correspond to the average error for that particular gaze direction,
while the line is the result of a quadratic polynomial curve fitting.
The lines correspond to the UT, UT Sub and MPIIGaze+ (crossperson) bars in Fig. 13. As can be seen from the figure, for
the model trained on UT Multiview subset, gaze estimation error
increased for images that were close to the edge of the gaze range.
In contrast, the model trained on the whole UT Multiview showed
more robust performance across the full gaze direction range. The
most likely reason for this difference is given by Fig. 14, which
showns the percentage of images for the horizontal gaze directions
for the training samples of MPIIGaze+ and UT Multiview. As can
be seen from the figure, while UT Sub and MPIIGaze+ have the
same gaze direction distribution, UT Multiview and MPIIGaze+
differ substantially. This finding demonstrates the fundamental
shortcoming of previous works that only focused on cross-person
evaluations and thereby implicitly or explicitly assumed a single,
and thus restricted, gaze range. As such, this finding highlights
the importance not only of cross-dataset evaluations, but also of
developing methods that are robust to (potentially very) different
gaze ranges found in different settings.
5.2.2

Differences in Illumination Conditions

Illumination conditions are another important factor in unconstrained gaze estimation and have been the main motivation for
using fully synthetic training data that can cover a wider range
of different illuminations [14]. The third bar in Fig. 13 (Syn Sub)
corresponds to the same fine-tuned model as GazeNet+ (syn) in
Fig. 11, but with the gaze range restricted to the same head pose
and gaze angle distributions as MPIIGaze+. The fourth bar in
Fig. 13 (MPIIGaze+ (cross-person)) shows the results of withindataset cross-person evaluation on MPIIGaze+. For the second to
the fourth bar in Fig. 13, the training data has nearly the same
head angle and gaze direction range. The only difference is in

10
20
UT
UT Sub
MPIIGaze+

Mean error [degrees]

Mean error [degrees]

20

15

10

5

0
-46.0o

-34.5o

-23.0o

-11.5o

0o

11.5o

23.0o

34.5o

UT Sub
Syn. Sub
MPIIGaze+

15

10

5

0

46.0o

-150

16%
MPIIGaze+
UT

12%
8%
4%
-46.0

-34.5

-23.0

-11.5

0

11.5

-100

-50

0

50

100

150

Horizontal diﬀerence in mean intensity

23.0

34.5

46.0

Horizontal gaze direction

Fig. 14: Top: Gaze estimation error on MPIIGaze+ for the model
trained with UT Multiview, UT Multiview subset, and MPIIGaze+
for different horizontal gaze directions. Bottom: Percentage of
images for the horizontal gaze directions of MPIIGaze+ and UT.
the variation in illumination conditions in the training data. While
the use of synthetic training data results in improved performance
(from 8.0 degrees to 7.3 degrees), there is still a large gap between
cross-dataset and cross-person settings.
This tendency is further illustrated in Fig. 15, in which we
evaluated gaze estimation error with respect to lighting directions
with our GazeNet. Similar to Fig. 3, we plotted the mean gaze
estimation error according to the mean intensity difference between the left and right face region. The different colours represent
the models trained with UT Multiview subset, synthetic subset
and MPIIGaze+. They also correspond to UT Sub, Syn. Sub and
MPIIGaze+ (cross-person) in Fig. 13. The dots are averaged error
for horizontal difference in the mean intensity in the face region,
and lines are with quadratic polynomial curve fitting. Similar to
Fig. 14, the bottom of Fig. 15 shows the percentage of images for
mean greyscale intensity difference between the left and right half
of the face region. We cannot show the distribution for UT Sub
and Syn. Sub since their face images are not available. Compared
to the model trained solely on the UT subset, the model with
synthetic data shows better performance across different lighting
conditions. While there still remains an overall performance gap
from the domain-specific performance, the effect of synthetic data
is more visible in the area with extreme lighting directions.

Percentage of images

Percentage of images

Horizontal gaze direction
16%
12%
8%
4%
-150

-100

-50

0

50

100

150

Horizontal diﬀerence in mean intensity

Fig. 15: Top: Gaze estimation error on MPIIGaze+ across mean
greyscale intensity differences between the left and right half of
the face region for models trained on UT subset, SynthesEyes
subset, and MPIIGaze+. Bottom: Corresponding percentage of
images for all mean greyscale intensity differences.

(person-specific) illustrates the fundamental difficulty of personindependent gaze estimation. The difference between MPIIGaze+
(person-specific) and UT Sub (p.s.) also shows, however, that inthe-wild settings are challenging even for the person-specific case.
Fig. 16 shows the estimation error of each participant in both
cross-dataset (trained on the UT Multiview) and person-specific
(leave-one-person-out training on MPIIGaze+) settings with our
GazeNet. Bars correspond to mean error for each participant
and the error bars indicate standard deviations. Example faces
from each participant are shown at the bottom. As the figure
shows, for the cross-dataset evaluation the worst performance was
achieved for participants wearing glasses (P5, P8, and P10). This is
because the UT Multiview dataset does not include training images
covering this case, although glasses can cause noise in the eye
appearance as shown in Fig. 5a. For the person-specific evaluation,
glasses are not the biggest error source, given that corresponding
images are available in the training set. It can also be seen that
the performance differences between participants are smaller in
the person-specific evaluation. This indicates a clear need for
developing new methods that can robustly handle differences in
personal appearance for unconstrained gaze estimation.

5.3 Further Analyses
5.2.3 Differences in Personal Appearance
To further study the unconstrained gaze estimation task, we then Following the previous evaluations of unconstrained gaze estimaevaluated person-specific gaze estimation performance, i.e. where tion performance and key challenges, we now provide further
training and testing data come from the same person. The results analyses on closely related topics, specifically the influence of
of this evaluation on MPIIGaze+ are shown as the second last image resolution, the use of both eyes, and the use of head pose
bar (MPIIGaze+ (person-specific)) in Fig. 13. Since there are and pupil centre information on gaze estimation performance.
3,000 eye images for each participant in MPIIGaze+, we picked
the first 2,500 eye images for training and the rest for testing. 5.3.1 Image Resolution
Similarly, the last bar (UT Sub (p.s.)) in Fig. 13 shows the person- We first explored the influence of image resolution on gaze
specific evaluation within the UT subset, also with 2,500 eye estimation performance, since it is conceivable that this represents
images for training and 500 eye images for testing. The perfor- a challenge for unconstrained gaze estimation. To this end, we
mance gap between MPIIGaze+ (cross-person) and MPIIGaze+ evaluated the performance for the cross-dataset evaluation setting

Mean error [degrees]

11
Cross-dataset
14
Person-specific
12
10
8
6
4
2
0 P1 P2 P3 P4 P5 P6 P7 P8 P9 P10 P11 P12 P13 P14 P15

9.8 10.1 11.4 14.0

30x18

9.9 10.1 11.4 14.0

15x9

11.4 10.7 11.4 14.0

8x5

15.7 12.5 11.6 14.1
60x36 30x18 15x9

8x5

Training set

(a)

15
14
13
12
11
10

16
14
12
10
8
6
4
2
0

Mean error [degrees]

Test set

60x36

Mean error [degree]

Fig. 16: Gaze estimation error for each participant for two evaluation schemes: cross-dataset, where the model was trained on
UT Multiview and tested on MPIIGaze+, and person-specific,
where the model was trained and tested on the same person from
MPIIGaze+. Sample images are shown at the bottom.

11.7

60x36

10.9

30x18

11.4

15x9

as strong directional lighting, performance can be improved by
using information from both eyes. To study this in more detail, we
selected all images from MPIIGaze+ with two annotated eyes. We
then evaluated different means of merging information from both
eyes. The gaze estimation error when averaging across both eye
images using the model trained on the UT Multiview dataset is 9.8
degrees with a standard deviation of 2.1 degrees. The best-case
performance, i.e. always selecting the eye showing lower gaze
estimation error, is 8.4 degrees with a standard deviation of 1.9
degrees. The gap between these two bars illustrates the limitations
of the single eye-based estimation approach.
One approach to integrate estimation results from both eyes is
to geometrically merge 3D gaze vectors after the appearance-based
estimation pipeline. Given two 3D gaze vectors from both eyes, we
thus further computed the mean gaze vector originating from the
centre of both eyes. Ground-truth gaze vectors were also defined
from the same origin, and the mean error across all faces using
this approach was 7.2 degrees (standard deviation 1.4 degrees).
It can be seen that even such a simple late fusion approach
improves the estimation performance, indicating the potential of
more sophisticated methods for fusing information from both eyes.

14.0

8x5

(b)

Fig. 17: Gaze estimation error of the models trained on UT Multiview and tested on MPIIGaze+ for different image resolutions.
Test images were resized to the resolution of the training images.
(a) Combinations of different training and test set resolutions with
cell numbers indicating the average error in degrees. (b) The
mean estimation error for the models trained with certain image
resolutions across all images. Bars show the mean error across
participants in degrees; error bars indicate standard deviations.

(trained on UT Multiview and tested on MPIIGaze+) for different
training and testing resolutions with our GazeNet. Starting from
the default input resolution 60 × 36 in our model, we reduced
the size to 30 × 18, 15 × 9 and 8 × 5. We always resized
the test images according to the training resolution with bicubic
interpolation. During training, we modified the stride of the first
convolutional and max pooling layers of our GazeNet accordingly
so that the input became the same starting from the second convolutional layer, regardless of the original image input resolution.
Fig. 17a summarises the results of this evaluation with resolutions
of training images along the x-axis, and resolutions of testing
images on the y-axis. In general, if the test images have higher
resolution than the training images, higher resolution results in
better performance. Performance becomes significantly worse if
the test images are smaller than the training images.
Fig. 17b shows the mean error of these models trained on
one image resolution and tested across all testing resolutions, with
the error bar denoting the standard deviation across all images.
For the reason discussed above, the overall performance of the
highest-resolution model is worse than that of the second 30 × 18
model. This shows that higher resolution does not always mean
better performance for unconstrained gaze estimation.
5.3.2 Use of Both Eyes
Previous methods typically used a single eye image as input.
However, it is reasonable to assume that for some cases, such

5.3.3

Use of Head Pose Information

To handle arbitrary head poses in the gaze estimation task, 3D
head pose information has been used for the data normalisation
as described in Sec. 4.2. After normalisation, 2D head angle
vectors h were injected into the network as an additional geometry feature. The left side of Fig. 18 shows a comparison
between different architectures of the multi-modal CNN on the
UT Multiview dataset. We followed the same three-fold crossvalidation setting as in [6]. The best performance reported in [6]
is 6.5 degrees mean estimation error achieved by the head poseclustered Random Forest. However, when the same clustering
architecture is applied to the MnistNet (Clustered MnistNet), the
performance became worse than for the model without clustering.
In addition, our GazeNet (Clustered GazeNet) did not show any
noticeable difference with the clustering structure. This indicates
the higher learning flexibility of the CNN, which contributed to
the large performance gain in the estimation task. The role of
the additional head pose feature is also different in the two CNN
architectures. While the MnistNet architecture achieved better
performance with the help of the head pose feature, the effect of
the head pose feature became marginal in the case of the GazeNet.
Even though deeper networks like GazeNet can in general achieve
better performance, achieving better performance with shallower
networks is still important in some practical use cases where tehre
is limited computational power, such as on mobile devices.
The right side of Fig. 18 shows a comparison of models with
and without the head pose feature in the cross-dataset setting
(trained on UT and tested on MPIIGaze+). The effect of the
additional head pose feature is marginal in this case, but this is
likely because the head pose variation in the MPIIGaze dataset is
already limited to near-frontal cases. We performed an additional
experiment to compare the gaze estimation performance when
using the head pose estimated from the personal and the generic
3D face model. We achieved 9.8 degrees and 9.7 degrees for the
cross-dataset evaluation, respectively, suggesting that the generic
face model is sufficiently accurate for the gaze estimation task.

12

Clustered MnistNet
MnistNet without head pose
MnistNet
Clustered GazeNet
GazeNet without head pose
GazeNet

Mean error [degrees]

16
14
12
10
8
6
4
2
0

8.5

6.4

5.9

4.6

Within UT

4.5

4.4

13.3

13.2

9.9

9.8

Cross-dataset

Fig. 18: Gaze estimation error when using the pose-clustered
structure (Clustered MnistNet and Clustered GazeNet), without
head angle vectors h (MnistNet without head pose and GazeNet
without head pose) for within-UT and cross-dataset (trained on UT,
tested on MPIIGaze+) settings. Bars show the mean error across
participants; error bars indicate standard deviations; numbers on
the bottom are the mean estimation error in degrees.
5.3.4 Use of Pupil Centres
In GazeNet, we do not use pupil centre information as input.
Although intuitively, eye shape features, such as pupil centres, can
be a strong cue for gaze estimation, the model- or shape-based
baseline performed relatively poorly for both the cross-dataset
and cross-person evaluations. We therefore finally evaluated the
performance of GazeNet when using the pupil centre as an
additional feature for cross-person evaluation on MPIIGaze+. We
detected the pupil centre location inside the normalised eye images
using [8] and concatenated the pupil location to the geometry
feature vector (head angle h). While there was an improvement
between the models without and with the pupil centre feature,
the improvement was relatively small (from 5.4 to 5.2 degrees).
Performance improved more when using the manually annotated
pupil centres, but still not significantly (5.0 degrees).

6

D ISCUSSION

This work made an important step towards unconstrained gaze
estimation, i.e. gaze estimation from a single monocular RGB
camera without assumptions regarding users’ facial appearance,
geometric properties of the environment or the camera and user
therein. Unconstrained gaze estimation represents the practically
most relevant but also most challenging gaze estimation task. Unconstrained gaze estimation is, for example, required for secondperson gaze estimation from egocentric cameras or by a mobile
robot. Through cross-dataset evaluation on our new MPIIGaze
dataset, we demonstrated the fundamental difficulty of this task
compared to the commonly used person-independent, yet still
domain-specific, evaluation scheme. Specifically, gaze estimation
performance dropped by up to 69% (from a gaze estimation error
of 5.4 to 9.1 degrees) for the cross-dataset evaluation, as can
be seen by comparing Figs. 11 and 12. The proposed GazeNet
significantly outperformed the state of the art for both evaluation
settings and in particular when pre-trained on synthetic data (see
Fig. 11). The 3.1 degrees improvement that we achieved in the
cross-dataset evaluation corresponds to around 2.9 cm on the
laptop screen after backprojection. Performance on MPIIGaze
was generally worse than on EYEDIAP, which highlights the
difficulty but also the importance of developing and evaluating
gaze estimators on images collected in real-world environments.

We further explored key challenges of this task, including
differences in gaze ranges, illumination conditions, and personal
appearance. Previous works either implicitly or explicitly side
stepped these challenges by restricting the gaze or head pose
range [19], [76], studying a fixed illumination condition [6],
[12], [21], or by only recording for short amounts of time and
thereby limiting variations in personal appearance [13], [20].
Several recent works also did not study 3D gaze estimation but,
instead, simplified the task to regression from eye images to 2D
on-screen coordinates [17], [18]. While the 3D gaze estimation
task generalises across hardware and geometric settings and thus
facilities full comparison with other methods, the 2D task depends
on the camera-screen relationship. Our evaluations demonstrated
the fundamental shortcomings of such simplifications. They also
showed that the development of 3D gaze estimation methods that
properly handle all of these challenges, while important, remains
largely unexplored. The ultimate goal of unconstrained gaze estimation is to obtain a generic estimator that can be distributed as a
pre-trained library. While it is challenging to learn estimators that
are robust and accurate across multiple domains, an intermediate
solution might be to develop methods that adapt using domainspecific data automatically collected during deployment [2], [77].
The head angle vector plays different roles for the crossand within-dataset evaluations. It is important to note that a 3D
formulation is always required for unconstrained gaze estimation
without restricting the focal length of the camera or the pose of the
gaze target plane. 3D geometry, including the head pose, therefore
has to be handled properly for unconstrained gaze estimation – a
challenge still open at the moment. In this work we additionally
explored the use of the head angle vector as a separate input to the
CNN architecture as described in [15]. As shown in Fig. 18, while
head pose information does result in a performance improvement
for the shallower MnistNet architecture used in [15], it does not
significantly improve the performance of GazeNet.
The state-of-the-art shape-based method [8] performed poorly
in the cross-dataset evaluation, achieving only 47.1 degrees mean
error. Similarly, adding the detected pupil centres as additional
input to the CNN resulted in only a small performance improvement (see Section 5.3.4). While using eye shape and pupil centre
features is typically considered to be a promising approach, both
findings suggest that its usefulness may be limited for unconstrained gaze estimation, particularly on images collected in realworld settings – leaving aside the challenge of detecting these
features robustly and accurately on such images in the first place.

7

C ONCLUSION

In this work we made a case for unconstrained gaze estimation – a
task that, despite its scientific and practical importance, has been
simplified in several ways in prior work. To address some of these
simplifications, we presented the new MPIIGaze dataset that we
collected over several months in everyday life and that therefore
covers significant variation in eye appearance and illumination.
The dataset also offers manually annotated facial landmarks for
a large subset of images and is therefore well-suited for crossdataset evaluations. Through extensive evaluation of several stateof-the-art appearance- and model-based gaze estimation methods,
we demonstrated both the critical need for and challenges of
developing new methods for unconstrained gaze estimation. Finally, we proposed an appearance-based method based on a deep
convolutional neural network that improves performance by 22%

13

for the most challenging cross-dataset evaluation on MPIIGaze.
Taken together, our evaluations provide a detailed account of the
state of the art in appearance-based gaze estimation and guide
future research on this important computer vision task.

ACKNOWLEDGMENTS
We would like to thank Laura Sesma for her help with the dataset
handling and normalisation. This work was funded, in part, by the
Cluster of Excellence on Multimodal Computing and Interaction
(MMCI) at Saarland University, Germany, an Alexander von
Humboldt Postdoctoral Fellowship, Germany, and a JST CREST
Research Grant (JPMJCR14E1), Japan.

R EFERENCES
[1]
[2]
[3]
[4]
[5]
[6]
[7]

[8]
[9]
[10]
[11]
[12]

[13]
[14]
[15]
[16]

[17]

[18]

P. Majaranta and A. Bulling, “Eye tracking and eye-based human–
computer interaction,” in Advances in Physiological Computing, 2014,
pp. 39–65.
Y. Sugano, X. Zhang, and A. Bulling, “Aggregaze: Collective estimation
of audience attention on public displays,” in Proc. ACM Symp. User
Interface Software and Technology, 2016, pp. 821–831.
H. Sattar, S. Müller, M. Fritz, and A. Bulling, “Prediction of search
targets from fixations in open-world settings,” in Proc. IEEE Conf.
Computer Vision and Pattern Recognition, 2015, pp. 981–990.
K. A. Funes Mora and J.-M. Odobez, “Person independent 3d gaze
estimation from remote rgb-d cameras,” in Proc. IEEE Int. Conf. on
Image Processing, 2013, pp. 2787–2791.
T. Schneider, B. Schauerte, and R. Stiefelhagen, “Manifold alignment for
person independent appearance-based gaze estimation,” in Proc. IEEE
Int. Conf. Pattern Recognition, 2014, pp. 1167–1172.
Y. Sugano, Y. Matsushita, and Y. Sato, “Learning-by-synthesis for
appearance-based 3d gaze estimation,” in Proc. IEEE Conf. Computer
Vision and Pattern Recognition, 2014, pp. 1821–1828.
E. Wood, T. Baltrušaitis, L.-P. Morency, P. Robinson, and A. Bulling,
“Learning an appearance-based gaze estimator from one million synthesised images,” in Proc. ACM Symp. Eye Tracking Research Applications,
2016, pp. 131–138.
E. Wood and A. Bulling, “Eyetab: Model-based gaze estimation on
unmodified tablet computers,” in Proc. ACM Symp. on Eye Tracking
Research and Applications, 2014, pp. 207–210.
Y. Zhang, A. Bulling, and H. Gellersen, “Sideways: A gaze interface for
spontaneous interaction with situated displays,” in Proc. ACM CHI Conf.
on Human Factors in Computing Systems, 2013, pp. 851–860.
A. Torralba and A. A. Efros, “Unbiased look at dataset bias,” in Proc.
IEEE Conf. Computer Vision and Pattern Recognition, 2011, pp. 1521–
1528.
Y. Li, X. Hou, C. Koch, J. Rehg, and A. Yuille, “The secrets of salient
object segmentation,” in Proc. IEEE Conf. Computer Vision and Pattern
Recognition, 2014, pp. 280–287.
K. A. Funes Mora, F. Monay, and J.-M. Odobez, “EYEDIAP: A database
for the development and evaluation of gaze estimation algorithms from
rgb and rgb-d cameras,” in Proc. ACM Symp. on Eye Tracking Research
and Applications, 2014, pp. 255–258.
B. A. Smith, Q. Yin, S. K. Feiner, and S. K. Nayar, “Gaze locking: passive
eye contact detection for human-object interaction,” in Proc. ACM Symp.
on User Interface Software and Technology, 2013, pp. 271–280.
E. Wood, T. Baltrusaitis, X. Zhang, Y. Sugano, P. Robinson, and
A. Bulling, “Rendering of eyes for eye-shape registration and gaze
estimation,” in Proc. IEEE Int. Conf. Computer Vision, 2015.
X. Zhang, Y. Sugano, M. Fritz, and A. Bulling, “Appearance-based gaze
estimation in the wild,” in IEEE Conf. Computer Vision and Pattern
Recognition, 2015, pp. 4511–4520.
A. Villanueva, V. Ponz, L. Sesma-Sanchez, M. Ariz, S. Porta, and
R. Cabeza, “Hybrid method based on topography for robust detection
of iris center and eye corners,” Trans. Multimedia Computing, Communications, and Applications, vol. 9, no. 4, p. 25, 2013.
Q. Huang, A. Veeraraghavan, and A. Sabharwal, “Tabletgaze: dataset and
analysis for unconstrained appearance-based gaze estimation in mobile
tablets,” Machine Vision and Applications, vol. 28, no. 5, pp. 445–461,
2017.
K. Krafka, A. Khosla, P. Kellnhofer, H. Kannan, S. Bhandarkar, W. Matusik, and A. Torralba, “Eye tracking for everyone,” in Proc. IEEE Conf.
Computer Vision and Pattern Recognition, 2016, pp. 2176–2184.

[19] C. D. McMurrough, V. Metsis, J. Rich, and F. Makedon, “An eye tracking
dataset for point of gaze detection,” in Proc. ACM Symp. on Eye Tracking
Research and Applications, 2012, pp. 305–308.
[20] U. Weidenbacher, G. Layher, P.-M. Strauss, and H. Neumann, “A comprehensive head pose and gaze database,” in Proc. 3rd IET Int. Conf.
Intelligent Environments, 2007, pp. 455–458.
[21] Q. He, X. Hong, X. Chai, J. Holappa, G. Zhao, X. Chen, and
M. Pietikäinen, “Omeg: Oulu multi-pose eye gaze dataset,” in Image
Analysis, 2015, pp. 418–427.
[22] D. W. Hansen and Q. Ji, “In the eye of the beholder: A survey of
models for eyes and gaze,” IEEE Trans. Pattern Analysis and Machine
Intelligence, vol. 32, no. 3, pp. 478–500, 2010.
[23] C. H. Morimoto, A. Amir, and M. Flickner, “Detecting eye position and
gaze from a single camera and 2 light sources,” in Proc. IEEE Int. Conf.
Pattern Recognition, 2002, pp. 314–317.
[24] S.-W. Shih and J. Liu, “A novel approach to 3-d gaze tracking using
stereo cameras,” IEEE Trans. Systems, Man, and Cybernetics, Part B:
Cybernetics, vol. 34, no. 1, pp. 234–245, 2004.
[25] D. H. Yoo and M. J. Chung, “A novel non-intrusive eye gaze estimation
using cross-ratio under large head motion,” Computer Vision and Image
Understanding, vol. 98, no. 1, pp. 25–51, 2005.
[26] C. Hennessey, B. Noureddin, and P. Lawrence, “A single camera eyegaze tracking system with free head motion,” in Proc. ACM Symp. on
Eye Tracking Research and Applications, 2006, pp. 87–94.
[27] Z. Zhu and Q. Ji, “Eye gaze tracking under natural head movements,” in
Proc. IEEE Conf. Computer Vision and Pattern Recognition, 2005, pp.
918–923.
[28] Z. Zhu, Q. Ji, and K. P. Bennett, “Nonlinear eye gaze mapping function
estimation via support vector regression,” in Proc. IEEE Int. Conf.
Pattern Recognition, 2006, pp. 1132–1135.
[29] T. Ishikawa, S. Baker, I. Matthews, and T. Kanade, “Passive driver gaze
tracking with active appearance models,” in Proc. 11th World Congress
on Intelligent Transportation Systems, 2004.
[30] J. Chen and Q. Ji, “3d gaze estimation with a single camera without ir
illumination,” in Proc. IEEE Int. Conf. Pattern Recognition, 2008, pp.
1–4.
[31] 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 Proc. ACM Symp. on Eye Tracking Research and
Applications, 2008, pp. 245–250.
[32] R. Valenti, N. Sebe, and T. Gevers, “Combining head pose and eye
location information for gaze estimation,” IEEE Trans. Image Processing,
vol. 21, no. 2, pp. 802–815, 2012.
[33] L. Jianfeng and L. Shigang, “Eye-model-based gaze estimation by rgb-d
camera,” in Proc. IEEE Conf. Computer Vision and Pattern Recognition
Workshops, 2014, pp. 592–596.
[34] K. A. Funes Mora and J.-M. Odobez, “Geometric generative gaze estimation (g3e) for remote rgb-d cameras,” in Proc. IEEE Conf. Computer
Vision and Pattern Recognition, 2014, pp. 1773–1780.
[35] L. Sun, M. Song, Z. Liu, and M.-T. Sun, “Real-time gaze estimation with
online calibration,” IEEE MultiMedia, vol. 21, no. 4, pp. 28–37, 2014.
[36] S. Cristina and K. P. Camilleri, “Model-based head pose-free gaze
estimation for assistive communication,” Computer Vision and Image
Understanding, vol. 149, pp. 157–170, 2016.
[37] S. Baluja and D. Pomerleau, “Non-intrusive gaze tracking using artificial
neural networks,” DTIC Document, Tech. Rep., 1994.
[38] K.-H. Tan, D. J. Kriegman, and N. Ahuja, “Appearance-based eye gaze
estimation,” in Proc. IEEE Workshop Applications of Computer Vision,
2002, pp. 191–195.
[39] O. Williams, A. Blake, and R. Cipolla, “Sparse and semi-supervised
visual mapping with the Sˆ 3GP,” in Proc. IEEE Conf. Computer Vision
and Pattern Recognition, 2006, pp. 230–237.
[40] W. Sewell and O. Komogortsev, “Real-time eye gaze tracking with an
unmodified commodity webcam employing a neural network,” in Ext.
Abstr. ACM CHI Conf. on Human Factors in Computing Systems, 2010,
pp. 3739–3744.
[41] F. Lu, Y. Sugano, T. Okabe, and Y. Sato, “Adaptive linear regression for
appearance-based gaze estimation,” IEEE Trans. Pattern Analysis and
Machine Intelligence, vol. 36, no. 10, pp. 2033–2046, 2014.
[42] K. Liang, Y. Chahir, M. Molina, C. Tijus, and F. Jouen, “Appearancebased gaze tracking with spectral clustering and semi-supervised gaussian process regression,” in Proc. Conf. Eye Tracking South Africa, 2013,
pp. 17–23.
[43] F. Lu, T. Okabe, Y. Sugano, and Y. Sato, “Learning gaze biases with head
motion for head pose-free gaze estimation,” Image and Vision Computing,
vol. 32, no. 3, pp. 169 – 179, 2014.

14

[44] F. Lu, Y. Sugano, T. Okabe, and Y. Sato, “Head pose-free appearancebased gaze sensing via eye image synthesis,” in Proc. IEEE Int. Conf.
Pattern Recognition, 2012, pp. 1008–1011.
[45] K. A. Funes Mora and J.-M. Odobez, “Gaze estimation from multimodal
kinect data,” in IEEE Conf. Computer Vision and Pattern Recognition
Workshop, 2012, pp. 25–30.
[46] J. Choi, B. Ahn, J. Parl, and I. S. Kweon, “Appearance-based gaze
estimation using kinect,” in Proc. IEEE Conf. Ubiquitous Robots and
Ambient Intelligence, 2013, pp. 260–261.
[47] T. Gao, D. Harari, J. Tenenbaum, and S. Ullman, “When computer vision
gazes at cognition,” arXiv preprint arXiv:1412.2672, 2014.
[48] Y. Sugano, Y. Matsushita, Y. Sato, and H. Koike, “An incremental
learning method for unconstrained gaze estimation,” in Proc. European
Conf. Computer Vision, 2008, pp. 656–667.
[49] M. X. Huang, T. C. Kwok, G. Ngai, H. V. Leong, and S. C. Chan,
“Building a self-learning eye gaze model from user interaction data,” in
Proc. Int. Conf. on Multimedia, 2014, pp. 1017–1020.
[50] J. Chen and Q. Ji, “Probabilistic gaze estimation without active personal
calibration,” in Proc. IEEE Conf. Computer Vision and Pattern Recognition, 2011, pp. 609–616.
[51] Y. Sugano, Y. Matsushita, and Y. Sato, “Appearance-based gaze estimation using visual saliency,” IEEE Trans. Pattern Analysis and Machine
Intelligence, vol. 35, no. 2, pp. 329–341, 2013.
[52] F. Alnajar, T. Gevers, R. Valenti, and S. Ghebreab, “Calibration-free
gaze estimation using human gaze patterns,” in Proc. IEEE Int. Conf.
Computer Vision, 2013, pp. 137–144.
[53] P. Yu, J. Zhou, and Y. Wu, “Learning reconstruction-based remote
gaze estimation,” in Proc. IEEE Conf. Computer Vision and Pattern
Recognition, 2016, pp. 3447–3455.
[54] L. A. Jeni and J. F. Cohn, “Person-independent 3d gaze estimation using
face frontalization,” in Proc. IEEE Conf. Computer Vision and Pattern
Recognition Workshops, 2016, pp. 87–95.
[55] X. Zhang, Y. Sugano, M. Fritz, and A. Bulling, “Its written all over
your face: Full-face appearance-based gaze estimation,” in IEEE Conf. on
Computer Vision and Pattern Recognition Workshops (CVPRW), 2017.
[56] E. Wood, T. Baltrusaitis, L.-P. Morency, P. Robinson, and A. Bulling, “A
3d morphable eye region model for gaze estimation,” in Proc. European
Conf. Computer Vision, 2016, pp. 3756–3764.
[57] P. Xu, K. A. Ehinger, Y. Zhang, A. Finkelstein, S. R. Kulkarni, and
J. Xiao, “Turkergaze: Crowdsourcing saliency with webcam based eye
tracking,” arXiv preprint arXiv:1504.06755, 2015.
[58] R. Larson and M. Csikszentmihalyi, “The experience sampling method.”
New Directions for Methodology of Social & Behavioral Science, 1983.
[59] M. Kassner, W. Patera, and A. Bulling, “Pupil: an open source platform
for pervasive eye tracking and mobile gaze-based interaction,” in Proc.
Int. Conf. on Pervasive and Ubiquitous Computing: Adjunct Publication.
ACM, 2014, pp. 1151–1160.
[60] G. Bradski, “The OpenCV Library,” Dr. Dobb’s Journal of Software
Tools, 2000.
[61] R. Rodrigues, J. a. Barreto, and U. Nunes, “Camera pose estimation using
images of planar mirror reflections,” in Proc. European Conf. Computer
Vision, 2010, pp. 382–395.
[62] T. Baltrušaitis, P. Robinson, and L.-P. Morency, “Continuous conditional
neural fields for structured regression,” in Proc. European Conf. Computer Vision, 2014, pp. 593–608.
[63] M. Tonsen, X. Zhang, Y. Sugano, and A. Bulling, “Labelled pupils
in the wild: a dataset for studying pupil detection in unconstrained
environments,” in Proc. ACM Symp. Eye Tracking Research Applications,
2016, pp. 139–142.
[64] D. E. King, “Dlib-ml: A machine learning toolkit,” Journal of Machine
Learning Research, vol. 10, pp. 1755–1758, 2009.
[65] V. Lepetit, F. Moreno-Noguer, and P. Fua, “EPnP: An accurate o(n)
solution to the PnP problem,” Int. Journal of Computer Vision, vol. 81,
no. 2, pp. 155–166, 2009.
[66] K. Simonyan and A. Zisserman, “Very deep convolutional networks for
large-scale image recognition,” in Int. Conf. Learning Representations,
2015.
[67] D. Chen, S. Ren, Y. Wei, X. Cao, and J. Sun, “Joint cascade face detection
and alignment,” in Proc. European Conf. Computer Vision. Springer,
2014, pp. 109–122.
[68] Y. Sugano, Y. Matsushita, Y. Sato, and H. Koike, “Appearance-based
gaze estimation with online calibration from mouse operations,” Trans.
Human-Machine Systems, vol. 45, no. 6, pp. 750–760, 2015.
[69] J. Ngiam, A. Khosla, M. Kim, J. Nam, H. Lee, and A. Ng, “Multimodal
deep learning,” in Proc. Int. Conf. on Machine Learning, 2011, pp. 689–
696.

[70] Y. Jia, E. Shelhamer, J. Donahue, S. Karayev, J. Long, R. Girshick,
S. Guadarrama, and T. Darrell, “Caffe: Convolutional architecture for
fast feature embedding,” in Proc. Int. Conf. Multimedia, 2014, pp. 675–
678.
[71] D. Kingma and J. Ba, “Adam: A method for stochastic optimization,” The
Int. Conf. on Learning Representations (ICLR), 2015.
[72] Y. LeCun, L. Bottou, Y. Bengio, and P. Haffner, “Gradient-based learning
applied to document recognition,” Proc. of the IEEE, vol. 86, no. 11, pp.
2278–2324, 1998.
[73] R.-E. Fan, K.-W. Chang, C.-J. Hsieh, X.-R. Wang, and C.-J. Lin, “Liblinear: A library for large linear classification,” The Journal of Machine
Learning Research, vol. 9, pp. 1871–1874, 2008.
[74] F. Wilcoxon, “Individual comparisons by ranking methods,” Biometrics
bulletin, vol. 1, no. 6, pp. 80–83, 1945.
[75] K. A. F. Mora and J.-M. Odobez, “Gaze estimation in the 3d space using
rgb-d sensors-yowards head-pose and user invariance.” International
Journal of Computer Vision, vol. 118, no. 2, pp. 194–216, 2016.
[76] V. Ponz, A. Villanueva, and R. Cabeza, “Dataset for the evaluation of eye
detector for gaze estimation,” in Proc. ACM Conf. Ubiquitous Computing,
2012, pp. 681–684.
[77] X. Zhang, Y. Sugano, and A. Bulling, “Everyday eye contact detection
using unsupervised gaze target discovery,” in Proc. ACM Symp. on User
Interface Software and Technology, 2017, pp. 193–203.
Xucong Zhang is a PhD student in the Perceptual User Interfaces Group at the Max Planck Institute for Informatics, Germany. Xucong Zhang
received his BSc. from the China Agriculture University in 2007, and a MSc. from Beihang University in 2010. His research interests include computer vision, human-computer interaction, and
learning-based gaze estimation.

Yusuke Sugano is Associate Professor at the
Graduate School of Information Science and
Technology, Osaka University, Japan. Yusuke
Sugano received his BSc., MSc., and PhD degrees from the University of Tokyo, in 2005, 2007,
and 2010, respectively. He was previously a
Postdoc in the Perceptual User Interfaces Group
at the Max Planck Institute for Informatics, and
a project research associate at Institute of Industrial Science, the University of Tokyo. His
research interests include computer vision and
human-computer interaction.
Mario Fritz is Senior Researcher at the Max
Planck Institute for Informatics, Germany, where
he heads the Scalable Learning and Perception
Group. Mario Fritz received his MSc. in Computer Science from the University of ErlangenNuremberg, Germany, in 2004 and his PhD from
the Technical University of Darmstadt, Germany,
in 2008. From 2008 to 2011, he was a Postdoc
at the International Computer Science Institute
(ICSI) and UC Berkeley, US funded by a Feodor
Lynen Fellowship from the Alexander von Humboldt Foundation. His research interests include computer vision, machine learning, privacy, and natural language processing.
Andreas Bulling is head of the Perceptual User
Interfaces Group at the Max Planck Institute for
Informatics and the Cluster of Excellence on Multimodal Computing and Interaction at Saarland
University, Germany. Andreas Bulling received
his MSc. in Computer Science from the Karlsruhe Institute of Technology, Germany, in 2006
and his PhD in Information Technology and Electrical Engineering from ETH Zurich, Switzerland,
in 2010. From 2010 to 2013, he was a Feodor
Lynen and Marie Curie Research Fellow at the
University of Cambridge, UK. His research interests include computer
vision, ubiquitous computing, and human-computer interaction.

