Supplementary Material
1. Dataset Statistics
Following are the relevant data statistics:
1. Age: The curated DFW dataset has variation in subject’sage. The age ranges from 18-63 years. Table 1 depicts
the group-wise age distribution.
2. Gender Distribution: The data consists of 247 maleand 91 female subjects.
3. Daytime Distribution: 55.2% of the data is collected indaylight and 44.8% during evening with different lightsources. The daytime recording sessions were conductedduring both sunny and cloudy weathers. The recording
was performed in different places in the university resulting in variation due to different illumination sources.
Additionally, few recordings have performed at nightwith very little light source. As face detector failed to detect faces in these videos, these recordings are discarded
from the data.
4. Duration: The average length of a subject’s recording
session is 15-20 sec. The data was recorded over one andhalf month time span. 5) Face Size: The average facesize in the dataset is 179 × 179 (in pixel). To measure
the face size, Dlib face detection library [7] is used.
5. Other Attributes: Specular reflection add more challenge to any gaze estimation data. In DGW data, 30.17%of the subjects have prescribed spectacles. The subjects
having prescribed spectacles are requested to record datawith and without spectacle (subject to the condition if
they can). Thus, two different settings corresponding
to these subjects were recorded. Additionally, there arevariations in head pose due to sitting posture of the participants.

2. Validation of Automatic Data Annotation
Head Pose Variation. We analyse the variation in headpose values w.r.t. their zones by computing the density
based clustering [3] on the head pose information [4] (i.e.
yaw, pitch and roll). We observed that for zones 1-3, thehead-pose is mainly centrally concentrated (forming 2, 3and 2 clusters). For the remaining zones, there are morethan 5 clusters each. This experiment indicates that it may
be noisy, if the gaze data is based on head-pose only. Fig. 1
shows the clustering results of zones 2, 3, 5 and 7.
Comparison With Manual Annotation Process. For
Table 1. Age distribution in the DGW dataset.

Age Range
Subjects (in %)

18-25
61.8

26-35
28.7

36-456.7

Over 45
2.8

comparing the automatic annotation with manual annotation, expert and non-expert annotators are assigned. There
were 3 annotators (2 experts and 1 non-expert1 ) who wereassigned for this task. We asked the annotators to label
15 videos. Further, we calculate few statistics to judge thequality of labelling. 2 experts take approximately 10-15 min(on average) to annotate each video. The mean squared errors of automatic label with the 2 expert annotator’s labels
for 15 videos are 0.49 and 0.54 respectively. Similarly, themean squared error in case of non-expert annotator is 0.78.
The cohen’s kappa between the expert annotators is 0.8. At
microsecond level, there is a high probability of wrong labelling during annotation by human labellers.

3. Illumination Robust Layer
As per [11], both Lambertian and Phong models can beformulated by the following equations:
Ldif f use = Sd Ed (n.l)(1)

Ldif f use + Lspecular = Sd Ed (n.l) + Ss Es (v.r)γ(2)

In Lambertian Equation (Eq. 1), Sd is diffusion reflection coefficient; Ed denotes the diffuse lighting intensity;n corresponds to normal vector and l denotes normal vector along the direction of incoming light [11]. Similarly, inPhong Equation (Eq. 2), Ss is the specular reflection coefficient; Es denotes the specular lighting intensity; v is thenormal vector along observation direction and r is the normal vector along the reflected light. γ is a constant termedas ‘shininess constant’.

4. Data Pre-processing
After labelling the curated data, few pre-processing methods are performed to remove noise from the training data.
1) Face Detection. Dlib face detector [8] is computed with
low threshold value as there are large illumination variationsin the dataset. As a result of the low threshold value, weobserved that in few cases, the false face detection rate also
increased.
2) Optical Flow-based Face Pruning. In order to deal withthe false face detections, we compute dense optical flow [1]across the detected face frames. If two consecutive frames
have high Forbenius norm of the optical flow magnitude(i.e. above an empirically decided threshold), we discarded
the later one. An example of face pruning is shown in Fig. 2
in which the third frame is discarded due to its high Forbenius norm value. The comparison pairs are also marked in
the figure. This removes the incorrectly detected faces inthe training set.
1 Expert refers to labeler with prior labelling experience.

Figure 1. DBSCAN clustering of head pose along yaw, pitch and roll axis for zones 2, 3, 5 and 7 (left to right) respectively. Large numberof clusters within a zone, implies that we cannot only rely on head pose information for labelling the data. (best viewed in colour)

Figure 3. Lip movement effect analysis pipeline (Section ??).

Figure 2. Overview of the optical flow based pruning method. The
rejected frame is marked with a red cross.

5. Ablation Study
Eye Gaze Representation Learning. In order to analyze whether our network learnt a generalized face representation, we extracted the features from weights trainedon DGW and fine tuned for the task of eye gaze estimation. We fine-tuned the network on the Columbia gaze [10](CAVE) and TabletGaze [5] datasets. The results are shownin Table 3 and Table 2, respectively. In the case of CAVE,
our model stabilizes the standard deviation. Similarly, forTabletGaze, the fine tuned network works well. These quantitative results indicate that our proposed network has learnt
efficient representation from the DGW dataset.
Effect of Lip Movement. To check the effect of lips movement on the network, we performed the following experiTable 2. Results on Tablet Gaze with comparison to baselines [5].
Effectiveness of the learnt features from DGW dataset is demonstrated here. TG: TabletGaze, RP: Raw Pixels.

Methods
TGk-NN
RFGPR
SVR

RP
[5]
9.26
7.2
7.38
-

LoG
[5]
6.45
4.76
6.04
-

LBP
[5]
6.29
4.99
5.83
-

HoG
[5]
3.73
3.29
4.07
-

mHoG
[5]
3.69
3.17
4.11
4.07

Ours

3.77

ment: images were cropped from the eye region up to nose
tip point and the gaze zone prediction is re-trained. This resulted in drop of overall accuracy for the baseline network.
Fig. 3 shows the overall pipeline of the aforementioned experiment. It is interesting to note that the performance difference is minimal due to the effect of loss of head pose
information, when the eyes are used as input only.
Jetson Nano Experiments. It is important to note that amore complex backbone network may achieve better performance. We chose Inception-V1 due to relatively better
performance and to be able to evaluate the performance ona small platform such as the Nvidia Jetson Nano. These
networks will be expected to run on close to real-time in a
car based computer. On Nvidia Jetson Nano the best network runs at 10 Frames Per Second (FPS) with 34,532,961
parameters.

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3

Table 3. Results on the CAVE dataset (of °0 yaw angle) using angular deviation, calculated as mean error ± std. deviation (in°).

DatasetCAVE[6]x1.67 ± 1.19

[9]y3.47 ± 3.99x2.65 ± 3.96

[2]
y4.02 ± 5.82

[7] D E King. Dlib-ml: A machine learning toolkit. Journal ofMachine Learning Research, pages 1755–1758, 2009. 1[8] S Sharma, K Shanmugasundaram, and S K Ramasamy.
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IEEE Transactions on Pattern Analysis and Machine Intelligence, 41(3):611–624, 2019. 1x
1.67 ± 1.19

y1.74 ± 1.57

Oursxy2.17 ± 0.91 1.31 ± 0.73