1. The document describes an eye tracking experiment that aimed to determine if a person's age could be inferred based solely on features extracted from their eye gaze patterns (scanpaths). 2. Over 100 subjects, ranging in age from 2 to adults, viewed images while their eye movements were tracked. Gaze-based features like fixation duration and saccade amplitude were extracted from the scanpaths. 3. A machine learning model was trained to classify subjects into different age groups based on these gaze features. The model achieved over 90% accuracy in distinguishing 2-year-olds from adults, and over 50% accuracy in a 4-class classification task.

1. Your Gaze
Betrays Your
Age!
O. Le Meur
Eye tracking
experiment
Gaze-based
features &
Learning
Results
Discussion
Your Gaze Betrays Your Age!
Olivier Le Meur, Antoine Coutrot, Zhi Liu, Pia R¨am¨a,
Adrien Le Roch and Andrea Helo
IRISA - University of Rennes 1, France
olemeur@irisa.fr
September 7, 2017
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2. Your Gaze
Betrays Your
Age!
O. Le Meur
Eye tracking
experiment
Gaze-based
features &
Learning
Results
Discussion
Motivation
Objective:
To give evidence that information derived from observers’ gaze can
be used to infer their age.
Look at the screen and I will tell you your age!
ª by using only features extracted from your
scanpaths;
ª blind to the visual content.
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3. Your Gaze
Betrays Your
Age!
O. Le Meur
Eye tracking
experiment
Gaze-based
features &
Learning
Results
Discussion
Outline
1 Eye tracking experiment
2 Gaze-based features & Learning
3 Results
4 Discussion
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4. Your Gaze
Betrays Your
Age!
O. Le Meur
Eye tracking
experiment
Gaze-based
features &
Learning
Results
Discussion
Eye tracking experiment (1/3)
Protocol
Helo, A., Pannasch, S., Sirri, L., & R¨am¨a, P. (2014). The maturation of
eye movement behavior: Scene viewing characteristics in children and
adults. Vision Research, 103, 83-91.
ª Eye movement data are collected from a total of 101 subjects:
23 adults and 78 children;
ª Four groups are made:
• 2 y.o. (18 participants);
• 4-6 y.o. (22 participants);
• 6-10 y.o. (38 participants);
• adults (22 participants) (agv ± std = 28 ± 4.4).
ª 30 color pictures taken from children’s books;
ª Viewing duration = 10s, recognition and free viewing task;
ª EyeLink 1000 Remote eye tracker system.
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5. Your Gaze
Betrays Your
Age!
O. Le Meur
Eye tracking
experiment
Gaze-based
features &
Learning
Results
Discussion
Eye tracking experiment (2/3)
Some results
ª Fixation durations decrease with age (A)
ª Saccade amplitudes are reduced with age (B)
ª Center bias is less significant for young children (C)
Consistent with many studies on visual system development and
influences of aging (see paper).
5 / 17

6. Your Gaze
Betrays Your
Age!
O. Le Meur
Eye tracking
experiment
Gaze-based
features &
Learning
Results
Discussion
Eye tracking experiment (3/3)
Some results
Joint distribution of saccade amplitudes and orientations from
childhood to adulthood:
6 / 17

7. Your Gaze
Betrays Your
Age!
O. Le Meur
Eye tracking
experiment
Gaze-based
features &
Learning
Results
Discussion
Outline
1 Eye tracking experiment
2 Gaze-based features & Learning
3 Results
4 Discussion
7 / 17

8. Your Gaze
Betrays Your
Age!
O. Le Meur
Eye tracking
experiment
Gaze-based
features &
Learning
Results
Discussion
Gaze-based features & Learning
Gaze-based features (1/1)
We extracted 70 features from the 2686 scanpaths:
ª fixation durations, saccade amplitudes, saccade velocities,
saccade durations.
For each of these features, we computed median value, the mean
value, the STD value and the first derivative (⇒ 4 × 4 features).
ª center bias (mean, STD and first derivative) (⇒ 3 features).
ª covariance matrix 5 × 5, to represent the correlation among
features (⇒ 15 features).
ª histogram of saccade slopes (⇒ 36 features).
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9. Your Gaze
Betrays Your
Age!
O. Le Meur
Eye tracking
experiment
Gaze-based
features &
Learning
Results
Discussion
Gaze-based features & Learning
Learning (1/1)
We learn a non-linear mapping of scanpath-based features to age:
ª multi-class Gentle AdaBoost algorithm.
adaptive boosting = learn several weak classifiers and combine them
to get a strong classifier.
ª the number of weak classifiers is set to 60.
ª a classification tree as a weak learner.
ª 10-fold cross validation approach.
9 / 17

10. Your Gaze
Betrays Your
Age!
O. Le Meur
Eye tracking
experiment
Gaze-based
features &
Learning
Results
Discussion
Outline
1 Eye tracking experiment
2 Gaze-based features & Learning
3 Results
4 Discussion
10 / 17

11. Your Gaze
Betrays Your
Age!
O. Le Meur
Eye tracking
experiment
Gaze-based
features &
Learning
Results
Discussion
Results
Two-class classification
ª Confusion matrix for 2 y.o vs adults (A) and chidren vs adult
(B):
• Green and red boxes show the number and percentage of correct and
incorrect classifications, respectively.
• The grey boxes provide the correct and wrong predictions per class.
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12. Your Gaze
Betrays Your
Age!
O. Le Meur
Eye tracking
experiment
Gaze-based
features &
Learning
Results
Discussion
Results
Four-class classification
ª Confusion matrix for the four classes:
• Performances are lower
than those previously
observed.
• Best performances for 2
y.o. and adult groups.
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13. Your Gaze
Betrays Your
Age!
O. Le Meur
Eye tracking
experiment
Gaze-based
features &
Learning
Results
Discussion
Results
More results
ª For 2 y.o. vs adults, the use of median, average, STD and first
derivative of fixation durations provides an accuracy of 86.1%
(compared to 93.4%). (4 parameters compared to 70)
ª For four-class classification, overall accuracy increases with the
number of fixations:
• First 6 fixations: 41.3%
• First 9 fixations: 41.5%
• First 12 fixations: 45%
• First 15 fixations: 46.3%
• All: 52.2%
ª For four-class classification, the use of saliency-based features
extracted from predicted/human saliency maps does not help improve
the performance.
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14. Your Gaze
Betrays Your
Age!
O. Le Meur
Eye tracking
experiment
Gaze-based
features &
Learning
Results
Discussion
Outline
1 Eye tracking experiment
2 Gaze-based features & Learning
3 Results
4 Discussion
14 / 17

15. Your Gaze
Betrays Your
Age!
O. Le Meur
Eye tracking
experiment
Gaze-based
features &
Learning
Results
Discussion
Discussion (1/2)
A promising approach but performance needs to
be improved...
ª by using richer gaze descriptors (e.g. based on deep features,
HMM);
ª by using more sophisticated methods, such as deep learning.
ª by reconsidering the age groups, e.g. 3 classes may be sufficient.
We need more data!!
ª We can expect that the rise of low-cost eye-tracker (e.g.
webcam-based) will soon provide largescale eye-tracking
datasets.
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16. Your Gaze
Betrays Your
Age!
O. Le Meur
Eye tracking
experiment
Gaze-based
features &
Learning
Results
Discussion
Discussion (2/2)
A side result raised by this study:
ª We need to tailor our computer vision algorithms to specific
groups of observers (age, gender, visual impaired...)
Le Meur, O. et al. (2017). Visual Attention Saccadic Models Learn to
Emulate Gaze Patterns From Childhood to Adulthood. IEEE Transactions
on Image Processing, 26(10), 4777-4789.
Krishna, O. et al. (2017). Gaze Distribution Analysis and Saliency
Prediction Across Age Groups. arXiv preprint arXiv:1705.07284.
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17. Your Gaze
Betrays Your
Age!
O. Le Meur
Eye tracking
experiment
Gaze-based
features &
Learning
Results
Discussion
Thanks for your attention!
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