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Your gaze betrays your age

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Visual attention networks are so pervasive in the human brain that eye movements carry a wealth of information that can be exploited for many purposes. In this paper, we present evidence that information derived from observers' gaze can be used to infer their age. This is the first study showing that simple features extracted from the ordered sequence of fixations and saccades allow us to predict the age of an observer. Eye movements of 101 participants split into 4 age groups (adults, 6-10 year-old, 4-6 year-old and 2 year-old) were recorded while exploring static images. The analysis of observers' gaze provides evidence of age-related differences in viewing patterns. Therefore, we extract from the scanpaths several features, including fixation durations and saccade amplitudes, and learn a direct mapping from those features to age using Gentle AdaBoost classifiers. Experimental results show that the proposed image-blind method succeeds in predicting the age of the observer up to 92% (2y.o. vs adults) of the time.

Publié dans : Sciences
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Your gaze betrays your age

  1. 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 1 / 17
  2. 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. 2 / 17
  3. 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 3 / 17
  4. 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. 4 / 17
  5. 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. 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. 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. 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). 8 / 17
  9. 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. 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. 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. 11 / 17
  12. 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. 12 / 17
  13. 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. 13 / 17
  14. 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. 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. 15 / 17
  16. 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. 16 / 17
  17. 17. Your Gaze Betrays Your Age! O. Le Meur Eye tracking experiment Gaze-based features & Learning Results Discussion Thanks for your attention! 17 / 17

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