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How Old Do You Look? Inferring Your Age From Your Gaze

Abstract : The visual exploration of a scene, represented by a visual scanpath, depends on a number of factors. Among them, the age of the observer plays a significant role. For instance, young kids are making shorter saccades and longer fixations than adults. In the light of these observations, we propose a new method for inferring the age of the observer from its scanpath. The proposed method is based on a 1D CNN network which is trained by real eye tracking data collected on five age groups. In order to boost the performance, the training dataset is augmented by predicting a high number of scan-paths thanks to the use of an age-dependent computational saccadic model. The proposed method brings a new momentum in this field not only by significantly outperforming existing method but also by being robust to noise and data erasure.
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Contributor : Olivier Le Meur Connect in order to contact the contributor
Submitted on : Tuesday, December 11, 2018 - 2:03:49 PM
Last modification on : Friday, August 5, 2022 - 2:54:52 PM
Long-term archiving on: : Tuesday, March 12, 2019 - 2:28:30 PM


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  • HAL Id : hal-01951396, version 1


Tianyi Zhang, Olivier Le Meur. How Old Do You Look? Inferring Your Age From Your Gaze. International Conference on Image Processing, Oct 2018, Athènes, Greece. ⟨hal-01951396⟩



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