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.