  Eating habits are learned throughout the early stages of our lives. However,
it is not easy to be aware of how our food-related routine affects our healthy
living. In this work, we address the unsupervised discovery of nutritional
habits from egocentric photo-streams. We build a food-related behavioural
pattern discovery model, which discloses nutritional routines from the
activities performed throughout the days. To do so, we rely on
Dynamic-Time-Warping for the evaluation of similarity among the collected days.
Within this framework, we present a simple, but robust and fast novel
classification pipeline that outperforms the state-of-the-art on food-related
image classification with a weighted accuracy and F-score of 70% and 63%,
respectively. Later, we identify days composed of nutritional activities that
do not describe the habits of the person as anomalies in the daily life of the
user with the Isolation Forest method. Furthermore, we show an application for
the identification of food-related scenes when the camera wearer eats in
isolation. Results have shown the good performance of the proposed model and
its relevance to visualize the nutritional habits of individuals.
