Recognizing people's emotions from their frame of reference is very important in our everyday life. This capacity helps us to perceive or predict the subsequent actions of people, interact effectively with them and to be sympathetic and sensitive toward them. Hence, one should expect that a machine needs to have a similar capability of understanding people's feelings in order to correctly interact with humans. Current research on emotion recognition has focused on the analysis of facial expressions. However, recognizing emotions requires also understanding the scene in which a person is immersed. The unavailability of suitable data to study such a problem has made research in emotion recognition in context difficult. In this paper, we present the EMOTIC database (from EMOTions In Context), a database of images with people in real environments, annotated with their apparent emotions. We defined an extended list of 26 emotion categories to annotate the images, and combined these annotations with three common continuous dimensions: Valence, Arousal, and Dominance. Images in the database are annotated using the Amazon Mechanical Turk (AMT) platform. The resulting set contains 18,313 images with 23,788 annotated people. The goal of this paper is to present the EMOTIC database, detailing how it was created and the information available. We expect this dataset can help to open up new horizons on creating systems able of recognizing rich information about people's apparent emotional states.