  Recognition of intentions is a subconscious cognitive process vital to human
communication. This skill enables anticipation and increases the quality of
interactions between humans. Within the context of engagement, non-verbal
signals are used to communicate the intention of starting the interaction with
a partner. In this paper, we investigated methods to detect these signals in
order to allow a robot to know when it is about to be addressed. Originality of
our approach resides in taking inspiration from social and cognitive sciences
to perform our perception task. We investigate meaningful features, i.e. human
readable features, and elicit which of these are important for recognizing
someone's intention of starting an interaction. Classically, spatial
information like the human position and speed, the human-robot distance are
used to detect the engagement. Our approach integrates multimodal features
gathered using a companion robot equipped with a Kinect. The evaluation on our
corpus collected in spontaneous conditions highlights its robustness and
validates the use of such a technique in a real environment. Experimental
validation shows that multimodal features set gives better precision and recall
than using only spatial and speed features. We also demonstrate that 7 selected
features are sufficient to provide a good starting engagement detection score.
In our last investigation, we show that among our full 99 features set, the
space reduction is not a solved task. This result opens new researches
perspectives on multimodal engagement detection.
