
handle: 20.500.14243/264544
The paper describes a system for the human machine interaction that is able to identify users according how she looks at the monitor while using a given interface. The system does not need invasive measurements that could limit the naturalness of her actions and detects the eyes movement from the estimation provided by a kinect camera. The proposed approach clusters the sequences of user gaze on the screen characterizing the user identity according the particular pattern his/her gaze follows. The possibility of identify people through gaze movement introduces a new perspective on human-machine interaction. For example, a user can obtain different contents according his recorded preferences and a software can modify its interface to meet the preferences of a given user. © 2013 IEEE.
Human-Machine Interface, Entropy, Clustering, Mean Shift
Human-Machine Interface, Entropy, Clustering, Mean Shift
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