Title
Person identification through entropy oriented mean shift clustering of human gaze patterns
Abstract
The paper describes a system aimed at improving the human machine interaction that is able to identify users according how she looks at the monitor. The proposed system does not need invasive measurements that could limit the naturalness of her actions. The approach, here described, detects the gaze movements on the monitor and clusters the sequences of user gaze fixation points on the screen characterizing the user according the particular patterns her gaze follows. The recognition of the user is performed through a clustering process employing the Mean-Shift algorithm and it can open new perspective in human-machine interaction. In particular, the parameters of the clustering process are tuned optimizing an entropy oriented cost function that allows an automatic selection of the best parameters setting.
Year
DOI
Venue
2017
10.1007/s11042-015-3153-9
Multimedia Tools Appl.
Keywords
Field
DocType
Biometric, Identification, Gaze detection, Depth sensors, Mean shift, Entropy
Computer vision,Pattern recognition,Gaze,Computer science,Naturalness,Artificial intelligence,Mean-shift,Biometrics,Cluster analysis,GAZE FIXATION,Human machine interaction
Journal
Volume
Issue
ISSN
76
2
1573-7721
Citations 
PageRank 
References 
2
0.36
18
Authors
3
Name
Order
Citations
PageRank
Filippo Vella113825.37
Ignazio Infantino215132.13
Giuseppe Scardino351.16