DocumentCode :
68082
Title :
User Identification for Home Entertainment Based on Free-Air Hand Motion Signatures
Author :
Mendels, Omri ; Stern, Helman ; Berman, Sigal
Author_Institution :
Dept. of Ind. Eng. & Manage., Ben-Gurion Univ. of the Negev, Beer-Sheva, Israel
Volume :
44
Issue :
11
fYear :
2014
fDate :
Nov. 2014
Firstpage :
1461
Lastpage :
1473
Abstract :
A user identification system based on free-air hand signature-gestures acquired with a 3-D camera was developed. In the system, users interactively defined their own motion signatures by demonstration and trained the system by performing a plurality of signatures. The system identifies the user by comparing the distances of a sample to other signatures. The distance metric is learned by using neighborhood components analysis. An interactive enrollment algorithm which uses sequential clustering, and allows the system to advise the user during signature selection and system training was developed. Four validation tests were conducted: 1) all users using a single predefined signature-gesture (independent); 2) each user using a personal signature-gesture (dependent); 3) copycat tests for examining robustness against forgery; and 4) operation of the interactive enrollment system. For identifying a single user out of user cohorts of three to seven people, the independent system had average accuracies of 91%-77% depending on cohort size and signature shape. Higher average accuracies of 98%-92% were obtained for the dependent system. In the forgery tests, users with high signature variability over time were susceptible to forgeries, but users with low signature variability obtained a low equal error rate of 0.083. The interactive enrollment system significantly improved recognition accuracy. The proposed system can be integrated into gesture-based home entertainment systems and used for interface customization, content adaptation, and parental control. User attitudes toward the system within this context were assessed using the widely accepted technology acceptance model. Based on 69 responders, the results indicated a positive user attitude toward the system and a high intention to use it. The users expressed a preference for personalized gestures, a finding that indicates the importance of the interactive enrollment module for personalizing the signature-gestures.
Keywords :
entertainment; gesture recognition; home computing; image motion analysis; pattern clustering; 3D camera; content adaptation; copycat tests; distance metric; forgery tests; free-air hand motion signatures; free-air hand signature-gestures; gesture-based home entertainment systems; interactive enrollment algorithm; interface customization; neighborhood components analysis; parental control; personal signature-gesture; personalized gestures; sequential clustering; signature selection; signature-gesture personalization; single predefined signature-gesture; system training; user identification system; Accuracy; Covariance matrices; Optimization; Shape; Training; Trajectory; Vectors; Clustering methods; free-air hand signature gestures; human-machine interaction; human??machine interaction; motion analysis; user identification;
fLanguage :
English
Journal_Title :
Systems, Man, and Cybernetics: Systems, IEEE Transactions on
Publisher :
ieee
ISSN :
2168-2216
Type :
jour
DOI :
10.1109/TSMC.2014.2329652
Filename :
6842691
Link To Document :
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