• 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