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Opengazer: Gesture switch, Ticker & Head tracking
                                        Emli-Mari Nel, Patrick Welche
                                              Department of Physics
                                             University of Cambridge

 Overview                                                    Ticker
 ●   Process data from webcam to communicate:                ● Each voice reads a part of the alphabet.
                                                             ● For example, Cheerful Charlie reads “m”- “q”.

                             * £30, USB connection           ● Say “yes” when you hear the letter you want to write.

                             * Automatic gain control        ● Select a word in dictionary based on probability.

                             * 640x480 @ 30fps               ● Update click distribution.

                             * Easy to mount                 ● High speed:

                                                               ● Multiple voices/channels & tolerance to noise.

                                                             ● High noise tolerance:
 ●   Generate switch events from facial gestures:              ● Click distribution

                                                               ● Soft decisions on letter selections (probabilities)


                 →Laptop→YES→Text-entry method                 ● Random alphabet sequence.




 ●   Ticker:
     ● An audio text-entry method for single-switch users

     ● Can cope with noise: Imperfect switch, user delays


      “a” ? →YES (smile) →Increase probability of “a”

 ●
     Head translations→Mouse position→Dasher:
                                                                 Head tracker



                                                             Head tracker
                                                             ● Based on Viola-Jones face detector.
 ●Components of larger system called Opengazer               ● Use rectangular features.

 (gaze tracking) which is not complete yet.                  ● Find most probable location where face is.
 ● Help severely disabled users, e.g., with locked-in
                                                             ● Control mouse based on head translations.

 syndrome, to communicate automatically.

 Gesture switch
 Training:
       Make gesture three times
 Testing:
      Automatically detect gesture → switch event.
 Results:
         Robust eye-brow/smile detector:
         (outperforms EMG switch),
         Currently making robust for eye-motions.
 Method:
     ● Motion-based features.
     ● Quantify gesture: Sequence of images, where          Acknowledgments
       some pixels are more probable to be on than off.     We would like to thank David MacKay for his guidance, Mick
     ●
       Example:                                             Donegan for his assistance to visit users who can benefit from
       ● Face rectangle, 60x60.                             this research, and the Gatsby Charitable Foundation for
       ● Red (more probable),
                                                            previously funding this project.
       ● Eye brow raise = yes.
                                                            References
                                                            [1] http://www.inference.phy.cam.ac.uk/opengazer/
                                                            [2] http://www.inference.phy.cam.ac.uk/dasher/



www
                        www.aegis-project.eu

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Poster Aegis poster opgengazer

  • 1. Opengazer: Gesture switch, Ticker & Head tracking Emli-Mari Nel, Patrick Welche Department of Physics University of Cambridge Overview Ticker ● Process data from webcam to communicate: ● Each voice reads a part of the alphabet. ● For example, Cheerful Charlie reads “m”- “q”. * £30, USB connection ● Say “yes” when you hear the letter you want to write. * Automatic gain control ● Select a word in dictionary based on probability. * 640x480 @ 30fps ● Update click distribution. * Easy to mount ● High speed: ● Multiple voices/channels & tolerance to noise. ● High noise tolerance: ● Generate switch events from facial gestures: ● Click distribution ● Soft decisions on letter selections (probabilities) →Laptop→YES→Text-entry method ● Random alphabet sequence. ● Ticker: ● An audio text-entry method for single-switch users ● Can cope with noise: Imperfect switch, user delays “a” ? →YES (smile) →Increase probability of “a” ● Head translations→Mouse position→Dasher: Head tracker Head tracker ● Based on Viola-Jones face detector. ●Components of larger system called Opengazer ● Use rectangular features. (gaze tracking) which is not complete yet. ● Find most probable location where face is. ● Help severely disabled users, e.g., with locked-in ● Control mouse based on head translations. syndrome, to communicate automatically. Gesture switch Training: Make gesture three times Testing: Automatically detect gesture → switch event. Results: Robust eye-brow/smile detector: (outperforms EMG switch), Currently making robust for eye-motions. Method: ● Motion-based features. ● Quantify gesture: Sequence of images, where Acknowledgments some pixels are more probable to be on than off. We would like to thank David MacKay for his guidance, Mick ● Example: Donegan for his assistance to visit users who can benefit from ● Face rectangle, 60x60. this research, and the Gatsby Charitable Foundation for ● Red (more probable), previously funding this project. ● Eye brow raise = yes. References [1] http://www.inference.phy.cam.ac.uk/opengazer/ [2] http://www.inference.phy.cam.ac.uk/dasher/ www www.aegis-project.eu