Wave to Me: User Identification Using Body Lengths and Natural Gestures, at CHI 2014

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We introduce a body-based identification system that leverages individual differences in body segment lengths and hand waving gesture patterns. The system identifies users based on a two-second hand waving gesture captured by a Microsoft Kinect. To evaluate our system, we collected 8640 gesture measurements from 75 participants through two lab studies and a field study. In the first lab study, we evaluated the feasibility of our concept and basic properties of features to narrow down the design space. In the second lab study, our system achieved a 1% equal error rate in user identification among seven registered users after two weeks following initial registration. We also found that our system was robust even when lower body segments could not be measured because of occlusions. In the field study, our system achieved 0.5 to 1.6% equal error rates, demonstrating that the system also works well in ecologically valid situations. Lastly, throughout the studies, our participants were positive about the system.

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Wave to Me: User Identification Using Body Lengths and Natural Gestures, at CHI 2014

  1. 1. Wave to Me: User Identification Using Body Lengths and Natural Gestures Eiji Hayashi Manuel Maas Jason Hong Human-Computer Interaction Institute Carnegie Mellon University
  2. 2. Slick user identification
  3. 3. Slick user identification with reasonable security
  4. 4. Gesture
  5. 5. EachDifferent Gesture for user
  6. 6. AllSame Gesture for user
  7. 7. 11 97% accurate
  8. 8. System Lab Study 1 (Basic Evals) Lab study 2 (Long-term Eval) Field Study
  9. 9. System Lab Study 1 (Basic Evals) Lab study 2 (Long-term Eval) Field Study
  10. 10. Body-based User Identification Overview
  11. 11. Body-based User Identification Registration
  12. 12. Body-based User Identification Registration Identification
  13. 13. Body-based User Identification Registration Identification User ID Reject
  14. 14. Kinect
  15. 15. Kinect
  16. 16. Kinect Joint Positions
  17. 17. Kinect Feature Extraction Joint Positions
  18. 18. Kinect Feature Extraction Physiological 17 body segment lengths
  19. 19. Kinect Feature Extraction Physiological 17 body segment lengths Behavioral 26 movement properties
  20. 20. Kinect Feature Extraction 43 Features
  21. 21. Kinect Feature Extraction Feature Vector
  22. 22. Kinect Feature Extraction SVM Feature Vector
  23. 23. Kinect Feature Extraction SVM Pre-Recorded Data Feature Vector
  24. 24. Kinect Feature Extraction SVM Pre-Recorded Data User ID + Confidence
  25. 25. Kinect Feature Extraction SVM Threshold Pre-Recorded Data User ID + Confidence
  26. 26. Kinect Feature Extraction SVM Threshold Pre-Recorded Data User ID or Reject
  27. 27. Errors False Acceptance Rate (FAR) Accept others as a registered user False Rejection Rate (FRR) Reject a registered user as others
  28. 28. Errors False Acceptance Rate (FAR) Accept others as a registered user False Rejection Rate (FRR) Reject a registered user as others Equal Error Rate (EER) FAR = FRR = EER
  29. 29. Errors False Acceptance Rate (FAR) Accept others as a registered user False Rejection Rate (FRR) Reject a registered user as others Equal Error Rate (EER) FAR = FRR = EER Accuracy = 1 – 2 x EER
  30. 30. Assumption There are 7 registered users in our system
  31. 31. Assumption There are 7 registered users in our system Make comparison among studies easy
  32. 32. Assumption There are 7 registered users in our system Make comparison among studies easy Be reasonable for home use
  33. 33. Assumption There are 7 registered users in our system Choose 10,000 combination of 7 participants Calculate EER over them
  34. 34. System Lab Study 1 (Basic Evals) Lab study 2 (Long-term Eval) Field Study
  35. 35. Gestures Hand Waving Come-Over One Hand Raised Making a Phone Call
  36. 36. Data Collection Hand Waving Come-Over One Hand Raised Phone Call Gesture
  37. 37. Data Collection Hand Waving Come-Over One Hand Raised Phone Call Standing Sitting Gesture Posture
  38. 38. Data Collection Hand Waving Come-Over One Hand Raised Phone Call Standing Sitting 1st Day 3 days later Gesture Posture Session
  39. 39. Data Collection Hand Waving Come-Over One Hand Raised Phone Call Standing Sitting 1st Day 3 days later Gesture Posture Session 10
  40. 40. Data Collection Hand Waving Come-Over One Hand Raised Phone Call Standing Sitting 1st Day 3 days later Gesture Posture Session 10 160 / participants
  41. 41. Participants 36 participants 14 males / 22 females 19 – 64 years old 168cm (SD=10.2) 78.0 kg (SD=22.0)
  42. 42. Gestures Hand Waving Come-Over One Hand Raised Making a Phone Call
  43. 43. Using Either Gesture or Lengths Same day & posture 3 days later Different Posture Same day & posture 3 days later Different Posture Gesture Body Lengths EER [%]
  44. 44. Using Either Gesture or Lengths Same day & posture 3 days later Different Posture Same day & posture 3 days later Different Posture Gesture Body Lengths EER [%] 2.1% 0.5%
  45. 45. Using Either Gesture or Lengths Same day & posture 3 days later Different Posture Same day & posture 3 days later Different Posture Gesture Body Lengths EER [%] 11.8% 19.8%
  46. 46. Using Either Gesture or Lengths Same day & posture 3 days later Different Posture Same day & posture 3 days later Different Posture Gesture Body Lengths EER [%] 10.0% 41.5%
  47. 47. Using Both Gesture and Lengths 3 days later 3 days later Gesture Body Lengths EER [%] Both 3 days later (Standing) 3 days later (Sitting) 4.3% 6.2%
  48. 48. System Lab Study 1 (Basic Evals) Lab study 2 (Long-term Eval) Field Study
  49. 49. Data Collection Hand Waving Standing Sitting 1st Day 3 days later 1 week later 2 weeks later Gesture Posture Session 10 80 / participants
  50. 50. Participants 27 participants 20 males / 7 females 19 – 62 years old 173cm (SD=9.8) 75.1 kg (SD=21.1)
  51. 51. Long term StabilityEER[%] Days
  52. 52. Long term StabilityEER[%] Days Sitting Standing
  53. 53. Long term StabilityEER[%] Days Stable after the 3rd session
  54. 54. Training with 2 sessionsEER[%] Days
  55. 55. Training with 2 sessionsEER[%] Days Sitting Standing
  56. 56. Training with 2 sessionsEER[%] Days EER < 1%
  57. 57. System Lab Study 1 (Basic Evals) Lab study 2 (Long-term Eval) Field Study
  58. 58. Does it Work at Homes? • Collected data at participants’ living rooms • Placed a Kinect on a TV • Asked participant to behave as usual – Stand where you feel reasonable – Sit as you normally do in a living room
  59. 59. Participants 12 participants (5 house hold) 5 males / 7 females 18 – 42 years old 159.5cm (SD=11.4) 56.9 kg (SD=6.9)
  60. 60. It Worked! EER [%] Standing Sitting Standing Sitting Lab Study 2 Field Study
  61. 61. Implication Recognizing a gesture Recognizing a gesture AND a user’s identity
  62. 62. • Natural gestures + body lengths • 2 seconds of hand waving gesture Conclusion Gesture Body Lengths Proposed Scheme EER [%]
  63. 63. Wave to Me: User Identification Using Body Lengths and Natural Gestures Eiji Hayashi ehayashi@cs.cmu.edu www.cs.cmu.edu/~ehayashi/ Human-Computer Interaction Institute Carnegie Mellon University
  64. 64. Backup slides
  65. 65. # of Registered UsersEER[%] # of Registered Users
  66. 66. # of Registered UsersEER[%] # of Registered Users 2.8% 2.3% N=25
  67. 67. Open Questions Getting worse constantly? Training with two sessions?
  68. 68. Data Collection Hand Waving Standing Sitting 1st Session 2nd Session 3rd Session Gesture Posture Session 10 60 / participants
  69. 69. Yet Another Open Question Does it actually work at home?

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