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Finger Gesture Recognition through Sweep Sensor

Finger Gesture Recognition through Sweep Sensor. Pong C Yuen 1 , W W Zou 1 , S B Zhang 1 , Kelvin K F Wong 2 and Hoson H S Lam 2 1 Department of Computer Science Hong Kong Baptist University 2 World Fair International Ltd. Outline. Motivations Design Criteria Proposed Method

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Finger Gesture Recognition through Sweep Sensor

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  1. Finger Gesture Recognition through Sweep Sensor Pong C Yuen1, W W Zou1, S B Zhang1, Kelvin K F Wong2 and Hoson H S Lam2 1Department of Computer Science Hong Kong Baptist University 2World Fair International Ltd

  2. Outline • Motivations • Design Criteria • Proposed Method • Experimental results • Conclusions

  3. Motivations Vision-based interface Sensor-based interface • Insert some images using face, expression, body movement… Common objective: natural input to replace traditional physical input devices There should be a video about the body movement interface http://www.fitbuff.com/wp-content/uploads/2007/10/wii-fitness.jpg http://www.blogcdn.com/www.tuaw.com/media/2008/11/mac-101_-multi-touch-tips.jpg

  4. Motivations • While many sensor-based gesture input have been developed, there is no algorithm/system using sweep sensor • Why Sweep Sensor? • low cost • No latency problem (fingerprint recognition) • popularity

  5. Design Criteria • User friendliness • easily performed by a user. • intuitive and easy to understand. • User independent • Generic for all users. • Robustness • diversity of patterns captured. • Efficiency • Real-time application • mobile devices

  6. Segmentation • Segmentation • Segmentation Proposed Method • Input image • Input image • Input image noise reduction noise reduction noise reduction envelope enhancement envelope enhancement envelope enhancement direction estimation direction estimation direction estimation direction index D = Dleft/Dright direction index D = Dleft/Dright direction index D = Dleft/Dright input image input image input image • Feature Extraction • Feature Extraction • Feature Extraction • Classification • Classification • Classification Characteristics Formulate the noise Characteristics Formulate the noise Characteristics Formulate the noise 160 160 160 160 160 160 left right left right left right D > 0.5 D > 0.5 D > 0.5 i i i 140 140 140 140 140 140 left right left right left right 120 120 120 120 120 120 D < -0.5 D < -0.5 D < -0.5 No No No 100 100 100 100 100 100 80 80 80 80 80 80 yi yi yi feature vector feature vector feature vector t > t0 t > t0 t > t0 60 60 60 60 60 60 envelope envelope envelope 40 40 40 40 40 40 left tick right tick left tick right tick left tick right tick D > 1.3 D > 1.3 D > 1.3 20 20 20 20 20 20 y y y Yes Yes Yes left tick right tick left tick right tick left tick right tick 0 0 0 0 0 0 0 0 0 0 0 0 40 40 40 40 40 40 80 80 80 80 80 80 120 120 120 120 120 120 200 200 200 200 200 200 160 160 160 160 160 160 D > 1/1.3 D > 1/1.3 D > 1/1.3

  7. Input Image Characteristics • Different sensor characteristics • Noise level

  8. Segmentation • Owing to different sensor characteristics, the gesture images obtained, even the gesture is the same, will be different • Segmentation by estimating the sweeping time noise reduction vertical gradient thresholding horizontal projection Figure 2. The block diagram of feature extraction

  9. Segmentation (cont.) blank part noise reduction vertical gradient TH thresholding horizontal projection sweeping part

  10. Feature Extraction • Time information t (sweeping time) • Finger motion information d (direction) • Left and right • Left diagonal and right diagonal

  11. Feature Extraction (left / right) noise reduction Left Right direction estimation direction index D = Pleft-Pright input image direction enhancement A B i-th fingerprint texture C D

  12. Feature Extraction (left tick / right tick) noise reduction envelope enhancement direction estimation direction index D = Dleft/Dright input image 160 160 i 140 140 120 120 100 100 80 80 yi 60 60 envelope 40 40 y 20 20 0 0 0 0 40 40 80 80 120 120 200 200 160 160

  13. Classification • A very simple rule based on a combination of movement • Classification tree (decision tree) left right D > 0.5 left right D < -0.5 No t > t0 feature vector left tick right tick D > 1.3 Yes left tick right tick D > 1/1.3

  14. Designed Gestures Left and Right Left tick and Right tick

  15. Experimental Results • 2 testing groups • 3 technical users – Engineers, and technical managers, research staff (95.0%) • 3 Non-technical users – secretary, clerk (86.87%) • Test on different sensors • 4 different sensors manufacture at different period of time

  16. Experimental Results • Evaluation interface There should be a video here

  17. Experimental Results Results by 3 non-technical staff with 4 different sensors

  18. Experimental Results • Integrated application with an image viewer There should be a video here

  19. Conclusions

  20. Thank You

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