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PRESENTATION

PRESENTATION . ON. “ GESTURE RECOGNITION ”. SUBMITTED BY :. SUBMITTED TO:. PRESENTATION OUTLINE :. INTRODUCTION STEPS OF GESTURE RECOGNITION TRACKING TECHNOLOGIES SPEECH WITH GESTURE APPLICATIONS. WHAT ARE GESTURES ???. Gestures are expressive, meaningful body motions –

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PRESENTATION

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  1. PRESENTATION ON “ GESTURE RECOGNITION ” SUBMITTEDBY: SUBMITTED TO:

  2. PRESENTATION OUTLINE : • INTRODUCTION • STEPS OF GESTURE RECOGNITION • TRACKING TECHNOLOGIES • SPEECH WITH GESTURE • APPLICATIONS

  3. WHAT ARE GESTURES ??? Gestures are expressive, meaningful body motions – i.e., physical movements of the fingers, hands, arms, head, face, or body with the intent to convey information or interact with the environment.

  4. GESTURE RECOGNITION: • Mood and emotion are • expressed by body • language. • Facial expressions. • Tone of voice. • Allows computers to interact • with human beings in a • more natural way. • Allows control without • having to touch the device.

  5. Human Computer Interface using Gesture • Replace mouse and keyboard. • Pointing gestures. • Navigate in a virtual environment. • Pick up and manipulate virtual objects. • Interact with a 3D world. • No physical contact with computer. • Communicate at a distance.

  6. STEPS OF GESTURE RECOGNITION:

  7. TRACKING TECHNOLOGIES: DATAGLOVES / CYBERGLOVES - Use of gloves equipped with sensors. - Use of fiber optic cables.

  8. SIGN LANGUAGE RECOGNITION • 5000 gestures in vocabulary. • each gesture consists of a hand shape, a hand motion and a location in 3D space. A C F

  9. Datagloves

  10. THE PROCESS Colour Segment Noise Removal Scale by Area

  11. TRACKING TECHNOLOGIES: • COMPUTER-VISION TECHNOLOGY. • USE OF CAMERAS • DEPTH CAMERAS. • STEREO CAMERAS. • NORMAL CAMERAS.

  12. THE VIDEOPLACE : Here, text is entered by pointing at the character desired Here the index finger is recognized and when extended, becomes a drawing tool. Here the index fingers and thumbs of the two hands are recognized and are used to control the shape of the object being defined

  13. Yes/No? Yes/No? Yes/No? Yes/No? Y A B C

  14. Hierarchical Search • We need to search thousands of images. • How to do this efficiently? • We need to use a “coarse-to-fine”searchstrategy.

  15. Blurring Factor = 1 Original image Blurring Factor = 2 Blurring Factor = 3

  16. Multi-scale Hierarchy Factor = 3.0 Factor = 2.0 Factor = 1.0

  17. Motion Recognition Hidden Markov Model ( HMM ) --- time sequence of images modeling HMM1 (Hello) f P(f|HMM1) P(f |HMM2) HMM2 (Good) HMM3(Bad) HMM4 (House)

  18. Prediction and Tracking • Given previous frames we can predict what will happen next • Speeds up search. • occlusions -

  19. Co-articulation In fluent dialogue signs are modified by preceding and following signs. intermediate forms A B

  20. Face recognition Single pose • Standard head-and-shoulders view with uniform background • Easy to find face within image

  21. Aligning Images Alignment • Faces in the training set must be aligned with each other to remove the effects of translation, scale, rotation etc. • It is easy to find the position of the eyes and mouth and then shift and resize images so that are aligned with each other

  22. Nearest Neighbour • Once the images have been aligned you can simply search for the member of the training set which is nearest to the test image. • There are a number of measures of distance including Euclidean distance, and the cross-correlation.

  23. Principal Components Analysis • PCA reduces the number of dimensions and so the memory requirement is much reduced. • The search time is also reduced

  24. Problems with PCA • The same person may sometimes appear differently due to • Beards, moustaches • Glasses, • Makeup • These have to be represented by different ellipsoids.

  25. Facial Expressions • There are six types of facial expression • We could use PCA on the eyes and mouth – so we could have eigeneyes and eigenmouths Anger Fear Disgust Happy Sad Surprise

  26. Multiple Poses • Heads must now be aligned in 3D world space. • Classes now form trajectories in feature space. • It becomes difficult to recognise faces because the variation due to pose is greater than the variation between people.

  27. Model-based Recognition • We can fit a model directly • to the face image • Model consists of a mesh which is matched to facial features such as the eyes, nose, mouth and edges of the face. • We use PCA to describe the parameters of the model rather than the pixels.

  28. Speech with Gesture • Voice and gesture compliment each other and form a powerful interface that either a modality alone. • Speech and gesture make a more interactive interface. • Combining gesture and voice increase recognition accuracy.

  29. MEDIA ROOM Within the media room user can use gesture ,speech ,eye movements or combination of all three. Example: One application allowed user to manage color coded ship against a map of a carribean . A user just need to point the location and need to say “create a large blue tank”. A blue tank will appear on the location. Media room

  30. Applications • Sign language recognition: gesture recognition software can transcribe the symbols represented through sign language into text. • Control through facial gestures: Controlling a computer through facial gestures is a useful application of gesture recognition for users who may not physically be able to use a mouse or keyboard. • Immersive game technology: Gestures can be used to control interactions within video games to try and make the game player's experience more interactive or immersive.

  31. A person playing game. Computer is responding as per user instruction. A girl is instructing the computer from her body movements .

  32. Applications • Virtualcontrollers: For systems where the act of finding or acquiring a physical controller could require too much time, gestures can be used as an alternative control mechanism. • Affective computing: In affective computing, gesture recognition is used in the process of identifying emotional expression through computer systems. • Remote control: Through the use of gesture recognition, “remote control with the wave of a hand” of various devices is possible. The signal must not only indicate the desired response, but also which device to be controlled.

  33. Future Work: • Occlusions (Atid). • Grammars in Irish Sign Language. • --- Sentence Recognition. • Body Language.

  34. References • Wu yang,vision based gesture recognition lecture notes in artificial intelligence 1999. • Wikipedia .

  35. THANK YOU !!!! ANY QUERIES ???

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