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Elsevier PPT

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PPT on HCI using Hand Gesture

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Elsevier PPT

  1. 1. Authors Presented By Ram Pratap Sharma, Ram Pratap Sharma Asst. Prof. Gyanendra Kr. Verma Department Of Computer Engineering National Institute Of Technology, Kurukshetra 1/27/2016 Presented By: Ram Pratap Sharma 1
  2. 2.  Introduction  Problem Definition  Literature Review  Methodology  Experimental Setups  Experimental Results  Conclusion  Future Work  References 1/27/2016 Presented By: Ram Pratap Sharma 2
  3. 3.  In this modern world, computers are being used by a large number of people and its demand is still growing day by day  Over the years many devices are being designed to make an easy interaction between human and computers  The increase in human–computer interaction has made user interface technology more important  The most natural and intuitive alternative to the cumbersome devices (like keyboard, mouse etc.) is to use human hand gesture for human – computer interaction  A gesture can be defined as a physical movement of the hands, arms, face and body with the intent to convey information or meaning  It can also be defined as a movement of a limb or the body as an expression of thought or feeling. (Oxford Concise Dictionary1995) 1/27/2016 Presented By: Ram Pratap Sharma 3
  4. 4. 1/27/2016 Presented By: Ram Pratap Sharma 4 To design a simple, natural, precise and robust framework i.e. a computer vision algorithm for Human Computer interaction using human hand gesture
  5. 5.  There are various bodily motion which can originate gesture but the common form of gesture origination comes from the face and hands  Contact based and vision based technologies are two main types of technologies used for robust, accurate and reliable hand gesture recognition systems  In [4], a method for detecting finger from the detected hand, can be used as a non-contact mouse, has been proposed  In [5], the author has used Lucas Kanade Pyramidical Optical Flow algorithm to detect moving hand and K-means algorithm to find center of moving hand  In [6], a fast, simple and effective gesture recognition algorithm for robot application has been presented which automatically recognizes a limited set of gestures 1/27/2016 Presented By: Ram Pratap Sharma 5
  6. 6.  In [7], a comparative analysis of different segmentation techniques and how to select an appropriate segmentation method for the system have been presented 1/27/2016 Presented By: Ram Pratap Sharma 6
  7. 7. 1/27/2016 Presented By: Ram Pratap Sharma 7 Images Acquisition Hand Segmentation (Skin-based YCbCr color space) Preprocessing (Image erosion, Hole filling) Feature Extraction (Geometrical features) Recognition
  8. 8.  Recorded each video stream of duration time approximately 10 seconds at the rate of 30 frames per seconds and at the resolution of 1280x720 using digital camera of 8 megapixel  Experiments performed in three different sessions, each having 6 different class of gesture (total images for each session = 50x6)  All the experiments were carried out in MATLAB 8.1.0 (R2013a), on a 64- bit Intel Pentium processor (2.40 GHz) with 2 GB RAM 1/27/2016 Presented By: Ram Pratap Sharma 8
  9. 9. Session 1 Session 2 Session 2  After performing the experiments we have seen that the overall accuracy of the system is 95.44%  The minimum accuracy we have achieved by class 3 gesture in session 3 due to the gesture shapes and positions 1/27/2016 Presented By: Ram Pratap Sharma 9
  10. 10.  Gesture recognition is very challenging and interesting task in terms of accuracy and usefulness in computer vision  Rotation, illumination change, background variations, and pose variation of hand makes the problem more challenging  Most important advantage is that we can efficiently interact with the application from a distance without any physical restriction  We have proposed an algorithm for recognizing the hand gestures in a constant background and good lighting conditions  This application can be very helpful for the people of developing countries and most importantly to physically challenged user  After performing the experiment we have achieved overall accuracy of approx. 95.44% which is a quite good result after comparing with other systems 1/27/2016 Presented By: Ram Pratap Sharma 10
  11. 11.  We can use some other mode of communications (like speech, head, face etc.) with hand together to acquire more accurate results and number of gestures  We need to build more robust algorithm for both recognition and detection even in the cluttered background and a normal lighting condition  We need to extend the system for some more classes of gesture as we have implemented it for only 6 classes of gesture  Also the vision-based approach has preferred to be less complex than 3D model based approach. But for the future perspective we should also have to make the progress in 3D representation of gesture 1/27/2016 Presented By: Ram Pratap Sharma 11
  12. 12. [1] Rautaray S S and Agrawal A, “Vision based hand gesture recognition for human computer interaction: A survey”, Springer Transaction on Artificial Intelligence Review, pp. 1-54, 2012. [2] Payeur P, Pasca C, Cretu A, and Petriu E M, “Intelligent Haptic Sensor System for Robotic Manipulation”, IEEE Transaction on Instrumentation and Measurement, 54(4), pp. 1583-1592, 2005. [3] Hasan M M, and Mishra P K, “Hand Gesture Modeling and Recognition using Geometric Features: A Review”, Canadian Journal on Image Processing and Computer Vision, 3(1), pp. 12-26, 2012. [4] Kang S K, Nam M Y, and Rhee P K, “Color Based Hand and Finger Detection Technology for User Interaction”, IEEE International Conference on Convergence and Hybrid Information Technology, pp. 229-236, 2008. [5] Rautaray S S and Agrawal A, “A Novel Human Computer Interface Based On Hand Gesture Recognition Using Computer Vision Techniques,” In Proceedings of ACM IITM’10, pp. 292-296, 2010. 1/27/2016 Presented By: Ram Pratap Sharma 12
  13. 13. [6] Malima A, Özgür E, and Çetin M, “A Fast Algorithm For Vision-Based Hand Gesture Recognition For Robot Control”, IEEE Signal Processing and Communications Applications, pp. 1-4, 2006. [7] Ibraheem N A, Khan R Z, and Hasan M M, “Comparative Study of Skin Color based Segmentation Techniques”, International Journal of Applied Information Systems (IJAIS), 5(10), pp. 24-38, 2013. 1/27/2016 Presented By: Ram Pratap Sharma 13
  14. 14. Questions 1/27/2016 Presented By: Ram Pratap Sharma 14
  15. 15. 1/27/2016 Presented By: Ram Pratap Sharma 15

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