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Artificial Neural Network for hand Gesture recognition


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Artificial Neural Network for hand Gesture recognition

introduction to ANN algorithm

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Artificial Neural Network for hand Gesture recognition

  1. 1. Main Project Seminar ARTIFICIAL NEURAL NETWORK APPROACH FOR HAND GESTURE RECOGNITION Project : Hand Gesture Recognition Robotic Arm Guide - Jaison Varghese John By - D.Vigneshwer ,S7 EC Alpha
  2. 2. Introduction • Gesture recognition is an important for developing alternative human-computer interaction modalities. • Artificial Neural networks are flexible in a changing environment. • This presentation gives the overview of ANN for gesture recognition. • It also describes the process of gesture recognition using ANN.
  3. 3. Artificial Neural Networks • Neural nets represent an approach to Artificial Intelligence that attempts to model the human brain.
  4. 4. Gesture Recognition Using Artificial Neural Networks • ANN is an adaptive system that changes its structure based on external or internal information that flows through the network during the learning phase. • The utility of artificial neural network models lies in the fact that they can be used to infer a function from observations.
  5. 5. Method • In MATLAB, Feedforword and Backpropogation algorithms are used for gesture recognition. • Backpropagation is a supervised learning technique used for training artificial neural networks. • Feed-forward normally refers to a multi-layer perceptron network in which the outputs from all neurons go to following but not preceding layers, so there are no feedback loops.
  6. 6. Figures • Backpropagation Network • A simple feed-forward Neural Net
  7. 7. Algorithm (1) Initialize the weights in the network (often randomly) (2) Repeat for each example in the training set do = neural-net-output (network, e); forward pass (3) T = teacher output for e (4) Calculate error (T - O) at the output units (5) Compute delta_wi for all weights from hidden layer to output layer backward pass. (6) Compute delta_wi for all weights from input layer to hidden layer backward pass continued. (7) Update the weights in the network end (8) until all examples classified correctly or stopping criterion satisfied (9) Return (network)
  8. 8. Steps • Step1- The first thing for the program to do is to read the image database. • Step2- Resize all the images that were read in Step1 to 150x140 pixels. This size seems the optimal for offering enough detail while keeping the processing time low. • Step3 - Next thing to do is to find the edges. For this two filters were used. • Step 4 - Dividing the two resulting matrices (images) dx and dy element by element and then taking the atan (tan−1). This will give the gradient orientation. Figure . X-Y filters
  9. 9. Step 5 - Then the MATLAB function im2col is called to rearrange the image blocks into columns. Step 6 - Converting the column matrix with the radian values to degrees. Fig (a) Before Histogram Equalization Fig (b) After Histogram Equalization Figure . Histogram of images
  10. 10. Relation With Project The classification process for system control Gesture Recognition System
  11. 11. Advantages: • A neural network can perform tasks that a linear program cannot. • When an element of the neural network fails, it can continue without any problem by their parallel nature. • A neural network learns and does not need to be reprogrammed. • It can be implemented in any application and without any problem. Disadvantages: • The neural network needs training to operate. • Requires high processing time for large neural networks.
  12. 12. Application • Character Recognition • Image Compression • Stock Market Prediction • Medical Applications • Security Applications • Gesture recognition • Pattern recognition
  13. 13. Conclusion • Human hand gestures provide the most important means for non-verbal interaction among people. • At present, artificial neural networks are emerging as the technology of choice for many applications, such as pattern recognition, gesture recognition, prediction, system identification, and control. • ANN provides good and powerful solution for gesture recognition in MATLAB. • The ability of neural nets to generalize makes them a natural for gesture recognition.
  14. 14. References • Sebastian Marcel, Oliver Bernier, Jean Emmanuel Viallet and Daniel Collobert. (2000). “Hand Gesture Recognition using Input –Output Hidden Markov Models”, Proc. of the Fourth IEEE International Conference on Automatic Face and Gesture Recognition, pp.456 - 461. • Xia Liu and Kikuo Fujimura. (2004). “Hand Gesture Recognition using Depth Data”, Proc. of the Sixth IEEE International conference on automatic Face and Gesture Recognition, pp. 529- 534.
  15. 15. Thank You