Artificial Neural Network for hand Gesture recognition
Main Project Seminar
ARTIFICIAL NEURAL NETWORK
APPROACH FOR HAND GESTURE
Project : Hand Gesture Recognition Robotic Arm
Guide - Jaison Varghese John
By - D.Vigneshwer ,S7 EC Alpha
• Gesture recognition is an important for
developing alternative human-computer
• Artificial Neural networks are flexible in a
• This presentation gives the overview of ANN
for gesture recognition.
• It also describes the process of gesture
recognition using ANN.
Artificial Neural Networks
• Neural nets represent an approach to Artificial
Intelligence that attempts to model the
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.
• In MATLAB, Feedforword and Backpropogation
algorithms are used for gesture recognition.
• Backpropagation is a supervised learning
technique used for training artificial neural
• 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.
• A simple feed-forward
(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
(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)
• Step1- The first thing for the program to do is to read the
• 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
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
Fig (a) Before Histogram Equalization
Fig (b) After Histogram Equalization
Figure . Histogram of images
Relation With Project
The classification process for system control
Gesture Recognition System
• A neural network can perform tasks that a linear
• When an element of the neural network fails, it
can continue without any problem by their
• A neural network learns and does not need to be
• It can be implemented in any application and
without any problem.
• The neural network needs training to operate.
• Requires high processing time for large neural
• 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.
• 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-