TECPRO

In face expression recognition projects the artificial neural network is used to recognize the face expression with back propagation algorithm. The powerful side of this new tool is its ability to solve problems that are very hard to be solved by traditional computing methods. (e.g. by algorithms). However, ANNs are, in some way, much more powerful because they can solve problems that not exactly know how to solve. Back Propagation ANNs is the most commonly used, as it is very simple to implement and effective. In this work it deals with Back Propagation ANNs.

Back Propagation ANNs contain one or more layers each of which are linked to the next layer. The first layer is called “input layer” which meets the initial input (e.g. pixels from a face expression image) and so do the last one “output layer” which usually holds input’s identifier (e.g. name of the input letter). The layers between input and output layers are called “hidden layer(s)” which only propagates previous layer’s outputs to the next layer and [back] propagates the following layer’s error to the previous layer. The connection between the artificial and the real thing is also investigated and explained. Finally, the mathematical models involved are presented and demonstrated. Our organization offer good assistance so that student can successfully complete their face expression analysis project and thesis.

D.Prabhu