Comparison of neural networks and conventional techniques for feature location in facial images

This paper compares two artificial neural network (ANN) techniques for facial feature location with an algorithmic method, namely template matching. All three techniques work on windowed data from 128×128 pixel facial images. The ANN techniques used are the multilayer perceptron, and a method using a Kohonen self-organising feature map to classify input patterns and a multilayer perceptron to interpret the output of the Kohonen network. The data used to train the ANNs is described along with the learning parameters and conditions used. The effect of normalising the input data is described. The results show that ANN techniques can equal and in some cases better the performance of template matching for facial feature location