Master Thesis - Department of Computer Science

was reported even when input image is very noisy or when portions of the images are
missing. A few NN based face recognition techniques are discussed in the following.
• Single Layer adaptive NN: A single layer adaptive NN (one for each person)
for face recognition, expression analysis and face verification was reported in
[119]. A system named Wilke, Aleksander and Stonham’s recognition devise
(WISARD) was devised. It needs typically 200-400 presentations for training
each classifier where the training patterns included translation and variation in
facial expressions. One classifier was constructed corresponding to one subject
in the database. Classification was achieved by determining the classifier that
was giving the highest response for the given input image.
• Multilayer Perceptron (MLP): Much of the present literature on face recog-
nition with neural networks present results with only a small number of classes
(often below 20). In [33] the first 50 principal components of the images were
extracted and reduced to five dimensions using autoassociative neural network.
The resulting representation was classified using a standard multilayer percep-
tron (MLP).
• Self-Organizing map (SOM): In [73] Lawrence et al. presented a hybrid
neural network solution which combines local image sampling, a self-organizing
map (SOM) and a convolutional neural network. The SOM provides a quan-
tization of the image samples into a topological space are also nearby in the
output space, thereby providing dimensionality reduction and invariance to mi-
nor changes in the image sample. The convolutional neural network provides
partial invariance to translation, rotation, scale and deformation. The recog-
nizer provides a measure of confidence in its output. The classification error
approaches zero when rejecting as few as 10% of the examples on a database of
400 images which contains a high degree of variability in expression, pose and
facial details.
• Hopfield memory model: In [29], a Hopfield memory model for the facial
images is organized and the optimal procedure of learning is determined. A
method for face recognition using Hopfield memory model combined with the
pattern matching is proposed. It shows better performance of database having
20