  This paper demonstrates two different fusion techniques at two different
levels of a human face recognition process. The first one is called data fusion
at lower level and the second one is the decision fusion towards the end of the
recognition process. At first a data fusion is applied on visual and
corresponding thermal images to generate fused image. Data fusion is
implemented in the wavelet domain after decomposing the images through
Daubechies wavelet coefficients (db2). During the data fusion maximum of
approximate and other three details coefficients are merged together. After
that Principle Component Analysis (PCA) is applied over the fused coefficients
and finally two different artificial neural networks namely Multilayer
Perceptron(MLP) and Radial Basis Function(RBF) networks have been used
separately to classify the images. After that, for decision fusion based
decisions from both the classifiers are combined together using Bayesian
formulation. For experiments, IRIS thermal/visible Face Database has been used.
Experimental results show that the performance of multiple classifier system
along with decision fusion works well over the single classifier system.
