  This paper investigates the multiresolution level-1 and level-2 Quotient
based Fusion of thermal and visual images. In the proposed system, the method-1
namely "Decompose then Quotient Fuse Level-1" and the method-2 namely
"Decompose-Reconstruct then Quotient Fuse Level-2" both work on wavelet
transformations of the visual and thermal face images. The wavelet transform is
well-suited to manage different image resolution and allows the image
decomposition in different kinds of coefficients, while preserving the image
information without any loss. This approach is based on a definition of an
illumination invariant signature image which enables an analytic generation of
the image space with varying illumination. The quotient fused images are passed
through Principal Component Analysis (PCA) for dimension reduction and then
those images are classified using a multi-layer perceptron (MLP). The
performances of both the methods have been evaluated using OTCBVS and IRIS
databases. All the different classes have been tested separately, among them
the maximum recognition result is 100%.
