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

Quotient Based Multiresolution Image Fusion of Thermal and Visual Images Using Daubechies Wavelet Transform for Human Face Recognition