3 June 2011 Independent component analysis (ICA) of fused wavelet coefficients of thermal and visual images for human face recognition
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Abstract
In this paper, an image fusion technique based on weighted average of Daubechies wavelet transform (db2) coefficients from visual face image and their corresponding thermal images have been presented. Further, a comparative study has been conducted for dimensionality reduction based on Principal Component Analysis (PCA) and Independent Component Analysis (ICA). Fused images thus obtained are classified using a multi-layer perceptron (MLP). For experiments IRIS Thermal/Visual Face Database has been used. Experimental results show that the performance of ICA architecture-I is better than the other two approaches i.e. PCA and ICA-II. The average success rate for PCA, ICA-I and ICA-II are 91.13%, 94.44% and 89.72% respectively. However, approaches presented here achieves maximum success rate of 100% in some cases, especially in case of varying illumination.
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Mrinal K. Bhowmik, Debotosh Bhattacharjee, Dipak K. Basu, Mita Nasipuri, "Independent component analysis (ICA) of fused wavelet coefficients of thermal and visual images for human face recognition", Proc. SPIE 8058, Independent Component Analyses, Wavelets, Neural Networks, Biosystems, and Nanoengineering IX, 80581H (3 June 2011); doi: 10.1117/12.884455; https://doi.org/10.1117/12.884455
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