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Proceedings Paper

Independent component analysis (ICA) of fused wavelet coefficients of thermal and visual images for human face recognition
Author(s): Mrinal K. Bhowmik; Debotosh Bhattacharjee; Dipak K. Basu; Mita Nasipuri
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Paper 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.

Paper Details

Date Published: 4 June 2011
PDF: 10 pages
Proc. SPIE 8058, Independent Component Analyses, Wavelets, Neural Networks, Biosystems, and Nanoengineering IX, 80581H (4 June 2011); doi: 10.1117/12.884455
Show Author Affiliations
Mrinal K. Bhowmik, Tripura Univ. (India)
Debotosh Bhattacharjee, Jadavpur Univ. (India)
Dipak K. Basu, Jadavpur Univ. (India)
Mita Nasipuri, Jadavpur Univ. (India)


Published in SPIE Proceedings Vol. 8058:
Independent Component Analyses, Wavelets, Neural Networks, Biosystems, and Nanoengineering IX
Harold Szu, Editor(s)

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