Fusion of visible and infrared images using empirical mode decomposition to improve face recognition

In this effort, we propose a new image fusion technique, utilizing Empirical Mode Decomposition (EMD), for improved face recognition. EMD is a non-parametric data-driven analysis tool that decomposes non-linear non-stationary signals into Intrinsic Mode Functions (IMFs). In this method, we decompose images from different imaging modalities into their IMFs. Fusion is performed at the decomposition level and the fused IMFs are reconstructed to form the fused image. The effect of fusion on face recognition is measured by obtaining the Cumulative Match Characteristics (CMCs) between galleries and probes. Apart from conducting face recognition tests on visible and infrared raw datasets, we use datasets fused by averaging, principal component (PCA) fusion, wavelet based fusion and our method, for comparison. The face recognition rate due to EMD fused images is higher than the face recognition rates due to raw visible, raw infrared and other fused images. Examples of the fused images and illustrative CMC comparison charts are shown.