ICIP 2006, Atlanta, GA
 

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Paper Detail

Paper:TP-P6.4
Session:Face Recognition
Time:Tuesday, October 10, 14:20 - 17:00
Presentation: Poster
Topic: Biometrics: Face detection, recognition and classification
Title: FUSION OF VISIBLE AND INFRARED IMAGES USING EMPIRICAL MODE DECOMPOSITION TO IMPROVE FACE RECOGNITION
Authors: Harishwaran Hariharan; University of Tennessee 
 Andreas Koschan; University of Tennessee 
 Besma Abidi; University of Tennessee 
 Andrei Gribok; University of Tennessee 
 Mongi Abidi; University of Tennessee 
Abstract: 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. Index Terms—Face recognition, image analysis