Hierarchical Fusion of Multi Spectral Face Images for Improved ... - IIIT

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Experiments for validating the fusion algorithms are carried out for four fusion schemesdescribed in Sections 3.1, 3.2, 3.3 and 3.4:Experiment 1: Feature fusion of amplitude and phase features extracted from a singlespectrum face image.Experiment 2: Feature fusion of amplitude and phase features extracted from multispectral face images.Experiment 3: Image fusion of two face images of different spectrum.Experiment 4: Image and feature fusion of two face images of different spectrum.These experiments are performed using one training image per spectrum, e.g. one shortwaveIR and one visible face image are used for information (image or feature) fusion.The fused image or feature vector obtained is used for training the texture basedrecognition algorithm. To determine the recognition performance for combinations oflight spectrum and information fusion, the single image used for training also has similarmulti-spectral fused image characteristics. In experiments 2, 3 and 4, images from twodifferent spectrum are fused. There are six possible pairs of light spectrum combinations.1. Long-wave IR with medium-wave IR (L-M)2. Long-wave IR with short-wave IR (L-S)3. Long-wave IR with visible (L-V)4. Medium-wave IR with short-wave IR (M-S)5. Medium-wave IR with visible (M-V)6. Short-wave IR with visible (S-V)26
Table 2 summarizes the equal error rates for these experiments. For cases when thefeatures are extracted and fused from only one image (experiment 1), short-wave IR givesthe best results with EER of 4.01% followed by visible spectrum face image, with theEER of 4.20%. The performance of long-wave and medium-wave IR are close to eachother with an EER of 8.26% and 7.88% respectively. Feature fusion with multi spectralface images (experiment 2) performs best for the combination of short-wave IR andvisible spectrum, with an EER of 3.27%. Further, for fusion of information from twoimages (experiments 3 and 4), the combination of short-wave IR and visible spectrumperforms best with the EER ranging from 2.86% to 3.59%.Table 2EER (%) for image and feature fusion algorithms using Equinox databaseAlgorithmsFeature Fusion –Single SpectrumFeature Fusion –Multi SpectralImage Fusion(Amplitude Features)Image Fusion(Phase Features)Image and FeatureFusionEER of Single Spectrum ImagesEER of Fused ImagesLWIR MWIR SWIR Visible L-M L-S L-V M-S M-V S-V8.26 7.88 4.01 4.20 -- -- -- -- -- ---- -- -- -- 3.99 3.81 3.94 3.62 3.78 3.27-- -- -- -- 6.23 5.33 5.26 3.98 4.72 3.59-- -- -- -- 5.95 4.01 4.91 3.83 4.68 3.51-- -- -- -- 3.21 3.16 3.18 3.01 3.09 2.86For the fusion experiments, amplitude and phase features extracted from the short-waveIR and visible spectrum gives the best results. Visible spectrum images provide detailedinformation of the features which leads to comparatively high intraclass variability andhence high false rejection rate; whereas the short-wave IR is associated with reflectedradiation and is reflective in nature. This radiation is for the most part invisible to thehuman eye but is useful for recognition. Differences in appearance between the visible27
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Page 6 and 7: Inverse Fourier Transform (IFT) of
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Page 10 and 11: training data is used, it could lea
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Page 14 and 15: MSF⎪⎧MS= ⎨⎪⎩ MSIFIFMS+ 1F
Page 16 and 17: Since the visible and IR imaging de
Page 18 and 19: DWTLLLHHLHHVisibleFace ImageImageFu
Page 20 and 21: Short Wave IRFace ImageAmplitudeFea
Page 22 and 23: Figs. 8 and 9 show the Receiver-Ope
Page 24 and 25: In specific applications such as la
Page 28 and 29: and short-wave IR are due to the re
Page 30 and 31: show that the proposed image and fe
Page 32: [19] F. Prokoski, History, Current

Hierarchical Fusion of Multi Spectral Face Images for Improved ... - IIIT

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