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

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DWTLLLHHLHHVisibleFace ImageImageFusionIDWTDWTLLLHHLHHFused Visibleand IR ImageMedium WaveIR Face ImageFig. 4. Multispectral image level fusion of visible and medium-wave infrared face imagesFig. 5. Decision maps for the horizontal, vertical and diagonal bandsBased on these decision maps, the detailed bands for the fused imageILH − F, IHL−Fand IHH −Fbands to generate the fused image.are generated. Inverse DWT is then applied on the four fusedIF= IDWT I , I , I , I ) (33)( LL −FLH − F HL−FHH − FTo validate the image fusion algorithm, recognition performance of the fused face imageis calculated. Amplitude and phase features are extracted from the fused image andmatched independently as described in Section 2. Face images captured using different18
light spectrum yields complementary information that is useful for identification. Forimproved recognition performance, the fusion of this distinct multi-spectral informationinto a single image requires that the same image be captured using different lightspectrum.For an image of size N x N, the computational complexity of DWT and IDWT is2⎛ N ⎞O ⎜⎟ [1]. The fusion process has a complexity of O ( N log N). Hence the overall⎝ 4 ⎠complexity of the proposed image fusion algorithm is2⎛ N⎟ ⎞O ⎜ when N > 8 . This shows⎝ 4 ⎠that the image fusion algorithm is fast and can be used for real time applications.3.4 Integration of Image Fusion and Feature FusionThe algorithms proposed for both image level fusion and feature level fusion arecombined to further enhance the recognition performance. Fig. 6 shows the blockdiagram to hierarchically combine both the image and feature fusion techniques. Faceimages from different spectrums, e.g. short-wave IR and long-wave IR, are fused usingthe image fusion algorithm described in Section 3.3. The amplitude and phase featuresextracted from the fused image are then fused using the feature fusion algorithmdescribed in Section 3.1 to generate a composite feature vector. Correlation techniquedescribed in Section 3.1 is used to match the two fused feature vectors.19
Page 4 and 5: face is detected using triangle bas
Page 6 and 7: Inverse Fourier Transform (IFT) of
Page 8 and 9: whereiPF 1 andiFP2are the i th fram
Page 10 and 11: training data is used, it could lea
Page 12 and 13: max⎪⎧ 1⎨ −⎪⎩ 2∑( αi
Page 14 and 15: MSF⎪⎧MS= ⎨⎪⎩ MSIFIFMS+ 1F
Page 16 and 17: Since the visible and IR imaging de
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 26 and 27: Experiments for validating the fusi
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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