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F ace image m apping from NIR to VIS Jie Chen Machine Vision Group ee.oulu.fi/mvg

F ace image m apping from NIR to VIS Jie Chen Machine Vision Group http://www.ee.oulu.fi/mvg. Outline. Problem Methods Preliminary results Plans for next period. F ace image m apping from NIR to VIS. Problem NIR: Near infrared imaging VIS: Visual light imaging.

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F ace image m apping from NIR to VIS Jie Chen Machine Vision Group ee.oulu.fi/mvg

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  1. Face image mapping from NIR to VIS Jie Chen Machine Vision Group http://www.ee.oulu.fi/mvg

  2. Outline Problem Methods Preliminary results Plans for next period

  3. Face image mapping from NIR to VIS • Problem • NIR: Near infrared imaging • VIS: Visual light imaging

  4. Face image mapping from NIR to VIS • Problem • NIR: Near infrared imaging • VIS: Visual light imaging

  5. Algorithm: Patches mapping Training • Training

  6. Algorithm: Patches mapping Training • Mapping φi,j

  7. Look up the KNN A patch of an input sample in S4 A patch of an input sample in S3 Corresponding patch of in S2 k-th nearest patch in S1 Weight of k-th nearest neighbor

  8. Weight computing

  9. Experiments • Setup • both S1 and S2 is composed of 300 samples. • 50 subjects, • each subject has 6 images but in different expression (anger, disgust, fear, happiness, sadness, and surprise). • wf=64, hf=80, wp=16, hp=16, wo =12, ho=12 • Testing:using leave-one-out and K=15.

  10. Reconstructed images

  11. Multi-resolution LBP (MLBP) (P=4,R=1) (P=8,R=1) (P=12,R=1.5) (P=16,R=2) (P=24,R=3)

  12. PNSR Pixel wise LBP

  13. Multi-resolution LBP

  14. PSNR on MLBP

  15. Plans for next period • Training data: • Use more samples (192*10 from CASIA, a group in Beijing, China) • Methods: • Combine the methods proposed in the paper (A. Hertzmann, SIGGRAPH,2001) for better performance

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