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Face Recognition

Face Recognition. Joshua I. Cohen. Overview. Cutting Edge Constraints: - Speed - Accuracy. Data Acquisition. Digital Camera. Data Acquisition Cont’d. Data Acquisition Cont’d. Video Camera w/ IR Filtering. Algorithm. Pre-Processing Convert .bmp to .pgm Isolate Largest Contour

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Face Recognition

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  1. Face Recognition Joshua I. Cohen

  2. Overview Cutting Edge Constraints: - Speed - Accuracy

  3. Data Acquisition • Digital Camera

  4. Data Acquisition Cont’d

  5. Data Acquisition Cont’d • Video Camera w/ IR Filtering

  6. Algorithm Pre-Processing Convert .bmp to .pgm Isolate Largest Contour Profile Curve Curve Matching Parse Data to Extract Costs

  7. I. Pre-Processing Starting Images

  8. I. Pre-Processing Cont’d Convert To Gray Scale

  9. I. Pre-Processing Cont’d Binary Threshold

  10. I. Pre-Processing Cont’d Centroid

  11. I. Pre-Processing Cont’d Filter Noise Using Connected Regions

  12. I. Pre-Processing Cont’d Fill Region Up Until Contour

  13. I. Pre-Processing Cont’d Remove Hair-Line Noise

  14. I. Pre-Processing Cont’d Crop Image Area

  15. I. Pre-Processing Cont’d Normalize Fill Border To Black Save to .bmp File

  16. II. Convert .bmp to .pgm • SUN utility • convert-to-pgm f1.bmp f2.pgm .bmp  .pgm

  17. III. Isolate Largest Contour • SGI utility • computeBoundaryLength f1.pgm –smooth 1 CONTOUROPEN/CLOSEnx1 y1 x2 y2 …xn yn .con file

  18. IV. Profile Curve

  19. V. Curve Matching • OpenCurveMatch f1.con f2.con 10 1 • Intrinsic Properties: length and curvature • Optimal Correspondence • Alignment Curve • Invariant: rotations, translations, viewpoint variation, articulation, occlusions

  20. VI. Parse Data to Extract Costs • f1-points-f2-points-match.txt • cost-summary.txt f1 f1 0 f1 f2 403.668 f1 f3 468.081 f1 f4 454.315 f2 f2 0

  21. Preliminary Results Amir vs Everyone

  22. Preliminary Results Cont’d Brian vs Ming / Vinesh

  23. Preliminary Results Cont’d Tom vs Vinesh

  24. Preliminary Results Summary 50-60% success rate margin of error very small - ~ 450 or less is a match - ~ greater than 450 not a match

  25. Remaining Issues Angle Eye

  26. Remaining Issues Cont’d • Angle Cont’d Forehead / Eye Normal

  27. Remaining Issues Cont’d • Hair-Line • Normalizing  Distortion

  28. Possible Solutions • Use More Criteria (local extrema) • - contour of nose • - contour of chin • Better Method to Filter Hair-Line • Curve Match Segments of Face Profile

  29. References • Ross Cutler, “Face Recognition Using Infrared Images and Eigenfaces”, April 1996. • Thomas B. Sebastian, Philip N. Klein, Benjamin B. Kimia, “Alignment-based Recognition of Shape Outlines”, 2001. • Zdravko Liposcak and Sven Loncaric, “A Scale-Space Approach to Face Recognition from Profiles,” 1999.

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