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Video Face Recognition , Pattern Recognition Final Report

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Video Face Recognition.

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Video Face Recognition , Pattern Recognition Final Report

  1. 1. VIDEO FACE RECOGNITION -THE LORD OF THE RINGS Yu-Chen-Lin Wang-Hsin-Shih
  2. 2. Outline • Motivation • Proposed Method • Experiment Design • Experiment • Demo • Conclusion
  3. 3. Motivation • 要找出特定演員在影⽚片中出現的時間往往曠⽇日 費時,需要看完整部影⽚片。︒ • 想要知道特定角⾊色在影⾯面中出現的時間和比重。︒ • 想看特定演員之間的對⼿手戲。︒
  4. 4. Proposed Method Method Flowchart
  5. 5. Method • Face Detection - Robust Real-Time Face Detection • FaceDetector • SkinFaceDetector • Feature Extraction • Eigenfaces • Local Binary Patterns Histograms • Classifier • K-nearest neighbor (K-NN)
  6. 6. Flow Chart Trailer Face Detection Feature Extraction Training data Feature Extraction ! Classifier Output Train Test Yes No Frodo Face Detection … Fetch Frame …
  7. 7. Experiment Design Dataset Generate Training Dataset Experiment Detail
  8. 8. Training Dataset • Galadriel 74 • Gandalf 37 • Gimli 38 • Gollum 46 • Legolas 112 • Aragorn 122 • Arwen 113 • Boromir 99 • Elrond 102 • Frodo 99 The Lord Of The Rings:
  9. 9. Dataset Generate • Crawl Google Image with Python Script.
  10. 10. Experiment Design • Label 5 ( 2 moveis) ✤ FaceDetector & SkinFaceDetector ✤ Eigenfaces & LBP ✤ K-NN , k =1, k=3 , k=5 ✤ Euclidean & Chi-Square & Cosine • Label 10 ( 4 moveis)
  11. 11. Experiment Evaluation Metrics Results
  12. 12. Evaluation Metrics • Face Detection Rate (%) ! ! • Face Recognition Rate (%) True ! True + False True + False + Unknown! True + False + Unknown + Non-Face DR = RR =
  13. 13. Results (1) • Compare FaceDecetor, Different Feature and K value ✤ Test data : 2002 The lord of rings trailer ✤ Face Detector : 20 images, SkinFace Detector : 14 images Model RR (%) Model RR (%) Face_LBP_1 78.95% SkinFace_LBP_1 100% Face_LBP_3 69.57% SkinFace_LBP_3 90.91% Face_LBP_5 70% SkinFace_LBP_5 90.91% Face_PCA_1 71.69% SkinFace_PCA_1 63.64% Face_PCA_3 66.67% SkinFace_PCA_3 45.45% Because 20 is larger than 14, we choose Face Detector.
  14. 14. Results (2) • Compare Distance ✤ Test data : 2003 The lord of rings trailer ✤ Parameter: 1. FaceDetector 2. LBP 3. k=1 4. Chi-square TRUE FALSE RR (%) Chi-Square 16 4 80.0% Cosine 14 6 70.0% Enclidean 13 7 65.0%
  15. 15. Results (3) • Final Test (Class * 5) ✤ Test data : The lord of ring trailer * 4 (different) ✤ Parameter: 1. FaceDetector 2. LBP 3. k=1 4. Chi-square TRUE FALSE Non-Face Unknown 2001-1 16 4 12 7 2001-2 16 5 15 2 2002 28 2 21 17 2003 20 4 10 11 Total 80 15 58 37 RR (%) 84.21% DR (%) 69.47%
  16. 16. Results (4) • Final Test (Class * 10) ✤ Test data : The lord of ring trailer * 4 (different) ✤ Parameter: 1. FaceDetector 2. LBP 3. k=1 4. Chi-square TRUE FALSE Non-Face Unknown 2001-1 22 9 16 2 2001-2 17 6 13 4 2002 26 8 25 24 2003 28 7 7 9 Total 93 30 61 39 RR (%) 75.60% DR (%) 72.60%
  17. 17. Demo
  18. 18. https://www.youtube.com/watch?v=-gou12pMmt4
  19. 19. Conclusion • LBP feature is better than Eigenfaces. • In our experiment, Chi-square distance is better than Cosine distance and Euclidean distance. • SkinDetectors aren’t useful all the time. • Hard to detect face in movie senses.
  20. 20. Thank you. END

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