- The document proposes a video face recognition method using face detection, feature extraction with local binary patterns histograms (LBP), and k-nearest neighbor (k-NN) classification. - An experiment was conducted on movie trailers from The Lord of the Rings films to evaluate the proposed method, comparing different face detectors, features, distance metrics, and values of k. - The results showed that LBP features with a Chi-square distance metric and k=1 performed best, achieving a face recognition rate of 84.21% on 5 classes and 75.60% on 10 classes.

1. VIDEO FACE RECOGNITION
-THE LORD OF THE RINGS
Yu-Chen-Lin
Wang-Hsin-Shih

2. Outline
• Motivation
• Proposed Method
• Experiment Design
• Experiment
• Demo
• Conclusion

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. Proposed Method
Method
Flowchart

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. Flow Chart
Trailer
Face
Detection
Feature
Extraction
Training
data
Feature
Extraction
!
Classifier Output
Train
Test
Yes
No
Frodo
Face
Detection
…
Fetch
Frame
…

7. Experiment Design
Dataset Generate
Training Dataset
Experiment Detail

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. Dataset Generate
• Crawl Google Image with Python Script.

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. Experiment
Evaluation Metrics
Results

12. Evaluation Metrics
• Face Detection Rate (%)
!
!
• Face Recognition Rate (%)
True !
True + False
True + False + Unknown!
True + False + Unknown + Non-Face
DR =
RR =

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. 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. 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. 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. Demo

18. https://www.youtube.com/watch?v=-gou12pMmt4

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. Thank you.
END