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Face Categorization using SIFT features. Mei-Chen (Mei) Yeh ECE 281B 06/13/2006. Bush vs Schwarzenegger. Serena Williams vs Venus Williams. Main goal. Face categorization problem Object class recognition techniques have seen progress in recent years (ex: bag-of-words models)

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face categorization using sift features

Face Categorization using SIFT features

Mei-Chen (Mei) Yeh

ECE 281B

06/13/2006

main goal
Main goal
  • Face categorization problem
    • Object class recognition techniques have seen progress in recent years (ex: bag-of-words models)
    • Different people are considered different object classes
  • Integration of SIFT features
    • Does SIFT help face recognition? To what degree?
  • Target on a few people
  • Applications
    • Face annotation in family albums
    • Name-based photo search
method
Method
  • Features: SIFT
  • Bag-of-words representation for faces
    • Each SIFT feature is considered a “codeword”
    • Build a dictionary based on training samples
    • Each face is represented as a histogram over codewords
  • Learning: Naïve Bayes Classifiers
slide6

frequency

…..

codewords

Vector quantization

codeword 2

codeword 1

SIFT feature vectors from training samples

codeword 3

128-d feature space

datasets
Datasets
  • The BioID Face Database (simple)
    • 1521 images with 23 people
    • Variety of illumination, background and face size
slide8
22 categories, 1613 images

70% for training, 30% for testing

measurement
Measurement
  • Confusion Matrix

Classifiers

Categories

Average Categorization Rate

slide10
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 bg

Average: 89.14%

datasets1
Datasets

http://www.cs.berkeley.edu/~millert/faces/faceDict/NIPSdict/

  • Faces in the Wild (challenging)
    • 851 images, 10 people + 1 non-faces
    • Extracted from news videos
slide12
11 categories, 851 images

70% for training, 30% for testing

slide13
81.82% 0.00% 0.00% 0.00% 0.00% 4.55% 4.55% 0.00% 4.55% 4.55% 0.00%

0.00% 83.33% 0.00% 0.00% 0.00% 0.00% 8.33% 8.33% 0.00% 0.00% 0.00%

4.00% 4.00% 68.00%0.00% 4.00% 16.00% 4.00% 0.00% 0.00% 0.00% 0.00%

20.00% 0.00% 0.00% 80.00%0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00%

8.00% 0.00% 0.00% 8.00% 68.00% 12.00% 0.00% 4.00% 0.00% 0.00% 0.00%

0.00% 0.00% 7.14% 0.00% 0.00% 85.71%0.00% 7.14% 0.00% 0.00% 0.00%

0.00% 0.00% 0.00% 0.00% 0.00% 0.00%100.00% 0.00% 0.00% 0.00% 0.00%

0.00% 9.09% 9.09% 0.00% 0.00% 4.55% 0.00% 77.27%0.00% 0.00% 0.00%

0.00% 4.76% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 85.71%9.52% 0.00%

0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 8.33% 8.33% 83.33%0.00%

0.00% 2.44% 2.44% 2.44% 2.44% 0.00% 0.00% 0.00% 2.44% 0.00% 87.80%

Average: 81.91%

conclusions
Conclusions
  • SIFT features + bag-of-words representation might work for face recognition
    • Simple dataset: good
    • Challenging dataset: may be improved
  • Consider the spatial relations between features may be the next step to improve the performance
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