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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



Serena Williams vs Venus Williams


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


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


22 categories, 1613 images

70% for training, 30% for testing


Measurement
Measurement

  • Confusion Matrix

Classifiers

Categories

Average Categorization Rate


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 11 12 13 14 15 16 17 18 19 20 21 bg

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


11 categories, 851 images 11 12 13 14 15 16 17 18 19 20 21 bg

70% for training, 30% for testing


81.82% 11 12 13 14 15 16 17 18 19 20 21 bg 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 11 12 13 14 15 16 17 18 19 20 21 bg

  • 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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