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Face Categorization using SIFT features

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

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  1. Face Categorization using SIFT features Mei-Chen (Mei) Yeh ECE 281B 06/13/2006

  2. Bush vs Schwarzenegger

  3. Serena Williams vs Venus Williams

  4. 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

  5. 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

  6. … frequency ….. codewords Vector quantization codeword 2 codeword 1 SIFT feature vectors from training samples codeword 3 128-d feature space

  7. Datasets • The BioID Face Database (simple) • 1521 images with 23 people • Variety of illumination, background and face size

  8. 22 categories, 1613 images 70% for training, 30% for testing

  9. Measurement • Confusion Matrix Classifiers Categories Average Categorization Rate

  10. 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%

  11. 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

  12. 11 categories, 851 images 70% for training, 30% for testing

  13. 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%

  14. 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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