Xingjie Wei and Chang-Tsun Li
Department of Computer Science, University of Warwick, UK
x.wei@warwick.ac.uk
http://warwick...
Unconstrained face recognition
• Partial occlusions and expression changes
• Intra-class variations > inter-class variatio...
Single sample per person problem
• Real-world scenarios
– law enforcement, driver license or passport
card identification
...
Our method
• Does not require a training phase
• Matches faces by simulating the
mechanism of fixations and saccades in
hu...
Face recognition by humans
• Fixation
• Saccade: quickly jump
between fixations
5
Three key observations
• Humans scan a series of fixations instead
of the whole face
– not all local areas are helpful due...
Framework
7
1, Simulating fixations and saccades by
random sampling
Framework
8
2, Dynamic Image-to-Class Warping
X.Wei, C.-T. Li, and Y. Hu. Face recognition with occlusion using dynamic im...
Framework
9
3, Majority voting for fixations
Majority voting for fixations
• Each fixation has the possibilities to classify
the face correctly or wrongly
• Correct re...
Experimental results
• Randomly located occlusions
– FRGC database, 100 subjects, 2 sessions
– Single gallery image per pe...
Experimental results
• Randomly located occlusions
12
Experimental results
• Real disguise and expressions
• AR database, 100 subjects, 2 sessions
– Gallery: neural expression ...
Experimental results
14
Experimental results
15lower computational complexity than CTSDP
16
Thank you
• Questions ?
• Xingjie Wei
• x.wei@warwick.ac.uk
• http://warwick.ac.uk/xwei
• Department of Computer Scienc...
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Fixation and Saccade based Face Recognition from Single Image per Person with Various Occlusions and Expressions

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IEEE Conference on Computer Vision and Pattern Recognition Workshop on Biometrics (CVPRW'13)

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Fixation and Saccade based Face Recognition from Single Image per Person with Various Occlusions and Expressions

  1. 1. Xingjie Wei and Chang-Tsun Li Department of Computer Science, University of Warwick, UK x.wei@warwick.ac.uk http://warwick.ac.uk/xwei Fixation and Saccade based Face Recognition from Single Image per Person with Various Occlusions and Expressions
  2. 2. Unconstrained face recognition • Partial occlusions and expression changes • Intra-class variations > inter-class variations  poor recognition performance ! 2
  3. 3. Single sample per person problem • Real-world scenarios – law enforcement, driver license or passport card identification • Learning-based method – will suffer because the training samples are very limited 3
  4. 4. Our method • Does not require a training phase • Matches faces by simulating the mechanism of fixations and saccades in human visual perception • Is robust to to local deformations of faces 4
  5. 5. Face recognition by humans • Fixation • Saccade: quickly jump between fixations 5
  6. 6. Three key observations • Humans scan a series of fixations instead of the whole face – not all local areas are helpful due to occlusion and expression variations • Law of large numbers – one fixation  not sufficient – a large field of random selections: √ • The spatial relationship between facial features is very important 6 Random sampling
  7. 7. Framework 7 1, Simulating fixations and saccades by random sampling
  8. 8. Framework 8 2, Dynamic Image-to-Class Warping X.Wei, C.-T. Li, and Y. Hu. Face recognition with occlusion using dynamic image-to-class warping (DICW). IEEE International Conference on Automatic Face and Gesture Recognition (FG'13), 2013 1 2 3 4 5 6 7 8 9 1 2 3 4 5 6 7 8 9 • Each fixation is represented as a sequence • Consider the facial order information
  9. 9. Framework 9 3, Majority voting for fixations
  10. 10. Majority voting for fixations • Each fixation has the possibilities to classify the face correctly or wrongly • Correct recognition rate = probability of the consensus being correct • Combined decision is wrong only if a majority of the fixations votes are wrong and they all make the same misclassification 10
  11. 11. Experimental results • Randomly located occlusions – FRGC database, 100 subjects, 2 sessions – Single gallery image per person – Random occlusion: 0%~50% 11
  12. 12. Experimental results • Randomly located occlusions 12
  13. 13. Experimental results • Real disguise and expressions • AR database, 100 subjects, 2 sessions – Gallery: neural expression face from session 1 13
  14. 14. Experimental results 14
  15. 15. Experimental results 15lower computational complexity than CTSDP
  16. 16. 16 Thank you • Questions ? • Xingjie Wei • x.wei@warwick.ac.uk • http://warwick.ac.uk/xwei • Department of Computer Science, University of Warwick, UK

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