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

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

Published in: Technology, Education
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  • 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. Unconstrained face recognition • Partial occlusions and expression changes • Intra-class variations > inter-class variations  poor recognition performance ! 2
  • 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. 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. Face recognition by humans • Fixation • Saccade: quickly jump between fixations 5
  • 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. Framework 7 1, Simulating fixations and saccades by random sampling
  • 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. Framework 9 3, Majority voting for fixations
  • 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. Experimental results • Randomly located occlusions – FRGC database, 100 subjects, 2 sessions – Single gallery image per person – Random occlusion: 0%~50% 11
  • 12. Experimental results • Randomly located occlusions 12
  • 13. Experimental results • Real disguise and expressions • AR database, 100 subjects, 2 sessions – Gallery: neural expression face from session 1 13
  • 14. Experimental results 14
  • 15. Experimental results 15lower computational complexity than CTSDP
  • 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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