1. Recognition of Partially Occluded Face Using
Gradientface and Local Binary Patterns
George D. C. Cavalcanti
Tsang Ing Ren,
Josivan R. Reis
2012 IEEE International Conference on Systems, Man, and Cybernetics
October 14-17, 2012, COEX, Seoul, Korea

2. Outline
 Introduction
 Occlusion Detection
 Recognition
 Experiments and results
 Conclusion
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3. INTRODUCTION
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4. Introduction
‫ ﻪ‬Challenges of face recognition systems is
the problem of occlusion.
‫ ﻪ‬Uncontrolled environments such as drastic
change of lighting, change of expression,
beards and occlusions.
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5. Proposed approach
‫ ﻪ‬Apply in the problem for face recognition
with sunglasses and scarf occlusion.
‫ ﻪ‬Consider illumination, rotation and
inclination problems.
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6. Flowchart
Occlusion
Non-occlusion
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7. OCCLUSION DETECTION
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8. ‫ ﻪ‬The image is divided into equal parts that
are classified into occluded and nonoccluded using MultiLayer Perceptron
(MLP)
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9. ‫ ﻪ‬Occlusion detection is a classification
problem
N: a set of training images
X: input layer
Y: output layer
Indicate :
1: non-occluded
-1: occlude
Partial Face Classifier Using LDA and MLP”. In Proceedings of the 2010. IEEE/ACIS 9th
International Conference on Computer and Information Science (ICIS '10)
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10. RECOGNITION
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11. ‫ ﻪ‬For the recognition, Local binary Pattern(LBP)
feature is used on the non-occluded image
part.
‫ ﻪ‬LBP widely used in face recognition
‫ ﻩ‬Discriminative power
‫ ﻩ‬Computational simplicity
‫ ﻩ‬Robustness to changes in grayscale.
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12. But:
LBP is not efficient for drastic lighting
variations.
Solve:↓
we use a Gradientface as a preprocessing
step before LBP.
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13. GradientFace
‫ ﻪ‬It is insensitive to variations in illumination
and stands out in face recognition applications.
‫ ﻪ‬Gradientface Method:
1. Transforms to the gradient domain
2. Eliminate noise or shadow (Gaussian filter)
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14. ‫ ﻪ‬Smooth the image through a convolution with a
Gaussian function
∗ is the convolution operator
σ is the Gaussian function
‫ ﻪ‬Compute the image gradient I convolving in
directions x, y
‫ ﻪ‬Generate as result ,Gradientface
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15. 15

16. Local binary patterns
ln : Corresponds to the central pixel value
lc : The 8-neigbor pixels values
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17. Recognition
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18. ‫[ ﻪ‬Gradientface + LBP ]
computational simplicity
robust to scales changes and illumination variations.
‫ ﻪ‬Define the similarity between the LBP histograms of
each image a similarity distance is used [7].
[E. P. Xing, A. Y. Ng, M. I. Jordan, and S. Russell, “Distance metric learning with application to clustering with side
information,” in Advances in Neural Information Processing Systems 15 , S. Becker, S.Thrun, and K. Obermayer, Eds.
Cambridge, MA: MIT Press, 2003, pp. 505–512 ]
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19. EXPERIMENTS AND RESULTS
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20. Database
‫ ﻪ‬AR Face
‫ ﻩ‬More than 4000 color images (70 men 56 women)
‫ ﻩ‬With different facial expressions, lighting conditions
and occlusions (sun glasses and scarf)
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21. ‫ ﻪ‬ORL
‫ 04 ﻩ‬subjects, 10 different images for each subject.
‫ ﻩ‬Facial expressions (open or closed eyes)
‫ ﻩ‬Facial details (glasses and without glasses)
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22. Experiments1- MLPClassifier
‫ ﻪ‬Occlusion detection
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23. Experiments2- Recognition
Database: AR Face
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24. Experiments2- Recognition
Database: ORL Face
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25. CONCLUSION
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26. Conclusion
‫ ﻪ‬Address the problem of face recognition with
occlusion caused by sunglasses and scarf.
‫ ﻪ‬The Gradientface applied to image with
illumination problem and used to pre-processing
the image, improved the recognition.
‫ ﻪ‬Combination of pre-processing techniques and
classifies can still demonstrate improvements in
face recognition problems.
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27. Q&A
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28. END
Thanks