1. Novel Face Detection Method Based on Gabor Features
Jie Chen, Shiguang Shan, Peng Yang, Shengye Yan, Xilin Chen, Wen Gao

2. Outline Face detection survey Existing problem of the system
Our solution
Experiments
Conclusion
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3. How many faces in this pixture？
Face detection
Face detection is to determine whether there are any faces within a given image, and return the location and extent of each face in the image if one or more faces present.
How many faces in this pixture？
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4. AdaBoost T T T T F F F F Local Feature Local Feature Local Feature
All Samples
Accepted Samples F F F F
Rejected samples
P. Viola and M. Jones “Rapid object detection using a boosted cascade of simple features” CVPR 2001
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5. Boosting and its variations
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6. Existing problem of the system
feature
Computation cost
false alarms
Representation ability
Haar
low
high
weak
Gabor
strong
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7. Our solution AdaBoost + (Harr+Gabor)
Harr features : increase the speed
Gabor features : decrease the false alarms.
The final strong classifier is consisted of a few hundreds of weak classifiers (Harr+Gabor features).
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8. Gabor filter
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9. Gaborface
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10. The schematic of the proposed method
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11. Experiments
Training set and testing set:
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12. Some examples of Set1
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13. The ROC curves for our detectors on the MIT face test set
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14. Training the Detector
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15. The ROC curves on the MIT+CMU frontal face test set.
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16. CAS-PEAL Face Database
Contains 99,594 images of 1040 individuals (595 males and 445 females)
Varying Pose, Expression, Accessory, and Lighting (PEAL)
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17. Detection rates on CAS-PEAL
Data Set
Faces
False alarms
Detection rates
Results in each sub-directory
Frontal
9029 66
96.42%
POSE
(within 30o) PD
4998 18
94.74% PM
4993 35
99.78% PU
135
98.06%
Total
24018
254
97.08%
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20. Demo show
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21. Thank you very much!
END
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