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EE368 Face Detection Project Report Group 6

EE368 Face Detection Project Report Group 6. 04880489 Yuichiro Yamashita yamas@stanford.edu. Contents. Algorithm selection System design Results Conclusion. Algorithm Selection. Main idea: Support Vector Machine Classifier using samples which lie on the decision boundary

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EE368 Face Detection Project Report Group 6

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  1. EE368 Face Detection Project ReportGroup 6 04880489 Yuichiro Yamashita yamas@stanford.edu

  2. Contents • Algorithm selection • System design • Results • Conclusion

  3. Algorithm Selection • Main idea: Support Vector Machine • Classifier using samples which lie on the decision boundary • Problem: Slow on Matlab • Prefilter before SVM • non-face rejection by color: using HSV Color space for skin color detection • non-face rejection by pattern: using Maximal Rejection Classifier (MRC)

  4. Recipe • Apply Skin Color Detector for Image • Create Image pyramid and apply MRU. • Take AND operation of 1 and 2, then classify by SVM • Merge Image pyramid, and remove repetitive face candidates.

  5. Skin Color Detector • Avoid false rejection • Faster is better • No need to be precise • The pixel with Hue of -330deg to 45deg is regarded as face region. Skin pixels plotted in HS-space. Hue being the angle q Work from J. Sherrah and S. Gong

  6. Prefilter by MRC • Train MRC by faces and non-faces of 19 x 10 block size (Upper half of the face) • Training set contains 543 faces and 477 non-face blocks manually picked-up and 100000 arbitrary picked-up non-faces images • Important: Avoid False Rejection

  7. Support Vector Machine • Train SVM by faces and non-faces of 19 x 19 block size. • Training set contains 543 faces and 293 non-face blocks. • Horizontal and Vertical differentiation to cancel DC offset. • Main Ideas from Papageorgiou, 1998 • SVM toolbox v0.54 by Cawley.

  8. Skin Color Detection: Result • Filtering by Hue value • Walls etc. are still classified as skin

  9. MRC: Result • Blight pixels are candidates for face. • Still some false positives found

  10. Face Detection Result after SVM

  11. Face Detection: Result • Images 1, 2, 3, 6, 7 are used for training, 4,5 is for test only • Detection rate of 86%, with 1 false alarm. • Can be improved with increased number of layers and giving more face examples.

  12. Conclusion • Face Detection System has been implemented with Support Vector Machine • Pre-filter by Skin Color and Maximal Rejection Classifier has been used to expedite the process • Reasonable Detection Rate. Can be easily improved with increased number of layers and face/non-face samples

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