1. An Efficient Skin Illumination Compensation Model for Efficient Face Detection
Speaker: YI-JIA HUANG Date: 2011/12/08 Authors: C . N. Ravi Kumar , Bindu . A Source: IEEE

2. Outline Introduction Proposed face detection steps
Experimental results
Conclusions
Comment

3. Introduction
Colour based approaches face difficulties in robustly detecting skin colour in the presence of complex background and different lighting conditions.
Based on lighting compensation techniques, various skin tones and with very complicated background and uses signature of the different skin regions identified to uniquely identify the face candidates.

4. Proposed face detection steps (1/5)
major modules
Skin Region Detection
Face Candidate Localization
The algorithm initially estimates and projects the lighting compensated red component content of the image.

5. Proposed face detection steps (2/5)
The LCRC component is evolved from the input colour image by transforming the RGB Colour Space component of the image onto YCbCr Colour Space component and removing the intensity and the blue component from the image.
The modified image is subjected to skin detection algorithm which detects only the true skin regions in the image.

6. Proposed face detection steps (3/5)
The complexity involved in computation is relatively more proficient when compared to that of the prior developed methodologies because of the fact that the luminance information is excluded from the computation.

7. Proposed face detection steps (4/5)
Skin illumination compensation model
detection using skin tone is much faster than processing other facial features.
human faces have variation in their skin colours, it is noted that such variation lies more in the intensity rather than the colour itself , that is to say that the skin colour chromaticity varies slightly between varied cases compared to the intensity variation.
Boundary detection
Signature plot
Face candidate extraction

8. Proposed face detection steps (5/5)

9. Experimental results (1/2)
Our algorithm can detect multiple faces of different sizes with a wide range of facial variations in an image.
Our algorithm works efficiently for occluded faces , faces of any size, faces with glasses, intensity variations etc.
Our algorithm can also detect the nonfrontal faces irrespective of the fact that whether the eyes and nose are visible or not.

10. Experimental results (2/2)

11. Conclusions (1/7)
We through this research paper are putting forth a humble effort in the form of an algorithm to detect faces under varied conditions.
Our detection algorithm initially takes the colour image and applies the skin illumination compensation model to modify the RGB to YCbCr converted image suitably to detect the valid skin regions.

12. Conclusions (2/7)
The detected skin regions are converted into the binary format and the signatures corresponding to each of the detected skin regions is found and the skin region corresponding to the face candidate is extracted by studying the signatures.
With these results, we can confidently state that our method outperforms all the published, popular methodologies.

13. Conclusions (3/7)

14. Conclusions (4/7)

15. Conclusions (5/7)

16. Conclusions (6/7)

17. Conclusions (7/7)

18. Comment
這篇使用了之前不同的方法來偵測，而且也做了一些 不少測試，偵測率有99%之高，不過這個方法(膚色燈 光補償)，還不夠了解因為這篇只說方法操作並沒有說 演算法，所以接下來打算從文獻來找出相關演算法。