Enhanced Local Texture Feature Sets for Face Recognition Under Difficult Lighting Conditions

1. Enhanced Local Texture Feature Sets for Face Recognition Under Difficult Lighting Conditions IEEE TRANSACTIONS ON IMAGE PROCESSING, VOL. 19, NO. 6, JUNE 2010 Xiaoyang Tan and Bill Triggs 報告者：王克勤

2. Introduction • Face recognition has received a great deal of attention from the scientific and industrial communities over the past several decades • This paper focuses mainly on the issue of robustness to lighting variations

3. Traditional approaches • Appearance-based • Normalization-based • Feature-based

4. Appearance-based approaches • Training examples are collected under different lighting conditions and directly used to learn a global model of the possible illumination variations • Direct learning of this kind makes few assumptions but it requires a large number of training images and an expressive feature set

5. Normalization-based approaches • Normalization based approaches seek to reduce the image to a more canonical form in which the illumination variations are suppressed • Histogram equalization

6. Histogram equalization • A method in image processing of contrast adjustment using the image's histogram http://en.wikipedia.org/wiki/Histogram_equalization

7. Feature-based approaches • Feature-based approaches extracts illumination-insensitive feature sets directly from the given image • Local binary patterns(LBP)

8. Local binary patterns(cont.) LBP=1X1 + 1X2 + 1X4 + 1X8 + 1X32 =47

10. Appearance-basedapproaches • Normalization-based approaches • Feature-based approaches

11. Preprocessing chain • LTP local texture feature sets • Multiple-feature fusion framework

12. Preprocessing chain

13. Gamma correction • Gamma correction is a nonlinear gray-level transformation • Replace gray-level withor (for )

14. Difference of Gaussian Filtering • Gamma correction does not remove the influence of overall intensity gradients such as shading effects • High-pass filtering removesboth the useful and the incidental information

15. Difference of Gaussian Filtering(cont.) • Difference of Gaussians is a grayscale image enhancement algorithm that involves the subtraction of one blurred version of an original grayscale image from another, less blurred version of the original • Difference of Gaussians can be utilized to increase the visibility of edges and other detail present in a digital image http://en.wikipedia.org/wiki/Difference_of_Gaussians

16. Masking • If facial regions (hair style, beard, ) that are felt to be irrelevant or too variable need to be masked out, the mask should be applied at this point

17. Contrast equalization • This stage rescales the image intensities to standardize a robust measure of overall contrast or intensity variation

18. Local ternary patterns • Local binary patternsthreshold at exactly the valueof the central pixel tend to be sensitive to noise • This section extends LBP to 3-valued codes, LTP

19. Local ternary patterns(cont.) The tolerance interval is [49, 59]

20. Local ternary patterns(cont.)

21. Local ternary patterns(cont.)