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
1
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
2
Traditional approaches
• Appearance-based
• Normalization-based
• Feature-based
3
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
4
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
5
Histogram equalization
• A method in image processing of contrast
adjustment using the image's histogram
http://en.wikipedia.org/wiki/Histogram_equalization
6
Feature-based approaches
• Feature-based approaches extracts
illumination-insensitive feature sets directly
from the given image
• Local binary patterns(LBP)
7
Local binary patterns(cont.)
1
1
1
1
0
1
1
0
8
0
32
2
4
16
64
128
LBP=1X1 + 1X2 +
1X4 + 1X8 +
1X32
=47
10
11
• Appearance-based approaches
• Normalization-based approaches
• Feature-based approaches
12
• Preprocessing chain
• LTP local texture feature sets
• Multiple-feature fusion framework
13
Preprocessing chain
14
Gamma correction
• Gamma correction is a nonlinear gray-level
transformation
• Replace gray-level with
or
(for
)
15
Difference of Gaussian Filtering
• Gamma correction does not remove the
influence of overall intensity gradients such as
shading effects
• High-pass filtering removes both the useful
and the incidental information
16
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
17
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
18
Contrast equalization
• This stage rescales the image intensities to
standardize a robust measure of overall
contrast or intensity variation
19
Local ternary patterns
• Local binary patterns threshold at exactly the
value of the central pixel tend to be sensitive
to noise
• This section extends LBP to 3-valued codes,
LTP
20
Local ternary patterns(cont.)
The tolerance interval is [49, 59]
21
Local ternary patterns(cont.)
22
Local ternary patterns(cont.)
23