Enhanced Local Texture Feature Sets for Face Recognition Under

Report
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

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