Enhanced Local Texture Feature Sets

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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

A
ppearance
-
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.)





10

1

2

4

8

16

32

64

128

1

1

1

1

0

1

0

0

LBP=1X1 + 1X2 +


1X4 + 1X8 +


1X32


=47


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

17

http://en.wikipedia.org/wiki/Difference_of_Gaussians

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.)


21


The tolerance interval is [49, 59]

Local ternary patterns(cont.)


22

Local ternary patterns(cont.)


23