PhD Viva Presentation-9-6-2015

Faculty of Information Science and Technology, Universiti Kebangsaan Malaysia
CENTER FOR ARTIFICIAL INTELLIGENCE TECHNOLOGY
By
OMAR M WAHDAN
SUPERVISED BY
Assoc. Prof. Dr. MOHAMMAD FAIDZUL BIN NASRUDIN
PROF. Dr. KHAIRUDDIN BIN OMAR
Geometrical Insensitive-To Shear and Half Rotation
Texture Descriptor from Local Binary Pattern for
Paper Fingerprinting

Contents
• Introduction
• Background
• Challenges in Paper Texture Verification
• Problem Statement
• Research Objectives
• Research Methodologies
• Investigation of The Irregular Rotation Phenomenon
• The Proposed Shearing Invariant Texture Descriptor (SITD)
• Effects of the Rotation to the SITD
• Across-bin matching techniques
• The Proposed Rotation Shear Invariant Texture Descriptor (RSITD)
• The Proposed Completed RSITD (CRSITD)
• Conclusions
• Research Contributions
• Future Directions

Introduction
• ”Biometric”is the science of identifying the individuals based on
unique physical and/or behavioral characteristic properties. E.g. face
shape, fingerprinting, signature, voice, etc. (Inbavalli & Nandhini 2014).
• The tokens in Both groups are used in various security applications. E.g.
individuals identification, open secured doors, money transactions, etc.
Individuals Identification Open Secured Doors Money Transactions

Introduction (2)
• Recently, it has been found that “any random structure
generating during the manufacturing process involves a
unique property” (Clinick et. al. 2013).
• As with human, the unique property could benefit to
authenticate the manufactured materials. e.g. documents.
• Physical documents verification is significant in various areas
such as: currency, legal deeds, certificates, artworks, lottery
tickets, tickets for flight or watching games like football,
cricket, etc. (Jayadevan et. al. 2012).

Background
• Generally, document verification are categorized into:
 A) Digital methods like visual cryptography or barcodes (Eldefra et. al. 2012).
 B) Special physical material like special papers, special ink (Sar et. al. 2013).
 C) Paper surface the unique token is the physical document paper texture
(Clarkson et. al. 2009).
• Digital methods can be scanned or copied, while using the physical
materials usually involves costly equipment.
• Conversely, paper texture is secured, inexpensive, and more efficient
over the other methods (Shields 2013).
• The unique token in the paper texture is
“the random and impossible-to-reproduce
structure of physical fibers constitute it”.

• Two methods are used to acquire papers textures:
 Movable Sensors such as
microscopic digital cameras or lasers.
Challenges in Paper Texture Verification
 Non-movable Sensors such as
normal desktop flatbed scanners.
 usually based on sophisticated devices
that are expensive and not available to
public researchers.
 inexpensive and widely available.
 produce images that are affinely
transformed, i.e., scaled, rotated, and/or
translated.
 produce images that only exhibit
Euclidian transformations, i.e., a
rotation and/or translation.

Problem Statement
• Recent scanned paper fingerprinting methods, e.g. Buchanan &
Cowburn (2011); Clarkson et al. (2009) were arbitrarily ignored the
effects of natural deformations inherent to texture acquisition, i.e.
slight and 180˚ rotation.
• That is because:
 The slight rotation deformation usually generated by a rotation based
on pivot placed at the corner of the paper instead of its geometrical
centre pivot.
 From a feature extraction standpoint, this deformation (called irregular
rotation) is completely different from traditional image rotation.
• Hence, the challenge is how to tackle the effects of the “irregular
rotation phenomenon” and “180˚ rotation”.

Research Objectives
• The main objective of the research is to develop a new shear invariant
features from the Local Binary Pattern for the use in scanned paper texture
fingerprinting.
• To achieve the objective, the following sub-objectives need to be carried out:
 To mathematically define the irregular rotation phenomenon of scanned
paper image.
 To propose a novel shear invariant features from LBP texture descriptor.
 To develop a 180˚ rotation invariant method based on the proposed
shearing invariant descriptor.
 To propose the complete modeling of the rotation shearing invariant
descriptor.
 To provide a standard test dataset to enable comparison of various
technique results.

Scopes
The scopes of the research are as the following:
 This research focuses on the automatic document authentication based on its
surface texture.
 The proposed document authentication method comprises several image processing
and machine vision techniques. The emphasis is on the feature extraction techniques.
 The involved documents are standard size A4 blank white papers, whereas no
physical attack in anyway implemented on these papers. Besides, 29 different
artificial and natural materials surfaces obtained from Outex standard images
dataset are also involved.
 The texture images acquired from the papers by using desktop Epson GT-2500
scanner with 50, 100, and 150 dpi resolutions.
 The proposed feature extraction method is developed based on the Local Binary
Pattern (LBP) texture descriptor as an image processing tool.
 The State-of-the-art SMI method and nine rotation invariant texture operators of
LBP and CLBP descriptors are used in comparison with the proposed methods.
 The current thesis does not implement any kind of feature selection operation to the
extracted features from the involved features descriptors.

The Datasets
• This research involved three datasets with total of 3518 images.
• In each dataset, half of the images are deformed with only shear
transform. The 180˚ rotation transform is added in some experiments.
 3) The papers textures dataset is self-developed and consist of 306 texture
images collected from surfaces of white standard A4 blank papers.
o The dataset is publicly available at www.ftsm.ukm.my/pr for free.
 1) Standard Outex images dataset consist of 3200 texture images
collected from 29 different artificial and natural materials surfaces.
o The dataset is publicly available at www.outex.oulu.fi for free.
 2) Standard shape-based dataset consist of 12 well-known benchmark
images in the pattern recognition area. The images are with distinctive
shapes.

• The standard shape-based images dataset• The paper texture acquisition• Samples from Outex dataset
The Datasets (2)
Barleyrice
Tile
Paper

Research Methodologies
• The Research methodologies are implemented in the
following four main stages:
Phase 1
• RESEARCH DESIGN
Stage 2
• DESIGN THE PROPOSED PAPER FINGERPRINTING METHOD
Stage 3
• EXPERIMENTS DESIGN
Stage 4
• PERFORMANCE EVALUATION, COMPARISON AND
DISCUSSION
Stage 1

Execution Phase
Research Methodologies (2)
Stage 1 RESEARCH DESIGN
Theoretical Studies Phase
Start
Identify the problem
statement
Literature Review
Irregular rotation
deformation investigation
Develop the proposed Shear
Invariant Texture Descriptor (SITD)
Achieve 180˚ Rotation Invariant
features based on SITD (RSITD)
Develop the Completed
RSITD (CRSITD)
End

Execution Phase
•Irregular rotation
deformation investigation.
•Develop the proposed
Shear Invariant Texture
Descriptor (SITD).
•Achieve 180 Rotation
Invariant features based on
(SITD).
•Develop the Completed
RSITD (CSITD).
RESEARCH DESIGN STAGE
Theoretical Studies Phase
• Studying the characteristic of the document surface (texture).
•Specifying the deformations originating during texture acquisition
process.
•Reviewing the methods toward tackling these deformations.
•Studying the impact of feature extraction to the overall performance.
•Studying the categories of texture-based feature extraction methods.
•Reviewing the exist representative rotation invariant texture
descriptors.
•Studying the features matching techniques and their categories.
•Reviewing the exist texture-based document fingerprinting methods.
•Reviewing the stages of document texture fingerprinting.

Database
Validation
Texture acquisition
Texture description
Fingerprint
Registration
Texture acquisition
Texture description
Fingerprint
Matching
Result GenuineFake
DESIGN THE PROPOSED PAPER FINGERPRINTING METHOD

Stage 3 EXPERIMENTS
Exp.
#
Reason of conduct Datasets # of
images
Source of images
1 To evaluate the performance of
the proposed methods based
on standard texture images.
-Outex 3200 -Artificial and
natural materials
textures.
on standard shape images.
-Shape-based
images
12 -Images with
distinctive shapes.
on real-world application.
-scanned papers 306 -White A4 papers
textures .
• In all the experiments, the data are equally divided. The original images
were used as reference data while the distorted images as test data.

Stage 4 PERFORMANCE COMPARISON
For performance comparison, the proposed methods are
evaluated against bunch of descriptors:
 The Local Binary Pattern (LBP) descriptor as it’s the
SITD based scheme. The descriptor includes 4 different
operators.
 The Completed LBP (CLBP) descriptor as it’s the
current state-of-the-art rotation invariant texture
descriptor. The descriptor includes 5 different operators.
•For the LBP and CLBP descriptor, different values of P and R are
tested.
 The Shearing Moment Invariant (SMI) descriptor as its
currently the sole available shear-only Invariant
descriptor.

The over all architecture of the works done
Studying the types of feature
extraction methods
Studying the scanned paper
image deformation
Studying the exist texture-
based authentication methods
Develop a set of equation
swaying and measure the
deformation which its the shear
- Develop the SITD
- Develop the RSITD
- Develop the CRSITD
Develop paper fingerprinting
method
Develop papers textures
images dataset
Experiments implementation
Standard images datasets
- Standard Outex dataset
- Standard shape-based dataset
State-of-the-art rotation and
shearing invariant
descriptors:
- Four operators of LBP
- Five operators of CLBP
- SMI
Add 180 rotation and/or shear
transforms based on the type of
experiment
Performance Analysis
and comparisons
Conclusions and future
directions

Investigation of The Irregular Rotation
Phenomenon
• The paper’s image could be rotated based on infinite pivots in its 2D plane.
• To rotate the image based on any pivot position:
– Translate the image with (tx,ty) to put the pivot from the center to, let’s say,
the top right corner.
– Rotate the image with Ɵ°.
– Return the image to its original position by translating it (-tx,-ty).
• These three steps are mathematically given by:
x‘ = (x - tx) cos Ɵ – (y – ty) sin Ɵ + tx
y‘ = (x - tx) sin Ɵ + (y – ty) cos Ɵ + ty

Phenomenon (2)
• Preliminary experiment was conducted by naked eyes to
compare few papers’ image pixels before and after the
irregular rotation.
 A square patches placed at all
corners of the paper’s image,
where their centres are 125 pixels
from the corners.
 It’s found that the irregular
deformation, in fact, is a shearing
transform.
(585,452)
(585,-452)
(555.3,-424.5)(-585,-425)
(-613.9,-383.6)
(-585,425)
(-584.3,465.8)
(585,452)
(460,300)
125

Phenomenon (3)
• To measure the shear, the normal transformation equation is used:
x‘ = x + ay
y‘ = bx + y
• By replacing (x, y) with the positions of the patches’ centres before the
rotation and (x', y') with their corresponding positions after the rotation,
the amount of horizontal and vertical shear can be measured as:
a = ((x - tx) cos θ - (y – ty) sin θ + tx - x) / y
b = ((x - tx) sin θ + (y - ty) cos θ + ty - y) / x
• The results (in mm) are:
• Since, none of the results are equal to zero, shear transformation is
confirmed to be exist.
Patch #
Position of patch center Amount of shear
Before After Horizontally Vertically
1 (460, 300) (455.7, 304.4) 0.0143 0.0096
2 (-460, 300) (-463.7, 336.5) 0.0124 0.0794
3 (460,- 300) (434.7,-295.2) 0.0841 0.0104
4 (-460, -300) (-484.6,-263.1) 0.0822 0.0802

Investigation Experiments
• To confirm the theoretical findings presented early, two feature
extraction methods were applied and compared, i.e. traditional
Moment Invariant (MI) descriptor and Shearing Moment
Invariant (SMI) descriptor.
• Generally, two main image matching experiments are conducted.
Experime
nt #
Reason of conduct # of
images
Source of images
both descriptors on shear
deformations produced under
controlled conditions
12 Standard images (Barbara,
Cameraman, Fingerprint,
Flintstones, House and Lena)
2 To empirically confirm that the
irregular rotation phenomenon is
a shear transformation
102 Scanned papers

Investigation Results
• The results are:
• As the experimental results always showed the shearing invariant
descriptor outperforms the regular affine invariant descriptor, the
theoretical findings have been verified.
• Because some critical applications like paper fingerprinting needs to
have a descriptor that can perform better than the SMI, developing a
new robust shearing invariant description method becomes crucial.
Experiment # SMI MI
1 66.7% 33.3%
2 78.4% 50.9%

The Proposed Shearing Invariant
Texture Descriptor (SITD)
• The theoretical studies phase reviewed rotation invariant texture methods
and concluded that the LBP is the current state-of-the-art descriptor.
• The basic LBP operator is a simple, yet efficient rotation invariant texture
descriptor. Also, its tendency to simplify the local image structure, as well as
its conciseness and low computational cost (Wei et. al. 2014).
• The operator assigns a label to every pixel of an image by thresholding the
3x3-neighborhood of each pixel with the center pixel value and considering
the result as a binary number.
• Where gc is the gray value of the central pixel, gp is the gray value of neighbor p, P is the number
of those neighbors, and R is the neighborhood radius.
85 99 12
53 54 86
57 21 23
31 45 -42
-1 32
3 -33 -31
=
1 1 0
0 1
1 0 0
31 45 42
1 32
3 33 31
Thresholding
Binary : 0011
Decimal: 3
1
,
0
1 0
( )2 , ( )
0 0
P
p
P R p c
p
x
LBP s g g s x
x



   

Binary : 1Binary : 11Binary : 011
Magnitude
component
Sign
component

Texture Descriptor (SITD) (2)
gc gc gc gc
0 1 2 3
gc gc gc gc
4 5 6 7
gc gc gc gc
8 9 10 11
gc gc gc gc
12 13 14 15
• The 16 (2P) possible
pattern where P=4
(number of
neighborhood pixels).

• Based on the sign component, the proposed SITD selects few pixels
from the image local patterns to achieve shearing invariant
features.
• To produce its lookup table which utilize to extract these features,
the SITD relies only on 4 neighbor pixels (P) with radius R=1 grid.
• Based on the type of shear, only two horizontal or vertical
neighbours’ pixels are involved.
• As a result, the SITD offers two operators, one invariant to
horizontal shear deformation (called ) and another
invariant to vertical shear deformation (called ).
4,1_ hsi
CLBP S
4,1_ vsi
CLBP S

 Horizontal Shear Invariant operator
• To achieve horizontal shear invariant features, a mapping from LBP4,1
to is defined:
• The defined operator produces a lookup table with 2P (24) different
elements. Each element corresponds to one of the possible 16 patterns.
• As a result, the operator describes image with only 4 histogram bins.
p2 p2 p2
p3 gc p1 p3 gc p1 p3 gc p1
p4 p4
p4
Shifting
hsi
4,1 4,1 4,1sum bitget ,3 *2^1 , bitCLBP_S = ( (LBP ) (LBP )get ,1 *2^0)
4,1_ hsi
CLBP S

 Vertical Shear Invariant operator
• Vertically shearing invariant features is achieved by define the mapping
from LBP4,1 to :
• Also the defined operator produces a lookup table with 2P (24) different
elements and describes image with only 4 histogram bins.
p2 p2 p1 p3 p2
p3 gc p1 gc gc
p4 P3 p4 p4 p1
Shifting Shifting
vsi ^1 ^0
4,1 4,1 4,1CLBP_S =sum(bitget(LBP ,4)*2 ,bitget(LBP ,2)*2 )
4,1_ vsi
CLBP S

• Results based on Outex dataset
• Similarly, the proposed SITD achieved 100% recognition rate based on
shape-based images.
• Results based on papers textures dataset
Operator
Shearing on x-direction by software
Av.
P,R 2 P,R 7 P,R 15 P,R 30 P,R 60
SITD 4,1 100 4,1 100 4,1 100 4,1 100 4,1 100 100
LBP 4,1 87.7 4,1 86.5 4,1 80.3 4,1 72.9 4,1 65.3 78.5
CLBP 4,1 88.9 4,1 87.8 4,1 84.6 4,1 77.8 4,1 72.9 82.4
SMI - 71.3 - 37.5 - 18.7 - 6.2 - 3.1 27.3
Results Based on SITD
Operator
Resolution (DPI)
Av.
P,R 50 P,R 100 P,R 150
SITD 4,1 68.6 4,1 100 4,1 100 89.5
LBP 4,1 66.7 4,1 78.4 4,1 85.2 76.7
CLBP 4,1 66.9 4,1 80.1 4,1 86.8 77.9
SMI - 60.7 - 68.6 - 77.5 68.9

• The 180˚ rotation is an additional challenge of using scanners for
image acquisition.
• This deformation is very common to occur and produces by scanning
paper upside down at the moment of generating the query images.
• The available rotation invariant descriptors are lack to shear
invariance characteristic.
• Contrarily, the proposed shearing invariant features bins are suffer
from sequence changing affect by 180˚ rotation.
• As it is mentioned early, the SITD produces a 4 bins histogram feature
vector.
• The 1st and 4th bins represent the number of image patterns their
SITD are equals to 0 and 3, respectively.
Effects of 180˚ Rotation to the SITD

Effects of the Rotation to the SITD (2)
• However, the 180˚ rotation has no effect to these two bins.
• Conversely, the 180˚ rotation turns the patterns of the 2nd bin to be
counted as patterns of 3rd bins and vice versa.
gc gc gc
Origin Pattern Sheared Pattern
Sheared and 180˚
rotated Pattern
SITD=11=3 SITD=11=3 SITD=11=3
SITD=10=2 SITD=10=2 SITD=01=1
Origin Pattern Sheared Pattern
Sheared and 180˚
rotated Pattern
gc gc gc

The feature
vectors extracted
from the same
undistorted and
distorted images
are:
Effects of 180˚ Rotation to the SITD (3)
gc gc gc gc
0 2 8 10
gc gc gc gc
1 3 9 11
gc gc gc gc
4 6 12 14
gc gc gc gc
5 7 13 15
gc
gc
gc
gc
FV1=f1
FV2=f1
,f2
,f3
,f3
,f2
,f4
,f4

• A further effect of the 180˚ rotation is present in applications rely on
patches instead of the whole image, e.g. paper texture fingerprinting.
• The 180˚ rotation turns the sequence of patches (as well as the 2nd
and 3rd bins), and the result produces an incorrect image fingerprint.
a b
c d ab
cda
F1= a1,a2,a3,a4,
b
b1,b2,b3,b4,
c
c1,c2,c3,c4,
d
d1,d2,d3,d4
d
d1,d3,d2,d4,
c
c1,c3,c2,c4,
b
b1,b3,b2,b4,
a
a1,a3,a2,a4F2=

• To cope with the effects of 180˚ rotation, two different
solutions are proposed:
 A) By use the Across-bins matching techniques.
 B) By Develop the Rotation Shear Invariant Texture Descriptor
(RSITD).
• Furthermore, the Completed Rotation Shear Invariant
Texture Descriptor (CRSITD) approach is proposed.

Across-Bin Matching Techniques
• The techniques calculate the distance between pairs of images
features based on information across the dimension.
• These techniques are various, while many of them are not suitable
to be used with the SITD for statistical reason.
• Based on the theoretical studies phase, the Quadratic Distance
(QD) and Earth Mover Distance (EMD) techniques are found to be
the most reliable.
q1 q2 q3 q4 qn
r1 r2 r3 rm

• Results based on papers texture dataset
Results Based on Across-Bin Matching
Operator P,R QD EMD
Shearing on x-direction by software
2 7 15 30 60 Av. 2 7 15 30 60 Av.
SITD 4,1 90.2 88.4 84.8 79.4 75.4 83.6 88.9 87.3 85.2 77.6 70.1 81.8
LBP 4,1 78.4 70 63.4 42.8 27.5 56.4 76.8 69.1 60.2 36.4 21.6 52.8
CLBP 4,1 84.5 80.1 79.7 68.5 62.3 75.1 84.8 80.6 77.9 68.5 64.6 75.3
SMI - 70.6 52.5 51.6 30.3 30.3 47.1 63.3 68.6 58.7 46.8 39.8 55.4
Operator P,R QD EMD
50 100 150 Av. 50 100 150 Av.
SITD 4,1 62.7 84.3 86.3 77.8 39.2 60.8 72.5 57.5
LBP 4,1 60.8 47.1 74.4 60.8 34.3 50.9 58.8 48
CLBP 4,1 61.4 68.5 77.6 69.5 37.7 54.4 62.9 51.7
SMI - 45.1 45.1 56.9 49.1 27.6 41.6 51.4 40.2

The Proposed Rotation Shear Invariant
Texture Descriptor (RSITD)
• To eliminate the effect of 180˚ rotation on the SITD 2nd and 3rd bins
extracted from single image patch, a sorting operation is employed.
• By known that features extracted from undistorted and distorted
images are respectively take the following forms:
F1=f1,f2,f3,f4 F2=f1,f3,f2,f4
• By implementing an ascending sort to the bins, the vectors becomes:
F1=f1,f2,f3,f4 F2=f1,f2,f3,f4
• Hence, an equal feature vectors were achieved, i.e., F1= F2.

Texture Descriptor (RSITD) (2)
• Also, to eliminating the effect of the rotation on the sequence of
the image patches, e.g. in paper fingerprinting, a novel two steps
method is proposed:
 1) Ascending sort to the features bins extracted from first and
second patches and descending sort to the features bins
extracted from third and fourth patches to obtain:
F1=a1,a2,a3,a4,b1,b2,b3,b4, c4,c3,c2,c1,d4,d3,d2,d1
and
F2= d1,d2,d3,d4,c1,c2,c3,c4, b4,b3,b2,b1,a4,a3,a2,a1

Texture Descriptor (RSITD) (3)
 2) Sum the bins extracted from the first patch with the bins
extracted from the fourth patch by adding the first bin to the last
bin, and the second bin to the one before the last, respectively,
etc.
F1=a1,a2,a3,a4, b1,b2,b3,b4, c4,c3,c2,c1, d4,d3,d2,d1
Similarly
F2= d1,d2,d3,d4, c1,c2,c3,c4, b4,b3,b2,b1, a4,a3,a2,a1
As the addition operation is commutative, an equal feature vectors
are obtained, i.e., F1=F2
F1=a1+d1,a2+d2,a3+d3,a4+d4,b1+c1, b2+c2,b3+c3,b4+c4
F2=d1+a1,d2+a2,d3+a3,d4+a4,c1+b1, c2+b2,c3+b3,c4+b4

• Results based on standard shape-based images dataset
Operator
Shearing on x-direction
Av.
P,R Bins 2 7 15 30 60
SITD 4,1 4 100 100 100 100 100 100
LBP 4,1 230 75.6 73.7 63.9 59.7 46.8 63.9
CLBP 4,1 6/6/
407360
86.6 85.9 79.7 74.8 68.8 79.2
SMI - 7 68.3 34.5 16.7 5.8 4.1 25.9
Results Based on RSITD
Operator P,R Bins Accuracy
SITD 4,1 4 6/6
SMI - 7 3/6
LBP 4,1 6 3/6
CLBP 4,1 6/6/68580 4/6

Results Based on RSITD (2)
Operator
Resolution (DPI)
Av.
P,R 50 P,R 100 P,R 150
SITD 4,1 64.7 4,1 98.1 4,1 98.1 86.9
LBP 4,1+12,2
+24,3
27.5 4,1 58.9 4,1 62.7 49.7
CLBP 4,1 58.8 4,1 68.9 4,1 78.4 68.7
SMI - 52.9 - 58.8 - 60.8 57.5

The Proposed Completed RSITD (CRSITD)
• The CRSITD is a generalization to the RSITD. Its generate features based on
both, sign and magnitude components of local patterns.
• The proposed descriptor is three steps method:
 1) Extract shearing invariant features based on magnitude component.
 2) Achieve 180˚ rotation invariant features by applying the rotation invariant
method proposed early.
• The 180˚ rotation is effected the extracted features in a similar manner as with SITD features.
 3) Concatenate the obtained features to their corresponding from RSITD.
• The shearing invariant features are extracted based on the CLBP operator (Guo
& Zhang 2010):
• where mp represent the magnitude obtained from the local differences of P
neighbor pixels. The c represent the mean value of the mp from the whole image.
1
0,
1,
_ ( , )2 , ( , )
0,
pP
pP R p
x c
CLBP M t m c t x c
x c



  


The Proposed Completed RSITD
(CRSITD) (2)
• As a result, the CRSITD offers two operators:
 Horizontal Shear Invariant operator
• To achieve horizontal shear invariant features, a mapping from CLBP4,1 to
is defined:
 Vertical Shear Invariant operator
• Similarly, to achieve horizontal shear invariant features, a mapping from
CLBP4,1 to is defined:
• Each of these operator produces a lookup table with 16 elements represent
the possible 16 patterns which describe the image with only 4 histogram bins.
,_ hsi
P RCLBP M
^1 ^0
, 4,1 4,1_ ( ( _ ,3)*2 , ( _ ,1)*2 )hsi
P RCLBP M sum bitget CLBP M bitget CLBP M
,_ vsi
P RCLBP M
^1 ^0
, 4,1 4,1_ ( ( _ ,4)*2 , ( _ ,2)*2 )vsi
P RCLBP M sum bitget CLBP M bitget CLBP M

The Proposed Completed RSITD
(CRSITD) (3)
• As with the SITD operators, the proposed operators achieved the
180˚ rotation invariant features by implementing the rotation
invariant method.
• To make a significant improvement over the RSITD, the sign and
the magnitude operators are concatenated into joint distribution,
i.e. with and with .
• As a result, the and are obtained.
4,1_ rhsi
CLBP M4,1_ rhsi
CLBP S 4,1_ rvsi
CLBP M4,1_ rvsi
CLBP S
S1 S2 S2 … Sn M1 M2 M3 … Mn
S1 S2 S2 … Sn M1 M2 M3 … Mn
4,1 4,1_ /rhsi rhsi
CLBP S M 4,1 4,1_ /rvsi rvsi
CLBP S M

• AS the CRSITD was proposed to enrich the RSITD with additional
discrimination information, its guarantee to replicate the superior
performance of the RSITD based on the standard images, i.e.
Outex and shape-based datasets.
Results Based on CRSITD
Operator
Resolution (DPI)
Av.
P,R 50 P,R 100 P,R 150
CRSITD 4,1 76.5 4,1 100 4,1 100 92.2
SITD 4,1 64.7 4,1 98.1 4,1 98.1 86.9
LBP 4,1+12,2
+24,3
27.5 4,1 58.9 4,1 62.7 49.7
CLBP 4,1 58.8 4,1 68.9 4,1 78.4 68.7
SMI - 52.9 - 58.8 - 60.8 57.5

Conclusions
• Traditionally, shearing transform has attracted less attention compared with other
transformations as it has no direct real-world applications.
• In this study, a massive investigation have been conducted to the deformations of
scanned papers images concluded that the shear is among the main transforms.
• Hence, the study proposed a theoretically and computationally simple yet robust
Shear only Invariant Texture Descriptor (SITD) based on the conventional LBP
descriptor.
• In real-world image acquisition using flatbed scanners, its very common to rotate the
paper up side down. The study is therefore proposed a novel 180˚ rotation invariant
method. The method have been implemented with SITD features to achieve the
RSITD.
• The RSITD has been enhanced by proposing the Completed RSITD (CRSITD). The
descriptor employed to extract additional discrimination features from the image’s
local patterns and later concatenate them into their corresponding from RSITD to
generate more robust feature vector.

Conclusions (2)
• This study proposed inexpensive robust fingerprinting method which employ as a
platform to evaluate the proposed descriptors.
• The method used the ordinary flatbed desktop scanner and the proposed descriptors
to generate invariant papers fingerprints.
• The proposed CRSITD achieved an average of 92.2% correctly authenticated papers.
The other benchmark methods SITD, LBP,CLBP, and SMI achieved: 86.9%, 49.7%,
68.7%, 57.5%, respectively.
• The obtained results showed that the CRSITD features are:
 Very efficient against the scanners deformations, i.e., shear and 180˚ rotation
 They were extracted in concise manner. They represented the image patch with only 4
features bin without information redundancy.
• Also, as these features are based on RSITD, its inherited the ability to describe various
kind of texture images and images with distinctive-shapes as well.
• Compared to the other benchmark methods, the CLBP descriptor performed
reasonably well and that was attributable to the massive amount of information
extracted from image’s local pattern.

Research Contributions
• The review to the existing physical paper texture fingerprinting techniques has
been the largest and the most comprehensive of its type.
The following contributions are respectively related to the first four sub-objectives:
• An extensive investigation to the irregular rotation transform has been
provided. Also, new mathematical formula proposed to interpret the irregular
rotation phenomenon produced from rotate a paper based on corner pivot. In
addition, it’s measured the amounts of originated shear transform.
• New and original Shear Invariant Texture Descriptor (SITD) has been proposed.
The descriptor developed based on conventional rotation invariant Local Binary
Pattern (LBP) method.
• The Rotation SITD (RSITD) proposed to achieve 180˚ rotation invariant based on
the SITD features is new and novel complete solution to tackle all the possible
deformations produces during image acquisition process using flatbed scanners.

Research Contributions (2)
• The CRSITD was another new innovative technique exhibited an
improvement over the performance of the RSITD. The technique employed
to extract invariance images features against shear and 180˚ rotation.
• The proposed scanned paper texture fingerprinting is a novel document
authentication method. The method utilized inexpensive commodity scanner
to acquire papers textures and benefited from the superior feature
descriptors proposed in this work to extract unique fingerprints.
• The research exhibited another two minor contributions:
 The publicly available papers textures images dataset is the first in its
domain.
 The conciseness of the proposed descriptors considered a valuable
advantage especially in real-world applications. The single operators in
each of these descriptors generated only four robust bins feature vector.

Future Directions
• It would be interesting to develop an additional computer vision applications
uses scanners for image acquisition based on the CRSITD. Possible applications
may include, offline character recognition, digits recognition, old manuscript
restoration, etc.
• It would be interesting to recognize an images with lower resolution than the
used in the current work as its reducing the computation cost of both paper
scanning and images description. This however will challenge the features
descriptors as the images provide less information about the patterns.
• To overcome some additional recognition challenges, the proposed CRSITD can
be further improved. The process may include extract additional description
information from the image’s local patterns and concatenate them into these
currently generated by the descriptor. The new information can be obtain based
on the grey level of the local patterns centre pixels.

References
[1] Inbavalli, P. & Nandhini, G. 2014. Body Odor as a Biometric Authentication. International Journal of
Computer Science & Information Technologies, 5(5): 91-104.
[2] Clinick, A. J., Torr, P. J. & Hayes, S. 2013. Advanced content authentication and authorization. U.S. Patent
No. 8,473,739. Washington, DC: U.S. Patent and Trademark Office.
[3] Jayadevan, R., Kolhe, S. R., Patil, P. M. & Pal, U. 2012. Automatic processing of handwritten bank cheque
images: a survey. International Journal on Document Analysis and Recognition (IJDAR), 15(4): 267-296.
[4] Eldefrawy, M. H., Alghathbar, K., & Khan, M. K. 2012. Hardcopy Document Authentication Based on Public
Key Encryption and 2D Barcodes. In International Symposium on Biometrics and Security Technologies
(ISBAST), pp. 77-81, March.
[5] Sarkar, A., Verma, R., & Gupta, G. 2013. Detecting Counterfeit Currency and Identifying Its Source.
Advances in Digital Forensics IX, Springer Berlin Heidelberg, pp. 367-384.
[6] Clarkson, W., T. Weyrich, A. Finkelstein, N. Heninger, J.A. Halderman, and E.W. Felten. 2009.
Fingerprinting blank paper using commodity scanners. 30th IEEE Symposium on Security and Privacy. May
2009. 301-314.
[7] Shields, T. C., Frieder, O. & Maloof, M. A. 2013. Automated forensic document signatures. U.S. Patent No.
8,438,174. Washington, DC: U.S. Patent and Trademark Office.
[8] Wei, H., Zhu, H. D., Gan, Y. & Shang, L. 2014. A New Local Binary Pattern in Texture Classification. Springer
International Publishing, In Intelligent Computing Theory, pp. 700-705.
[9] Guo, Z. & Zhang, D. 2010. A completed modeling of local binary pattern operator for texture
classification. IEEE Transactions on Image Processing, 19(6): 1657-1663.

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