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 efficientover 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 affinelytransformed, 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.


ScopesThe 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

StartIdentify the problem

statementLiterature 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 rotationdeformation investigation.

•Develop the proposedShear Invariant TextureDescriptor (SITD).

•Achieve 180 RotationInvariant features based on(SITD).

•Develop the CompletedRSITD (CSITD).

Research Methodologies (3)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 Genuine Fake

DESIGN THE PROPOSED PAPER FINGERPRINTING METHOD



Stage 3 EXPERIMENTS

Exp. #

Reason of conduct Datasets # of images

Source of images

1 To evaluate the performance ofthe proposed methods basedon standard texture images.

-Outex 3200 -Artificial and natural materials textures.

2 To evaluate the performance ofthe proposed methods basedon standard shape images.

-Shape-based images

12 -Images with distinctive shapes.

3 To evaluate the performance ofthe proposed methods basedon 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 methodsStudying 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

- SMIAdd 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


Investigation of The Irregular Rotation 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


Investigation of The Irregular Rotation 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.

Experiment #

Reason of conduct # of images

Source of images

1 To evaluate the performance ofboth descriptors on sheardeformations produced undercontrolled conditions

12 Standard images (Barbara, Cameraman, Fingerprint,

Flintstones, House and Lena)

2 To empirically confirm that theirregular rotation phenomenon isa 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 : 0011Decimal: 3

1

,0

1 0( )2 , ( )

0 0

Pp

P R p cp

xLBP s g g s x

x

Binary : 1Binary : 11Binary : 011

Magnitude component

Sign component


The Proposed Shearing Invariant 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_ hsiCLBP S

4,1_ vsiCLBP S



Horizontal Shear Invariant operator

• To achieve horizontal shear invariant features, a mapping from LBP4,1to 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 p4p4

Shifting

hsi4,1 4,1 4,1sum bitget ,3 *2^1 , bitCLBP_S = ( (LBP ) (LBP )get ,1 *2^0)

4,1_ hsiCLBP S

Lookup Tables.docx


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 ^04,1 4,1 4,1CLBP_S =sum(bitget(LBP ,4)*2 ,bitget(LBP ,2)*2 )

4,1_ vsiCLBP S

Lookup Tables.docx


• Results based on Outex dataset

• Similarly, the proposed SITD achieved 100% recognition rate based on shape-based images.

• Results based on papers textures dataset

OperatorShearing 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

OperatorResolution (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 PatternSheared 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 PatternSheared 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 EMDShearing 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 EMD50 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.


The Proposed Rotation Shear Invariant 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

andF2= d1,d2,d3,d4,c1,c2,c3,c4, b4,b3,b2,b1,a4,a3,a2,a1


The Proposed Rotation Shear Invariant 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

OperatorShearing 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/

40736086.6 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)


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

+24,327.5 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 Pneighbor pixels. The c represent the mean value of the mp from the whole image.

10,

1,_ ( , )2 , ( , )

0,

pPpP R p

x cCLBP 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.

,_ hsiP RCLBP M

^1 ^0, 4,1 4,1_ ( ( _ ,3)*2 , ( _ ,1)*2 )hsi

P RCLBP M sum bitget CLBP M bitget CLBP M

,_ vsiP 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_ rhsiCLBP M4,1_ rhsiCLBP S 4,1_ rvsiCLBP M4,1_ rvsiCLBP S

S1 S2 S2 … Sn M1 M2 M3 … Mn

S1 S2 S2 … Sn M1 M2 M3 … Mn

4,1 4,1_ /rhsi rhsiCLBP S M 4,1 4,1_ /rvsi rvsiCLBP 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


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

+24,327.5 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 chequeimages: 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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PhD Viva Presentation-9-6-2015

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