International Journal of Engineering Trends and Technology (IJETT) – Volume 34 Number 6- April 2016
3-D Palm Print Authentication by Global
Features
Mr Biju V G1, Mr Anu S Nair2, R Vasantha Kumar, Vinodhini M, Vinitha M, Rani S3
Professor, Department Of ECE, College Of Engineering, Munnar, Kerala,
Assistant Professor, Department Of ECE, College Of Engineering, Munnar, Kerala,
UG Scholar, Department Of ECE, College Of Engineering, Munnar, Kerala,
Abstract—Palm-Prints Have Been Widely Studied For
Personal Authentication Because They Are Highly Accurate
And Incur Low Costs. Most Of The Previous Work Has
Focused On 2-D Palm-Print Identification. In This Paper,
The 3-D Palm-Print Are Developed Based On Global
Features. The Three Proposed Global Features Are
Maximum Depth Of Palm Center, Horizontal Cross
Sectional Area Of Different Levels, And Radial Line Length
Which Are Used For Personal Authentication. A 3-D PalmPrint Database That Contains 26 Samples Has Been
Established By Using The Developed Acquisition System,
And The Test Results Illustrate The Effectiveness, Efficiency
Rate Of 89.6 And Accuracy Rate Of 87.8 Of Our System.
Keywords— Global Features, Orthogonal Linear
Discriminant Analysis (LDA) (OLDA), Palm-Print Indexing,
Ranking Support Vector Machine (SVM) (RSVM), 3-D
Palm-Print Identification
I. INTRODUCTION
Most Reliable Technology Used For Personal
Authentication Is BIOMETRICS Which Provides
Higher Level Of Security. Normally Used Biometrics
Are Fingerprint, Iris, Face, Signature, Ear Etc. When
Compared To Other Biometrics, Palm Prints Are
Larger Than Fingerprints And Are More Robust To
Scars And Dirt. Palm Print Images Are More
Acceptable Than Iris And Cheaper To Collect. Palm
Prints Can Accurately Distinguish Between
Individuals Than By Face And Can Also Identify
Monozygotic Twins [1].
Although 2-D Palm Print Recognition Techniques
Can Achieve High Accuracy, It Can Be Counterfeited
Easily And Much 3- D Palm Structural Information Is
Lost In The 2-D Palm Print Acquisition Process [2].
The Main Features Of 2-D Palmprint
Include
Principal Lines And Wrinkles[2].
In This Paper 3-D Techniques Have Been Used In
Biometric Authentication, Compared With Other 3-D
Biometric Characteristics,3-D Palmprint Has Some
Desirable Properties. In The Data Acquisition Process,
The Palm Can Be Placed Easily So That The
Collected Data Are Very Stable. In 3-D ,The
Curvature Features Of Palm Can Be Well Captured
By Using The Developed Structured Light Imaging
System[5]. With The Proposed Feature Extraction
And Matching Procedures, Whole 3-D Palmprint
Recognition System Can Reach Very High
ISSN: 2231-5381
Performance In Accuracy, Speed And Anticounterfeit
Capability.
In This Paper, The Three Novel Global Features
Are Extracted From A 3-D Palm-Print Image:
Maximum Depth (MD) At The Centre Of The Palm,
Horizontal Cross-Sectional Area (HCA) At Different
Levels Of The Palm; And Radial Line Length (RLL)
Measured From The Centroid To The Boundary Of
The 3-D Palm Print. Continuous Classification
Technique Is Also Widely Used For Indexing The
Database For Personal Identification[9].
These
Features Are Then Used To Describe And Classify
The Shape Of The 3-D Palm Print Using Continuous
Classification Only.
II. GLOBAL FEATURES EXTRACTION
The Following Describes The Procedure By First
Extracting A Region Of Interest(ROI) And Then
Three Proposed Global Features Are Extracted
A. ROI- Region Of Interest
Fig 1.Block Diagram Of ROI
A High Resolution 3-D Palm Print Acquisition
Device [5] Is Used To Capture Palm Print Image
Containing 768 X 576 Point. Then By Segmenting A
400 X 400 Point Square That Is 68, 108, 234, And 134
Points From The Top, Bottom, Left, And Right
Boundaries Of The 3-D Image Is The Location Of The
ROI. Then ROI Is Extracted Into 200 X 200 Matrices,
I = 1,2….200; J = 1, 2…..200, Where
Is
The Depth Value Of The Ith Row And Jth Column
Point Of The 3-D ROI
This Large ROI May Contain Noise, Based On The
Gradient A Mask Is Used To Remove Noisy Data. If
The Gradient Is Larger Than A Given Threshold, The
Point Is Regarded As Noisy Data. A 200 By 200
Matrix Is Used,
= 1, 2..,200; J = 1,2.., 200; To
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International Journal of Engineering Trends and Technology (IJETT) – Volume 34 Number 6- April 2016
Represent The Mask, Where
And
= 1 Is For Other Data.
= 0 Is Noisy Data
B. Extraction Of Three Global Features
The Three Global Features: MD, HCA, And RLL
Are Extracted Using The ROI Obtained From The
Original 3-D Palm Print Data
1) MD: MD Is The Maximum Depth Value Of The
3-D Palm From A Reference Plane. A Rectangle Is
Used To Decide The Reference Plane. The Depth Of
The Reference Plane
Is The Mean Depth And
Can Be Calculated By
Where Denotes Logical AND, ⊕ Represents A
Morphological Dilation Operation, And ⊝ Is The
Disk Morphological Structuring Element Whose Size
Can Be Calculated By 35 3 K.
(4)
(1)
And
Denote The Start Row, End Row,
Start Column, And End Column, Respectively.
Is
The Depth Value Of The Ith Row And Jth Column
Point Of The ROI,
Is The Corresponding Mask
Value. The Parameters
= 65,
=136, = 6, And
= 35 Were Set By Experience. This Region Is
Region Is Chosen Because The Bit Will Appears As
Flat. After Getting The Reference Plane, The MD Can
Be Calculated By Equ(2) Which Starts At 41st Row
And Extends To 160th And From The 65th Column
To The 190th Column
(2)
2) HCA- The HCA Is Defined As The Area
Enclosed By The Level Curve. A Group Of
Equidistant Horizontal Plane Is Used To Cut The 3-D
ROI To Describe The Shape Of 3-D Palm Print. And
Different Levels Are Represented In RGB, The Blue
Curves Denote Deeper Levels, The Red Curves
Denote Higher Levels, And The Remaining Curves
Are Medium Levels. From Fig.2 It Is Understood That
Most Of The Deeper Level Curves Are Enclosed And
The Areas Are Simply Connected. These Are More
Stable In Response To Noise Or Transformation
The Levels From The Deepest Point To The
Reference Plane Are Taken Into The Consideration To
Get The Stable HCA. Suppose, The 3-D ROI Region
Is Divided Into N Levels. Every Level , K = 1, 2
...N, Is Described With A 200 200 Matrix And
Calculated By
, If
otherwise Equal To 0,If
Where
Is The Depth
Value Of The Ith Row And Jth Column.
To Make It More Stable, Every Growing Level
Is Constrained From Its Previous Level Except The
First Level. That Is,
Fig. 2: N=8 Level Stacked
3) RLL: The HCA Is Basic For Finding RLL. To
Identify Samples Which Have A Similar CrossSectional Area But A Different Contour, The RLL
Feature Which Describes The Shape Of The Contour.
The Centroid Of The First Level
Is Calculated
First; Then, It Is Treated As The Centroid Of All
Levels. Then, From The Centroid, M Radial Lines
Are Drawn At Equal Angles Which Intersect With
The Contour Of Every Level. The Distance Between
The Intersection And The Centroid Is Defined As The
RLL. Record These Radial Lines From The Inner
Layers To The Outer Layers Starting With The
Horizontal Direction By An
Dimensional
Vector
; Where M Is The
Number Of Radial Lines And N Is The Number Of
Cross Sections.
Fig. 3: Radial Line Starting From The Centroid
M=8,16,32,64 Respectively
(3)
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International Journal of Engineering Trends and Technology (IJETT) – Volume 34 Number 6- April 2016
III. DIMENSION REDUCTION AND
CLASSIFICATION
The Measurements Used In This Paper Are
Reducing Dimensionality Using Orthogonal Linear
Discriminant Analysis
(OLDA) [10], Coarse-Level Matching For Efficient
Palm-Print Recognition And Rank Support Vector
Scheme (RSVM) [10] For Rank The Candidate
Samples In The Database.
Of Interest (ROI) That Has Been Divided Into N
Levels And M Radial Lines For Represent The Level
Contours. Initially, List The Global Features As A
Column
Vector,
With
Rows. Suppose The Training Database Which Has N
Samples And K Classes As
Adopting OLDA [10], Then The Optimal Projection
W Can Be Calculated As,
Where
Where M Is The Centroid Of The Ith Class Xi.
After Calculating
,
And ,SVD Is Applied To
. After Getting The Optimal Projection W = Q,
Then The 1 + N + N M Can Be Mapped From
Dimensional Vector To A Lower Dimensional Space
Γ.
Fig. 4: Flow Chart Of Registration And Recognition
With Coarse
Level Matching
A. Dimension Reducing Using OLDA
This Dimensionality Reducing Technique Is Used
In Classification Problems. The Main Objective For
This Technique Is To Find Optimal Projection. LDA
Requires The Sample Size In Database Should Be
Large When Compared To Its Dimension, But It Is
Not Always Possible, Therefore OLDA Is Used For
Reducing Dimensionality. Consider The 3D Region
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B. Coarse-Level Matching
The Purpose Of This Technique Is To Improve
The Efficiency Of Palm-Print Recognition And To
Speed Up The Identification During Retrieval. Here Γ
Dimensional Global Features Is Used For CoarseLevel Matching. Two Levels Of Matching Fine-Level
And Coarse Level Are Used In Matching. If The
Testing Sample Undergoes A Coarse-Level Matching
Then It Moves To A Fine-Level Matching, If It Does
Not Pass It Moves On To A Next Sample Until The
Last Sample In The Database. Coarse Level Matching
Is Much Faster Than The Fine-Level Matching .By
Mean Curvature Image (MCI) [2] Fine-Level
Matching Can Be Expressed As
(11)
Where
And
Features .
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Are The Two Binarized MCI
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International Journal of Engineering Trends and Technology (IJETT) – Volume 34 Number 6- April 2016
C. Rank Support Vector Machine (RSVM)
Ranking The Candidates Samples In Database In
Descending Order According To The Global Features
And Search For The Closest Matches Then It Is Quite
Difficult. Various Methods Have Been Proposed To
Speed Up The Nearest Neighbor. Here RSVM Method
Is Adopted To Rank The Candidates Sample In The
Database. Consider The Class Of Linear Ranking
Functions. Where Is The Weighted Vector, Hence
The Problem Is To Minimize Into The Following
SVM Classification Problem By Introducing Slack
Variable (I,J,K.).Input The Ranks Together With The
Γ Dimensional Global Features Into The RSVM
Algorithm To Know The Optimal Ranking Function.
Then A New Query Q, The Samples In The Database
Can Be Sorted By Their Value Of.
(12)
Fig. 5
IV. EXPERIMENTAL RESULTS
In This Paper The 3-D Palm-Print Acquisition
Device Developed In [5] Used To Establish A 3-D
Palm-Print Database Containing Samples Collected
From 2 Palms. The 3-D Palmprint Samples Were
Collected In Two Separated Sessions, 13 Samples In
Each Session. The Average Time Interval Between
The Two Sessions Is 1 Month. The Original Spatial
Resolution Of The Data Was 768 576. After ROI
Extraction, The Central Part (400
400) Was
Extracted And Downsampled To200 200 For Feature
Extraction And Recognition. The Database Was
Divided Into A Training Part And A Testing Part. As
Explained In Section III, The Dimension Of The
Proposed Global Features Is 1 + N + N M. To
Select The Values Of M And N, A Series Of
Verifications Was Carried Out On The Training
Database Where The Class Of The Input Palm Print
Was Known. Each Of The 3- D Samples Was
Matched With The Remaining Samples In The
Training Database.
When A Input Image Is Selected, ROI And Global
Features Are Extracted From The Given Input After
Selecting Global Feature Button In The Output Panel.
Then The Given Input Image Will Be Tested Whether
Input Is Matched With Any One Of Sample In The
Database Or Not. A Successful Match (Fig.5) Is
Where The Two Samples Are From The Same Class.
This Is Referred To As Intraclass Matching, And The
Candidate Image Is Said To Be Genuine. An
Unsuccessful Match (Fig.6) Is Referred To As
Interclass Matching, And The Candidate Image Is
Said To Be An Impostor
Fig.6
IV. CONCLUSIONS
In This Paper The Three Global Features
Used Are : MD, HCA And RLL. These Are Extracting
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International Journal of Engineering Trends and Technology (IJETT) – Volume 34 Number 6- April 2016
The Features Of Images. These Are Not Correlated
With 2D Images. It Improves The Efficiency Of 3D
Palm Print Recognition Using RSVM And CoarseLevel Matching By Treating Them As A
Multidimensional Vector And Use OLDA To Map It
To A
Dimensional Space. Our Recognition
Experiments Using An Established 3-D Palm-Print
Database Of 26 Samples Show That The Global
Features Improve Palm-Print Classification Which
Greatly Reduces Search Times.
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