Palmprint Classification

People involved:People involved:- Li Fang (Lecturer) Li Fang (Lecturer) - Maylor Karhang Leung (Assoc Prof) Maylor Karhang Leung (Assoc Prof) - Kean Fatt Choon (Final Year Project student)Kean Fatt Choon (Final Year Project student)

Palmprint ClassificationPalmprint Classification

TaskTask

Create a hierarchical system to improve the speed of palmprint recognition

ContentsContents

Victor - Victor - Introduction, Research, conventional process

Tejas - Tejas - Algorithm, explanation of various categories

IntroductionIntroductionWhat is palmprint recognition?What is palmprint recognition?

Form of computer-aided personal recognition

Capturing images of palmprint and matching it with the database

Use for security purposes in many countries

DefinitionsDefinitionsIntroduction to Introduction to

principal linesprincipal lines

Life Line

Head Line

Heart Line

RationaleRationaleWhy palmprint?Why palmprint?

Widely used by many security agencies.

Cost effective

Non-intrusive

Possible to build highly accurate biometric system

RationaleRationaleWhy others methods such as iris and Why others methods such as iris and

fingerprint are not highly effectivefingerprint are not highly effective ?

Iris input devices are expensive.

Iris is intrusive

Fingerprint require high definition capturing devices.

Some may be finger deficient

Contains 1000 imagesPalmprint Capture

Input

Database

Result

Output

BEGIN

Match with user’s registered palm print in the database?

END

FalseTrue

LimitationsLimitations Image captured has to be matched with

every single image in database

Time consuming

Too high computational complexity to be applicable

Aims & ExpectationsAims & Expectations Our aim is to speed up this process by adding in 2

extra filters before the palm print is matched

We expect to increase the speed of the recognition which is one of the most deterring limitation

SurveySurvey Conducted a survey among people

living in Singapore• Gender • Age• Nationality

Survey can be used in our study and design of algorithm which will suit the residents here.

Survey ResultSurvey ResultFrom our survey,

The population palms can be classified into 6 categories (elaborated in the later slide)

Majority of the population lies in one category.

However, significant amount of the population still falls under the other categories

Studies have shown...Studies have shown... According to the algorithm proposed According to the algorithm proposed

on the research paperon the research paper

The algorithm proposed categorizes the palmprints into 6 categories

Palm Categories

cat 1cat 2cat 3cat 4cat 5 cat 6

Cat 1

Cat 5Cat 4

Cat 3Cat 2

Cat 6

Palm Categories

ResultResultAlgorithm proposed by the research Algorithm proposed by the research

paperpaper

cat 1

cat 2

cat 3

cat 4

cat 5

cat 6

Category 5Category 5

New AlgorithmNew AlgorithmWhy a new algorithm is required?Why a new algorithm is required?

78% of the people lie in the 5th category

Based on the current system, the input image has to be matched with every image in the database before the result is obtained

FlowchartFlowchart

Image matching with the images in same category in data base

Categories with the new algorithm

Result

Categories with the initial algorithm

Belong to Category 5?

N

Input Palmprint

Y



Result



N

Input Palmprint

Y

FlowchartFlowchart



Result



N

Input Palmprint

Y

FlowchartFlowchart

In Cat. 1



Result



N

Input Palmprint

Y

FlowchartFlowchart

In Cat. 1NOT CAT. 5



Result



N

Input Palmprint

Y

FlowchartFlowchart



Result



N

Input Palmprint

Y

In Cat. 1NOT CAT. 5

Not Cat5

Compare

Result

FlowchartFlowchart



Result



N

Input Palmprint

Y

FlowchartFlowchart



Result



N

Input Palmprint

Y

In Cat. 5

FlowchartFlowchart



Result



N

Input Palmprint

Y

In Cat. 5True

FlowchartFlowchart



Result



N

Input Palmprint

Y

In Cat. 5E.g cat. A

FlowchartFlowchart



Result



N

Input Palmprint

Y

In Cat. 5Cat A.

Compare

Cat A

Result

New ProcessNew Process

Contains 1000 Images

Palmprint Capture

Input

Database

Result

Cat A Cat B Cat C

Cat D Cat E Not Cat5

New AlgorithmNew AlgorithmStep 1Step 1

The first line connected from the end of the little finger to the intersection of the life line and head line (green line)

The second line is connected from the end of life line to intersection of life and head line (red line)

The third line is connected from point of intersection green line and heart line to midpoint of red line (purple line)


Draw a triangle inside the triangle by connecting the mid points of the each line

Divide the two triangle into 4 parts as shown


Draw a line from end of heart line to end of life line

Draw a line from beginning of heart line to the intersection of life line and head line

The location of the point of intersection of these 2 lines can then be used to categorize the palm

ImplementationImplementation

Category A Category B

Category C

Category D Category E

ResultResult

We tried this algorithm on 100 subjects The pie chart above shows the percentage of each

category It can be concluded that algorithm proposed is

effective

Cat A

Cat B

Cat C

Cat D

Cat E

17.6%

22.3%

18.3%

23.1%

18.7%

SummarySummary Our research showed that process of Our research showed that process of

palmprint recognition is inefficient and can palmprint recognition is inefficient and can be improvedbe improved

Our survey analysis revealed that most Our survey analysis revealed that most people lie in one particular categorypeople lie in one particular category

Proposed a robust algorithm via study of Proposed a robust algorithm via study of characteristics of principal lines to reinforce characteristics of principal lines to reinforce the method of palmprint classificationthe method of palmprint classification

Tried out the proposed algorithm on 100 Tried out the proposed algorithm on 100 subjects to investigate its effectivenesssubjects to investigate its effectiveness

The EndThe End

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Palmprint Classification