Partial face recognition using core features of the face

Student ID :

Intake ID :

Student

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:

Supervisor :

Assessor :

Email : vssen2006@gmail.com

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A

. Project Title.

Partial face recognition using core features of the face

B

.

Brief description on project background.

(.i.e. problem context, rationale, description of problem area, nature of challenge)

Problem context

In modern world security is a one of main concern. Because there is a significant rise

of threats to society with higher increasing rate of crimes and terrorist activities. As a

result of that there is a huge usage of surveillance systems to ensure security of lives

and properties of the citizens in the society.

There are different ways to identifying a particular person. Biometric identification

approaches have achieved huge attraction because of the accuracy and uniqueness of

the biometric factors of a person which cause to give high accurate result. Among

biometric identification approaches like finger print recognition, palm recognition, iris

recognition and voice recognition, face recognition acts an important role because

comparing with other approaches, face recognition approach does not requires

people’s cooperation. The advantage of face recognition approach is people do not

need to look into an iris scanner, to place their hands on a fingerprint reader, or to

speak to a close-by microphone. Hence face recognition can useful in footages taken in

surveillance and security applications.

Because of that usage of face recognition in surveillance systems has increased

significantly in many places such as buildings with restricted access, air ports and

banks to strengthen the security.

Rationale

But when it come to real world, It might not be possible to capture full frontal picture

of a face at all the times in uncontrolled environments. Even though there are many

face recognition systems available, most of these work in optimal conditions.

Especially without full frontal face, these systems fail to recognise a face. As a result

of that most of system cannot give accurate face match results. Because of that there

can be lot of complications in identifying a person in an image.

Apart from that in most times criminals are trying to cover their faces and also impact

of different cultures and environment factors sometimes it is not possible to expose

full face. In those situations normal face recognition approaches fail to give well

accurate result.

Description of problem area

The reason for failing current systems are they can not identify partial parts of the

faces comparing with full faces because current approaches are not capable of

identifying individuals using partial face regions which has less characteristics in small

face region comparing with full faces.

The proposed system will identify individual’s 2D frontal (0 degree camera angle) face

images using different three face regions (eye region, nose region and mouth region)

which submit to system as input.

Benefits

The purposed solution will help to identify individuals using partial face regions

Normally to identify a person it is necessary to have full frontal face but in this

approach it is only enough to have a partial face region(eye section, mouth

section ,nose section)

Improved recognition speed

As mentioned by Teo, Neo & Teoh (2007) by using eye region to face

recognition it is possible to reduce the processing time and increase the

efficiency of the system.

A good method for identify partially disguised faces

Nature of challenge

The challenges of this project can divide into two main sections.

Academic challenge

Since there are lot of approaches for full frontal face recognition there are no such

techniques for partial face recognition. Identifying appropriate approach for partial

face recognition by investigation and analysing existing popular methods like

Eingenface(PCA),Linear Discriment Analysis(LDA) ,Elastic Graph

Matching(EGM),Neural Network(NN),Support Vector Machine (SVM) will be a

challenge.

Again above challenge can be divide in to sub parts based on main functionalities

1. Identify submitted face region

2. Identify unique characteristics of submitted regions

3. Detect appreciate face region from the database that need to match with

submitted face region

4. Match and provide the results

Technical Challenge

As mentioned above there can be lot of approaches for face recognition because of that

selecting a proper programming environment that allow to develop the application

should be done after identifying and analysing capabilities of platforms.

The research will be carried out to overcome above challenges.

Reference

Teo, C.C. Neo, H.F and Teoh, A.B.J. (2007). A study on partial face recognition of

eye region. International Conference on Machine Vision. [Online] .25(29) P.46-49.

Available from -

http://ieeexplore.ieee.org/Xplore/login.jsp?url=http%3A%2F%2Fieeexplore.ieee.org%

2Fiel5%2F4459057%2F4469254%2F04469271.pdf%3Farnumber%3D4469271&auth

Decision=-203. [Accessed: 24/01/10]

. Brief description of project objectives.

(i.e. scope of proposal and deliverables)

Scope of this project

This project is for develop a system that allows to recognize individuals using partial

regions of face. Initially the project is based on recognize individuals using submitted

face regions.

The Following limits will apply

This project will only focus on identifying individual using only eye region ,

nose region and mouth region

The all images have taken in 0 degree camera angle which mean all images are

frontal view images and taken in controlled environment.

The eyes should be open and mouth should be closed in the faces. All faces

should be in neutral mood

Core Functionality of the artefact can identify as follows

The main functionality of the solution is “recognize individuals using partial

face regions”.

Extra functionality

Detect and extract different face regions from submitted full frontal face and

recognize individuals based on partial face regions.

Furthermore, the solution will not work on recognizing in following situations

Disguised faces which cannot use for extract at least one of eye region or nose

region or mouth region.

Different poses of face regions.(taken in different angles)

Low quality images ( less resolutions,etc)

Different facial expressions

The proposed artefact will develop a standalone application which runs on PC.

D

.

Brief description of the resources needed by the proposal.

(i.e. hardware, software, access to information / expertise, user involvement etc.)

Hardware

To carry out this project it does not require any special hardware other than well

equipped computer.

Software

Following software will need to carry out this projects

Applications

Visual Studio 2008

MatLab R2009b

MatLab Tool kits for

Image Processing

Neural Networks

Microsoft SQL Server 2005

API and Wrappers

OpenCV OpenCV 2.0

EmguCV wrapper Version 2.0.1.0

Access to information

As a primary expertise learning sources www.face-rec.org and face detection web

communities.

User involvement

Users will involve to test and evaluate the final artefact

E

.

Academic research being carried out and other information, techniques being

learnt.

(i.e. what are the names of books you are going to read / data sets you are going to

use)

Since this is not take primary research because this does not required domain

knowledge. The academic research of this project can divide into sub parts based on

main modules of the artefact. The referring materials will be based on those modules

Input identification module

This module will identify weather submitted region is a nose , mouth and eye region

Jain, A.K .Zhong, Y and Lakshmanan, S. (1996). Object matching using deformable

templates. IEEE Transactions on Pattern Analysis and Machine Intelligence. [Online]

18 (3) .P267-278. Available from -

http://ieeexplore.ieee.org/Xplore/login.jsp?url=http%3A%2F%2Fieeexplore.ieee.org%

2Fiel1%2F34%2F10362%2F00485555.pdf%3Farnumber%3D485555&authDecision=-

203. [Accessed: 24/01/10]

Al-Mamun,I.H.A. Jahangir,N. Islam,M.S and Islam,M.A.(2009).Eye Detection in

Facial Image by Genetic Algorithm Driven Deformable Template Matching .IJCSNS

International Journal of Computer Science and Network Security. [Online] 9(8).P287-

294. Available from - http://paper.ijcsns.org/07_book/200908/20090840.pdf.

[Accessed: 24/01/10]

Gupta,N.Gupta .R,Singh,A & Wytock,M. (2008). Object Recognition using Template

Matching. [Online]. 12th December 2008. Available from -

http://stanford.edu/~nikgupta/reports/cs229-report.pdf. [Accessed: 24/01/10]

Face region detection module

This module will detect the region of the full face to match

Gourier, N.Hall, D.Crowley, J.(2004). Facial features detection robust to pose,

illumination and identity. IEEE International Conference on Systems, Man and

Cybernetics. [Online] 1. P.617-622. Available from -

http://ieeexplore.ieee.org/xpl/freeabs_all.jsp?arnumber=1398368. [Accessed: 24/01/10]

Gourier, N.Hall, D.Crowley, J.(2004). Facial features detection robust to pose,

illumination and identity.IEEE Computer Society. [Online] 1(0).P617-622. Available

from -http://ieeexplore.ieee.org/xpl/freeabs_all.jsp?arnumber=1398368. [Accessed:

24/01/10]

Wilson,P.I.Fernandez,J.(2006). Facial feature detection using Haar classifiers.Journal

of Computing Sciences in Colleges. [Online] 21(4).P127 - 133. Available from -

http://portal.acm.org/citation.cfm?id=1127416&dl=&coll=&CFID=72892264&CFTO

KEN=26634221. [Accessed: 24/01/10]

Face match module

This will match face region against faces in database

Savvides, M.Abiantun, R.Heo, J.Park, S.Xie, C.Vijayakumar, B.V.K.(2006). Partial &

Holistic Face Recognition on FRGC-II data using Support Vector Machine.2006

Conference on Computer Vision and Pattern Recognition Workshop (CVPRW'06).New

Yourk,17th June 2006.New York: IEEE Computer Society. pp.48-54.

Teo, C.C. Neo, H.F and Teoh, A.B.J. (2007). A study on partial face recognition of eye

region. International Conference on Machine Vision. [Online] .25(29) P.46-49.

Available from -

http://ieeexplore.ieee.org/Xplore/login.jsp?url=http%3A%2F%2Fieeexplore.ieee.org%

2Fiel5%2F4459057%2F4469254%2F04469271.pdf%3Farnumber%3D4469271&auth

Decision=-203. [Accessed: 24/01/10]

Sato,K.Shah,S.Aggarwal,J.K.(1998).Partial face recognition using radial basis function

networks.Third IEEE International Conference on Automatic Face and Gesture

Recognition, 1998. Proceedings. [Online] 14(16). P.288-293. Available from -

http://ieeexplore.ieee.org/Xplore/login.jsp?url=http%3A%2F%2Fieeexplore.ieee.org%

2Fiel4%2F5501%2F14786%2F00670963.pdf%3Farnumber%3D670963&authDecision

=-203.[Accessed: 24/01/10]

Sato,K.Shah,S.Aggarwal,J.K.(1998).Partial face recognition using radial basis function

networks.IEEE Computer Society, 1998. Proceedings. [Online] 14(16). P288-293.

Available from -

http://ieeexplore.ieee.org/Xplore/login.jsp?url=http%3A%2F%2Fieeexplore.ieee.org%

2Fiel4%2F5501%2F14786%2F00670963.pdf%3Farnumber%3D670963&authDecision

=-203.[Accessed: 24/01/10]

Yuen,C.T.Rizon,M.San,W.S and Seong,T.C.(2010).Facial Features for Template

Matching Based Face Recognition.American J. of Engineering and Applied Sciences,

2010. Proceedings. [Online] 3(1).P899-903. Available from -

http://www.scipub.org/fulltext/ajeas/ajeas31899-903.pdf.[Accessed: 24/01/10]

But I will not limit only to above sources. But above sources will be use as primary

sources apart from that following books will be refer but not limit to

Li,S.Z.Jain,A.K. (2005). Handbook of face recognition. [Online] USA: Springer

Science & Business. Available from -

http://books.google.com/books?id=amVDaTdgKYcC&dq=face+recognition&source=g

bs_navlinks_s.[Accessed: 24/01/10]

Nixon,M.S.Aguado,A.S. (2008). Feature extraction and image processing. [Online]

London: Academic Press. Available from -

http://books.google.com/books?id=jXmJqzQgdY8C&dq=matlab+image+processing&s

ource=gbs_navlinks_s.[Accessed: 24/01/10]

Data Set

The AR Face Database, Purdue University, USA

“4,000 colour images corresponding to 126 people's faces (70 men and 56 women).

Images feature frontal view faces with different facial expressions, illumination

conditions, and occlusions (sun glasses and scarf).”

(Face Recognition Homepage , 2010)

Face Recognition Data, University of Essex, UK

“395 individuals (male and female), 20 images per individual. Contains images of

people of various racial origins, mainly of first year undergraduate students, so the

majority of individuals are between 18-20 years old but some older individuals are also

present. Some individuals are wearing glasses and beards.”

(Face Recognition Homepage , 2010)

Above datasets will be use to generate face regions and recognitions. May be

combinations of images in database will use due to lack of frontal face images in one

database.

If it will request more images the images in other datasets like Yale Face Recognition

Database B (Face Recognition Homepage, 2010) will be taken.

Reference

Face Recognition Homepage. (2010). Face Recognition Homepage-Databases.

[Online]. Available from - http://www.face-rec.org/databases/. [Accessed: 26 January

2010]

F

.

Brief description of the development plan for the proposed project.

(i.e. which software methodology and why, the major areas of functions to be

developed and the order in which developed)

When selecting a developing methodology, it should select a methodology based on

characteristics of the project.

The characteristics of the project.

Project is short term and will develop by an individual.

The features of the artefact can be categorise based on modules

It might need to change or enchased some functionalities and features

Some of modules are depend on each other’s functionalities because of that

without completing one and testing one module it is not possible to continue

with next one.

By analysing characteristics of the project, a variation of evolutionary prototyping,

incremental prototype will be use in this project.

Justification

As mentioned by Albert, Yeung & Hall (2007, p352) “In incremental prototyping

applications are developed and delivered incremental after establishing the overall

architecture of the system. During the prototyping process, requirements and

specifications for each Increment are developed and refine............................. When all

the increments are developed, a rigorous integration process is undertaken to ensure

that all increments and related software modules are functionally operational with one

another.”

As mentioned above the proposed solution is based on different software modules

because of that it is ideal for use incremental prototype for this project.

Apart from that SQA (2007) has mentioned that because of the increment based

approach the output of each increment can be test individually which might influence

the outcome of further development. In this project the accuracy of a modules directly

impact to the success of other modules. By using incremental prototyping it is possible

to evaluate success of module while it is developing another unrelated module and

build or add modules on top of previous module.

Development Plan

Phase 1 Academic Research Week 4 – Week 14 Week (Duration 10 Weeks)

Phase 2 Analysing and Designing Week 16 – Week 18 (Duration 2 Weeks)

Phase 3 Development Increment 1 Image Pre-processing Module Week 19- Week 20 (Duration 1 Weeks)

Phase 4 Development Increment 2 Face region Identification module Week 21- Week 22 (Duration 2 Weeks)

Phase 5 Development Increment 3 Face region detection module Week 23 - Week 25 (Duration 3 Weeks)

Phase 6 Development Increment 4 Face Match Module Week 26- Week 28 (Duration 3 Weeks)

Phase 7 Development Increment 5 Final Integration Week 29- Week 30 (Duration 1 Weeks)

Phase 8 Testing, Evaluation and correcting Week 31- Week 32 (Duration 1 Weeks)

Phase 8 Finalizing Documentation and Submission Week 31- Week 32 (Duration 1 Weeks)

Reference

Albert, K. W. Yeung, G.Hall, B.(2007).Spatial database systems: design,

implementation and project management.[Online] Dordrecht: Springer. Available

from-

http://books.google.com/books?id=AMX44TGkvWkC&dq=incremental+prototyping&

source=gbs_navlinks_s. [Accessed: 24/01/2010]

SQA. (2007). Incremental Prototyping. [Online]. Available from -

http://www.sqa.org.uk/e-learning/IMAuthoring01CD/page_09.htm. [Accessed:

24/01/2010]

G

.

Brief description of the evaluation and test plan for the proposed project.

(i.e. what is the success criteria and how will be evaluated & implementation will

be tested, indicate the estimated size of the demonstration/test database)

The modules of the solution will be tasted based on features of it. As mentioned in

section E, the primary modules and units will be tested separately. And after integrating

all modules the final integration test and other test will be perform to entire artefact.

Input identification module

To test this module it will use three regions of a individuals

This will be checked that weather it is capable of identifying input region separately in

other word will it identify what region user has input to the system

Successful Criteria

Identifying eye region

Identifying nose region

Identifying mouth region

differentiate non regions and facial regions

pass type of region to Face region detection module

The way of evaluation implementation and functionality

Since this use incremental prototyping it will be test all the criteria at the end of module

implementation. Mainly black box testing will be perform but if it required to check the

internal functionalities of the module white box testing will be perform.

Test data

To test this module it will be use generated face regions from face database. It will be

check 10 test cases per each 3 regions.

Face region detection module

In this it will detect the face region in frontal full face image in database based on given

data from Input identification module and prepare the selected region for match.

The capability of doing that will be checked

Successful Criteria

Detect given face region in frontal full faces in database

Extract that face regions from the faces in database

Create temporary list to be match

Pass created list to face match module

The way of evaluation implementation and functionality

Since this use incremental prototyping it will be test all the criteria at the end of module

implementation. Mainly black box testing will be perform but if it required to check the

internal functionalities of the module white box testing will be perform.

Test data

To test this module it will be use 10 frontal faces for detect each region which mean

there will be 30 testes for all three regions

Face match module

This will match submitted face region against all the face’s regions in the database.

The accuracy of the matches will be checked. And also frailer rate also will be taken

into the consideration.

Successful Criteria

Recognize particular individual’s face using eye region

Recognize particular individual’s face using nose region

Recognize particular individual’s face using mouth region

The way of evaluation implementation and functionality

Since this use incremental prototyping it will be test all the criteria at the end of module

implementation. Mainly black box testing will be perform but if it required to check the

internal functionalities of the module white box testing will be perform.

Test data

To test these modules it will be use 100 frontal faces for detect each region which mean

there will be 300 testes for all three regions.