3 d face recognition

8/13/2019 3 D face recognition

1/11

Three-Dimensional Face Recognition

Proposed by

Anubhav Shrivastava

Roll number:1005210009

Final Year

Supervised by

Dr. Y N Singh

Associate Professor,

Computer Science and Engineering,

Institute of Engineering And Technology, Lucknow

Institute of Engineering and Technology, Lucknow,

[an autonomous constituent college of UPTU]

DEPARTMENT OF COMPUTER SCIENCE AND ENGINEERING


2/11

2

Introduction

Face recognition offers several advantages over other biometrics

Covert operation.

Human readable media.

Public acceptance.

Data required is readily available police databases etc.

Growing interest in biometric authentication

National ID cards, Airport security (MRPs), Surveillance.

Fingerprint, iris, hand geometry, gait, voice, vein and face.


3/11

Problem Statement

Given a two dimensional image of a scene, identify or verify one or more

persons in the scene using a stored database of three dimensional faces.

Broadly speaking there shall be 5 steps in face recognition system:

sensing segmentationFeature

extractionClassification

Postprocessing

input

All the phases of the phase recognition system will have a different

algorithms.

The idea is develop these modules separately and the later integrate them.


4/11

4

Limitations of 2D Face RecognitionSystem effectiveness is highly dependant on image capture conditions.

Face recognition is not as accurate as other biometrics.

Error rates that are too high for many applications in mind.

Image

taken

withmobile

phone;distorted

face

Notlooking

straightinto the

camera

Improperflash or

improperlightening

Shadowon face

Too much

glare on

spectacles

Poor

resolution


5/11

5

A Possible Solution

3D Face Recognition

Use of geometric depth information rather than colour and texture

Invariant to lighting conditions

Ability to rotate face model in 3D space

Invariant to head angle

3D models captured to scale

Absolute measurements invariant to camera distance


6/11

3D Face DataStored in OBJ file format.

Approximately 8000 points on a facial surface.

Greyscale texture mapped.

Wire-mesh TexturePolygons Lighting


7/11

7

Test Databasepublicly available 3D Face data of large range of expression,orientation, gender, ethnicity, age.


8/11

8

Test Procedure


9/11

Programing Specifications Rapid application development model of software development life cycle will

be used.

The software will be targeted to run on windows operation system

Language: C#

Frame work: Microsoft .NET

Integrated development Environment: Microsoft visual studio

C# supports rich library in image processing and mathematical work

Module 1

Module 2 Module 4

Module 3


10/11

Phase Recognition System Development Timeline The idea is to develop a software with the trivial algorithms available and

then later enhance the efficiency of algorithms.

The targeted timeline of the development of the project is :

Learning ofprogramming in C#and .NET framework

By 31stDecember

Feature Extraction

Algorithm By 15thJanuary

Featureclassificationalgorithm

By 31

st

January

SegmentationAlgorithm

By 15thFebruary

Sensing algorithm

By 28thmarch

Post processingalgorithm

By 15stmarch

Integration ofmodules

30thApril

Documentation

May


11/11

Readings and References

Christopher M Bishop, "Pattern Recognition and machine learning", Springer

university Press, chapter 12 [Principal component analysis], chapter

13[Hidden Markov Model]

M. Narasimha Murty and V. Susheela Devi, Pattern Recognition, An

Algorithmic Approach, Springer University Press.

W.S. Yambor, Analysis of PCA-based and Fisher Discriminant-Based Image

Recognition Algorithms, M.S. Thesis, Technical Report CS-00-103, ComputerScience Department, Colorado State University, July 2000

L. Sirovich and M. Kirby, Low-dimensional procedure for the characterizationof human faces, Division of Applied Mathematics, Brown University,

Providence, Rhode Island 02912

3 d face recognition

Documents