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REAL TIME TRACKING OF FACIAL

FEATURESbyABHISHEK KUMAR(REG. NO. 200912004)

ASEEM SINHA - (REG. NO. 200912018)

PRAVEEN KUMAR - (REG. NO. 200912071)

Under the Guidance of-

MRS. AMRITA BISWASAssociate Professor

EC Department

SMIT

Department of Electronics and Communication

Sikkim Manipal Institute of Technology

Majitar,Rangpo,East Sikkim-737136

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CONTENTS

Objective

Motivation

Difficulties

Introduction

Implementation

Results

Applications

Limitations Conclusion

Future scope

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OBJECTIVE

Detection of multiple faces from a still image.

Creating a mask in order to fit on the face.

Detection and features tracking of single facefrom video.

Detection and features tracking of multiple

faces from video.

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MOTIVATION

The wide range of commercial and law

enforcement applications.

The availability of feasible technologies for

research and projects.

Current systems are still far away from the

capability to cope up with real time

applications.

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DIFFICULTIES

Face Variation

Face position

Wearing glasses Low resolution images

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INTRODUCTION

Face detection

It is the task of localizing faces in an input

image, is a fundamental part of any faceprocessing system. The extracted faces can

then be used for initializing face tracking or

automatic face recognition.

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Face feature tracking

It is the process of locating a moving face or

several of them over a period of time and thenlocating facial features like eyes, nose and

mouth.

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IMPLEMENTATION

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CONTD

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RESULTS

Actual image Processed image

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RESULTS CONTD

Actual video frame Processed video frame

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RESULTS CONTD

Actual video frame Processed video frame

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APPLICATIONS

Access control

Identification systems

Biometrics Video surveillance

Human computer interface

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LIMITATIONS

Due to bad lighting condition, tracking is

sometimes not possible.

Due to partially covered faces, tracking is not

possible.

Fully rotated faces are not possible to track.

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CONCLUSION

We proposed a modified method for

automatic fitting of a deformable face mask to

a previous unseen face.

By making mask we are able to fit this mask to

different input images and video.

The face model is built with the high and low

image resolution.

Successful implementation on multiple faces.

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FUTURE SCOPE

Work can be done to link the project with

database for security purposes.

Further processing can be done for rotated

faces.

Further processing can be done for blurred

images and videos.

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REFERENCES

P. Viola and M. Jones, Rapid Object Detection using a

Boosted Cascade of Simple Features, vol. 2, pp. 240-249,

2003.

J.G. Wang and E . Sung, Frontal view detection and facial

features extraction using color and morphological operations,

Pattern recognition letters, vol. 20, pp. 1053-1068, oct . 1999.

W. Zhao, R. Chellappa, P. J. Phillips, and A. Rosenfeld, Face

recognition: A literature survey, ACM Computing Surveys,

vol. 35, no. 4, pp. 399458, 2003.

Digital Image Processing Using MATLAB by Gonzalez,

Woods & Eddins, Prentice, pp.25-29.

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THANK YOU!