face recognition face identification

PROPOSED WORK ON

FACE RECOGNITION

Presented By

Kalyan Acharjya

A Presentation on Initial stage of M.Tech Dissertation Work

Department of…………………………University of………..

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Kalyan Acharjya

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CONTENTS

Introduction to Digital Image Processing.

Face Recognition.

Why Face Recognition.

How Face Recognition.

Literature Survey [Going On].

Problem Statement.

Challenges for Real Time Applications.

Standard Face Images Database.

Conclusions and Future Work.

References.

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Kalyan Acharjya, India

INTRODUCTION : DIGITAL IMAGE PROCESSING

An Image has infinite intensity value.

Also infinite picture point -How its stored?

Digitization of image.

Spatial discretization by Sampling.

Intensity discretization by Quantization.

An stored image is process in various means

(Techniques) for enhance or extracts some

features from it, is well considerable as

DIGITAL IMAGE PROCESSING. 4


FACE RECOGNITION

How computer or systems is identify any person by comparison its

FACE with its previous stored database. Its also a part of

COMPUTER VISION.

FACE RECOGNITION is the part of Digital Image Measurement.

Its High Level Processing involved making sense of an ensemble of

recognize FACE with analysis of unknown FACE.

Its almost similar to Human being, who identify any person, if

he/she have already met. Although it(Human) fails sometimes (rare

case).5


WHY FACE RECOGNITION ?

The world is urged for more and accurate face recognition rate.

How COMPUTER VISITON is possible, as human being are?

Automatic person identification.

FACE RECOGNISITION have lots of real world applications.

Automatic Attendance System.

Security Purposes.

Computer Interaction etc.

Crowd Surveillance. [US (MIT) invested $ 100 million for perfect recognition

system-Times of India, Oct 2013 ].

In 2011, London riots many suspects of partial face images were not

recognized by COTS FR system[15].6


Comparison

HOW FACE RECOGNITION?

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Input Image

Face Detection*

Crop Face Image

Features Extraction

Identification

Face Image

Database

*The targeted work will not include Face Detection Part.*The input images will crop face images from standard Face Database.

Who is She?


LITERATURE SURVEY [1]

Title : Li, Liao and Jain, “Partial Face Recognition,Alignment free Approach”, IEEE, May, 2013.

Technique Used: Authors proposed an alignment freeface recognition method based on multi-key pointdescriptors. (MKD).

Conclusion: Authors concluded that MKD method issuperior than leading commercial FR systems likePitpatt and faceVACS SDK.

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LITERATURE SURVEY CONTD..[2]

Title : Mersico, Nappi and Wechsler, “Robust FaceRecognition fro Uncontrolled Pose and Illumination ”,IEEE, January, 2013.

Technique Used: Authors proposed a novel frame workbased on normalization strategies and Face Analysisfor Commercial Entities (FACE).

Conclusion: The result showed the significant increasein recognition rate [95% in FERET fa Database] inaccuracy, whether comparison with other availablealgorithms.


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LITERATURE SURVEY CONTD.. [3]

Title : Park and Savvides , “Individual Kernal TensorSubspaces for Robust Face Recognition: A ComputationallyEfficient Tensor Framework without requiring ModeFactorization ”, IEEE, Oct, 2007.

Technique Used: The work based on high order tensor toconstruct a multi linear structure and model the multiplefactors of face variations.

Conclusion: The paper introduced the new concept thatappearance factor, the factor of person’s identity modeledby a tensor structure can be used for better facerecognition system specially for difference types ofappearance of same faces.


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LITERATURE SURVEY CONTD.. [4]

Title: Karim, Lipu, Rahman and Sultana , “Face Recognitionusing PCA based Method”, IEEE, 2010.

Technique Used: The work based on Principle ComponentAnalysis (PCA).

Conclusion: The paper concluded the Principle ComponentAnalysis is better then their predecessor, where recognitionrate 84.1 % (Male Face) and 95.45 % (Female Face) in caseof Indian face database.

Also recognition rate 92.5 % (Male Face) and 85 % (FemaleFace) in case of University of Essex, UK face database.


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LITERATURE SURVEY CONTD..[5] MANY MORE…..!

Title : Meade, Kumar and Phillips, “Comparativeperformance of Principle Component Analysis , GaborWavelets and Discrete Gabor Wavelets”, CanadianJournal of Electronics and Computer Engg., Spring,2005.

Technique Used: Comparative performance analysis ofPCA with Gabor Wavelets and Discrete GaborWavelets.

Conclusion: Gabor Wavelets showed the bestperformance on FERET database, as Gabor Wavelets isleast affected by illumination levels.


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PROBLEM STATEMENT

Maximum works were proposed the Face Recognition system in

particular case (Either Pose or Illumination or specific Face

Database).

Till date there is not single face recognition system for fulfilling

the all (or Maximum factors) in real time application.

Every method have its pros and cons.

The presenter motivated by the unsatisfactory scenario of Face

Recognition system to enhance the performance of it.


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AVAILABLE FACE RECOGNITION ALGORITHMS

BASED ON [4]

Principle Component Analysis (PCA).

Normalization of Histogram Analysis (NHA).

Independent Component Analysis (ICA).

Normalized Cross Correlation (NCC).

Sum of Absolute Difference (SAD).

Linear Discernment Analysis (LDA).

Discrete Wavelets Transform (DWT).

Gabor Wavelet Transform(GWT).

Multilayer Appearance-Tensor based (MAT).

Multiple Descriptor Key point (MDK-SDK).(Partial Face Also) etc.


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CHALLENGE FOR FACE RECOGNITION [1]

External OcclusionBy other face

Self OcclusionBy non frontal pose

Facial AccessoriesBy Sunglass

Not Proper Illumination

Sensor saturationBy under exposure orover exposure

Limited Field of View (FOV) By out of camera.


STANDARD FACE RECOGNITION DATABASE [4]

The Choice of appropriate database to be used based

on targeted work.

Color FERET Database

Yale Face Database.

PIE Database.

FIA Video Database.

CBCL Face Recognition Database.

Expression Image Database.

Mugs hot Identification Database.

Identification Database.

Indian Face Database.

Face Recognition Data, University of Essex, UK 16


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CONCLUSIONS AND FUTURE WORK

The presenter in initial phase of project work, so exact algorithms

yet to be finalized.

The presenter aware to follow any based paper should have same

face database.

Modify the any previous mentioned face recognition algorithms to

enhance the recognition rate of identification.

The proposed work may also target for fusion between two

algorithms.

When available algorithms will modified or develop, the result will

be compare with based paper or predecessor method result .


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REFERENCES

[1] Li, Liao and Jain, “Partial Face Recognition, Alignment free Approach”, IEEE

Transaction on Pattern Analysis and Machine Intelligence, VOL 35, No 5, May,

2013.

[2] Mersico, Nappi and Wechsler, “Robust Face Recognition fro Uncontrolled Pose

and Illumination ”, IEEE Transaction on Systems, man and Cybernetics:

systems, VOL. 43, NO. 1, January, 2013.

[3] Park and Savvides , “Individual Kernal Tensor Subspaces for Robust Face

Recognition: A Computationally Efficient Tensor Framework without requiring

Mode Factorization ”, IEEE on Systems, man and Cybernetics: systems, VOL. 37,

NO. 5 , Oct, 2007.

[4] Karim, Lipu, Rahman and Sultana ,“Face Recognition using PCA based

Method”, IEEE, 2010.

[5] Meade, Kumar and Phillips, “Comparative performance of Principle

Component Analysis , Gabor Wavelets and Discrete Gabor Wavelets”, Canadian

Journal of Electronics and Computer Engg., VOL. 30, NO. 2, Spring, 2005.


REFERENCES CONTD..

[6]Kar, Debbarma, Saha and Pal, "Study of Implementing AutomatedAttendance System using Face Recognition Technique” International Journal ofComputer and Communication Engineering, VOL. 1, No. 2, July 2012.

[7] Balcoh, Yousaf, Waqar and Baig,”Algorithm for Efficient AttendanceManagement: Face Recognition based Approach”, IJCSI (Online), Vol.9, No.1,July 2012.

[8] Jiang, Sadka and Crooks, "Technical Correspondence-Multimodal BiometricHuman Recognition for Perceptual Human-Computer Interaction ” IEEETransaction on Systems, man and Cybernetics-Part C:Applications and Review,VOL. 40, NO. 6, November, 2010.

[9]Jyoti, Chadha, Vaidya and Roja,”A robust, low-cost approach to FaceDetection and Face Recognition”, CiiT International Journal of Digital ImageProcessing, ISSN 0974-9586(Online), Vol. 15, No 10, October 2011.

[10] Lu and Tan,”Cost-Sensitive Subspace Analysis and Extensions For FaceRecognition”,IEE transactions on Information forensics and Security, Vol. 8, No3, March 2013.

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REFERENCES CONTD..

[11] Toole, Philips, Jiang and Abdi,”Face Recognition Algorithms SurpassHuman Matching Faces over changes in Illumination”, IEEETransactions on Pattern Analysis and Machine Intelligence, VOL. 29, No.9, September 2007.

[12]Zhang, Shan, Chen and Gao,”Local Gabor Binary Patterns Based onKullback-Leibler Divergence for Partially Occluded Face Recognition”,IEEE Signal Processing Letters, Vol. 14, No.11, November 2007.

[13] Liu and Liu, “A hybrid Color and Frequency Features Method forFace Recognition”,IEEE transactions on Image Processing, Vol. 17, No.10. October 2008.

[14] Mohanty, Sarkar, Kasturi and Phillips, "Subspace Approximation ofFace Recognition Algorithms: An Empirical Study”, IEEE Transactionson Information Forensics and Security”, Vol. 3, No. 4, December 2008.

[15] “Police use Facial recognition Technology to Nab Rioters”,http://www.msnbc.msn.com/id/44110353/ns

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