KASHYAP JUTHANI EM2006028AKASH KAPADIA EM2006029NIKUNJ KOTHARI EM2006031VISHAL GALA EM2007062

BIOMETRICS• Biometric characteristics

• Physiological – fingerprint, face recognition, iris recognition, hand and palm geometry

• Behavioral – typing rhythm, gait , and voice

• Biometric functions

• Verification

• Identification

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FACE RECOGNITION

• Procedure

1. Enrollment

2. Maintenance of database

3. Recognition

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TECHNIQUES• PCA

• Principal component analysis (PCA) involves a mathematical procedure that transforms a number of possibly correlated variables into a smaller number of uncorrelated variables called principal components

• The main idea of the principal component analysis is to find the vectors which best describe the distribution of face images within the entire image space.

• Face space is comprised of eigenfaces, which are the eigenvectors of the set of the face

• The first principal component accounts for as much of the variability in the data as possible, and each succeeding component accounts for as much of the remaining variability as possible

• PCA aims to extract a subspace where the variance is maximized

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TECHNIQUES• LDA

• LDA is a method used to find the linear combination of features which best separate two or more classes of objects or events

• LDA is also called Fisher Discriminant Analysis• In computerized face recognition, each face is

represented by a large number of pixel values. LDA is primarily used here to reduce the number of features to a more manageable number before classification. Each of the new dimensions is a linear combination of pixel values, which form a template.

• The linear combinations obtained using Fisher's linear discriminant are called Fisher faces, while those obtained using the related principal component analysis are called eigenfaces.

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Independent Component Analysis

“Independent component analysis (ICA) is a method for finding underlying factors or components from multivariate (multi-dimensional) statistical data. What distinguishes ICA from other methods is that it looks for components that are both statistically independent, and non Gaussian.”

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The simple “Cocktail Party” Problem

Sources

Observations

s1

s2

x1

x2

Mixing matrix A

x = As

n sources, m=n observations

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0 50 100 150 200 250

-0.2

-0.1

0.0

0.1

0.2

V1

ICA

Observing signals Original source signal

0 50 100 150 200 250

-0.10

-0.05

0.00

0.05

0.10V

4

Classical ICA (fast ICA) estimation

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Observed Random Variables

Two Independent Sources Mixture at two Mics

aIJ ... Depend on the distances of the microphones from the speakers

2221212

2121111

)(

)(

sasatx

sasatx

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Independent outputs

Get the Independent Signals out of the MixtureApril 8, 2023 10

ICA Technique• Given a set of observations of random variables

x1(t), x2(t)…xn(t), where t is the time or sample index, assume that they are generated as a linear mixture of independent components:

• Mathematically,

• X=As, where A is some unknown matrix. Independent component analysis now consists of estimating both the matrix A and the si(t), when we only observe the xi(t).”

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ICA model for Face Recognition• Use statistical “latent variables“ system• Random variable sk instead of time signal• xj = aj1s1 + aj2s2 + .. + ajnsn, for all j• x = As• IC‘s s are latent variables & are unknown AND

Mixing matrix A is also unknown• Task: estimate A and s using only the

observeable random vector x• Lets assume :-

• no. of IC‘s = no of observable mixtures and• A is square and invertible

• So after estimating A, we can compute W=A-1 and hence we get the independent components.

• s = Wx = A-1x

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The process of face recognition using ICA

• Capturing image or using a pre saved image as input.

• Preprocessing :• by Dimension

Reduction Using PCA

• Data centering

• Whitening

Examples of EigenFaces (Principle Components)

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• Estimating Independent components by using FastICA Algorithm.

• Decorrelation of the outputs

• estimate the independent components one by one.

• run the one-unit fixed-point algorithm for wp+1.

• after every iteration step

subtract from wp+1 the

projections wT p+1 wj wj.

ICA Process …Contd.

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Database image Image to be recognized

Accurately

Recognized

Output

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APPLICATIONS OF FACE RECOGNITION

• BIOMETRICS – driver’s licenses, entitlement programs, immigration, national ID, passports, voter registration

• INFORMATION SECURITY – application security, desktop logon (windows NT, windows 95), database security, file encryption, intranet security, internet access, medical records, official company records, national records

• LAW ENFORCEMENT AND SURVEILLANCE – advanced video surveillance, CCTV control portal, post-event analysis

• SMART CARDS – stored value security, user authentication

• ACCESS CONTROL – facility access, vehicular access

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DRAWBACKS OF FACE RECOGNITION TECHNOLOGY

Manufacturers that make use of face recognition technology :-

• ASUS• TOSHIBA• LENOVO

Face recognition drawbacks:- • Influences of changes in lighting• Influences of image capturing devices• Influences of image processing

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FUTURE PROSPECTS• Elimination of plastic and paper

money

• High level of security

• Authenticity of attendance at colleges and work places

• Registrations and admission processes

• Pass keys for personal accounts

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Thank You…

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