Face Recognition Technology

W.A.L.S.Wijesinghe

IntroductionBiometricsA biometric is a unique, measurable characteristic of a humanbeing that can be used to automatically recognize an individualor verify an individual’s identity

1. Finger- Scan2. Iris Scan3. Retina Scan4. Hand Scan5. Facial Recognition

80 landmarks on a human face.

o Distance between eyeso Width of the noseo Depth of the eye socketo Cheekboneso Jaw lineso Chin

Why we choose face recognition over other biometric?

It requires no physical interaction on behalf of the user.

It does not require an expert to interpret the comparison result.

Identify a particular person from large crowd

Verification of credit card, personal ID, passport

History of Face Recognition 1. In 1960 , scientist (Bledsoe, Helen,Charles) began work on using the computer to recognize human faces. 2. Before the middle 90’s- single-face segmentation.

3. EBL-Example-based learning approach by Sung and Poggio (1994).

4. The neural network approach by Rowley etal. (1998).

5.FRVT-Face Recognition Vendor Test-(2002)6. FRGC-Face Recognition Grand Challenges-(2006)7. Polar Rose Technology-Text surrounding photo-(2007)

3D image

Face recognition: Procedure

Input face image(Capture)

Face feature extraction

Feature Matching Decision maker

Output result

Facedatabase

1. Eigen face or PCA (Principal Component Analysis)

Other method;1. EBGM -Elastic Bunch Graph Method.-2D Image

2. 3D Face Recognition Method 3D Image

2.0 Face Feature Extraction Methods

PCA-Principal Component Analysis(Eigen Face Method)

1.Create training set of faces and calculate the eigen faces ( Creating the Data Base)

2. Project the new image onto the eigen faces.

3. Check closeness to one of the known faces.

4. Add unknown faces to the training set and re-calculate

1.0 Creating training set of imagesFace Image as I(x,y) be 2 dimensional N by N array of (8 bit) intensity values.Image may also be considered as a vector of dimension N2.

( 256x256 image = Vector of Dimension 65,536 ) y Image T1=I1(1,1),I1(1,2)…I1(1,N),I1(2,1)……..,

I1(N,N)

x

Training set of face images T1,T2,T3,……TM.-

1. Average Face of Image =Ψ = 1 ( ∑M Ti ) ; M –no. of images M i=1

Ψ average face

2. Each Training face defer from average by vector Φ Φi Eigen face

Each Image Average Image

Ti Ψ

Φi =Ti - Ψ

Uk Eigen vector ,λk Eigen value of Covariance Matrix C

Where A is,

λk Eigen value

C= λk Uk

Face Images using as Eigen Faces (Uk) training images (Ti) U=( U1

1,…U1n, U2

1,…U2n,….., Uk

1,……Ukn, Um

1,……Umn)

-Image must be in same size-

Facedatabase

date base –eigen vectors U

ωk = UkT

Φ

New Image(T) Its Eigen face (Φ) U1

U2

X . k Class . Uk

Φ = T – Ψ

Ω = ∑k=1m ωk=

minimum ||Ω - Ωk ||

Using Eigen faces Identify the New face image

Mathematical equations-Identify new face image.

1. New face image T transform into it’s eigen face component by Φ = T – Ψ 2. Find the Patten vector of new image Ω ωk = Uk

T Φ ; where Uk eigen vectors

Ω = ∑k=1m ωk

To determine the which face class provide the best input face

image is to find the face class k by minimum ||Ω - Ωk ||

Face Image Detected in k Face Class.

Usage & Recent Development1.Immigration-US-VISIT- United State Visitor & immigration

status Indicator

2. Banks-ATM &check cashing security .3.Airport –Detected for registered traveler to verify the

traveler.

4. Classification of face by Gender, Age, attributes.

Access Control Products

Access Control into Bank

Kiosk Lyon Airport, France

New Face Reader with LCD Face Reader with

mirror -ATM

Future of Face RecognitionBillboard with face recognition –Advertising

Face base Retailing-(Shopping) retail stores, restaurants, movie theaters, car rental

companies, hotels. (You Can pay the bills using your face)

Recognition Twins

More High Speed accessing of Database

Thank You.