Face recognition technology uses biometrics to automatically recognize individuals or verify their identity based on unique measurable characteristics of the human face. It analyzes 80 landmarks on the face such as distance between eyes, width of nose, cheekbones, and jawline. Face recognition is commonly used for identification from large crowds, verification for credit cards and passports, and does not require physical contact or specialized interpretation of results. Common methods of face recognition include eigenface analysis using principal component analysis to extract features from faces and match new images to those in a database. Recent applications include uses for immigration, security, and targeted advertising based on facial analysis.

1. Face Recognition TechnologyW.A.L.S.Wijesinghe

2. IntroductionBiometricsA biometric is a unique, measurable characteristic of a humanbeing that can be used to automatically recognize an individualor verify an individual’s identityFinger- ScanIris ScanRetina ScanHand ScanFacial Recognition80 landmarks on a human face.Distance between eyes

3. Width of the nose

4. Depth of the eye socket

5. Cheekbones

6. Jaw lines

7. ChinWhy 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 crowdVerification of credit card, personal ID, passport

8. History of Face Recognition1. 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

9. Face recognition: ProcedureInput face image(Capture)Face feature extractionFeature MatchingDecision makerFacedatabaseOutput result

10. 2.0 Face Feature Extraction Methods1. Eigen face or PCA (Principal Component Analysis)Other method;1. EBGM -Elastic Bunch Graph Method.-2D Image2. 3D Face Recognition Method 3D Image

11. 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

12. 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

13. Training set of face images T1,T2,T3,……TM.- 1. Average Face of Image =Ψ = 1 ( ∑M Ti ) ; M –no. of images M i=1Ψ average face

14. 2. Each Training face defer from average by vector ΦΦiEigen face Each Image Average Image Ti ΨΦi =Ti - Ψ

15. Uk Eigen vector ,λk Eigen value of Covariance Matrix C Where A is,λk Eigen value C= λkUk

16. Face Images using as Eigen Faces (Uk) training images (Ti) U=( U11,…U1n, U21,…U2n,….., Uk1,……Ukn, Um1,……Umn) -Image must be in same size- Facedatabase

17. Using Eigen faces Identify the New face image date base –eigen vectors U ωk = UkTΦNew Image(T) Its Eigen face (Φ) U1 U2X . k Class .UkΦ = T – ΨΩ = ∑k=1m ωk= minimum ||Ω - Ωk ||

18. Mathematical equations-Identify new face image. 1. New face image T transform into it’s eigen face component byΦ = T – Ψ 2. Find the Patten vector of new image Ωωk = UkTΦ ; where Ukeigen 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.