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 Technology W.A.L.S.Wijesinghe

3. Width of the nose

4. Depth of the eye socket

5. Cheekbones

6. Jaw lines

8. 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

9. Face recognition: Procedure Input face image(Capture) Face feature extraction Feature Matching Decision maker Face database Output result

10. 2.0 Face Feature Extraction Methods 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

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 images Face 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=1 Ψ average face

14. 2. Each Training face defer from average by vector Φ ΦiEigen face Each Image Average Image Ti Ψ Φi =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- Face database

17. Using Eigen faces Identify the New face image date base –eigen vectors U ωk = UkTΦ New Image(T) Its Eigen face (Φ) U1 U2 X . k Class . Uk Φ = T – Ψ Ω = ∑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 by Φ = T – Ψ 2. Find the Patten vector of new image Ω ωk = 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.

19. Usage & Recent Development 1.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.

20. Access Control Products Access Control into Bank New Face Reader with LCD Kiosk Lyon Airport, France Face Reader with mirror -ATM

21. Future of Face Recognition Billboard 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

22. Thank You.