Introduction Biometrics A biometric is a unique, measurable characteristic of a human being that can be used to automatically recognize an individual or verify an individual’s identity Finger- Scan Iris Scan Retina Scan Hand Scan Facial Recognition 80 landmarks on a human face.
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 Face database Output result
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
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 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
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 ΦiEigen 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= λkUk
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
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 ||
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.
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.
Access Control Products Access Control into Bank New Face Reader with LCD Kiosk Lyon Airport, France Face Reader with mirror -ATM
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