Face Recognition

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Face Recognition

  1. 1. Face Recognition Technology<br />W.A.L.S.Wijesinghe<br />
  2. 2. Introduction<br />Biometrics<br />A biometric is a unique, measurable characteristic of a human<br />being that can be used to automatically recognize an individual<br />or verify an individual’s identity<br />Finger- Scan<br />Iris Scan<br />Retina Scan<br />Hand Scan<br />Facial Recognition<br />80 landmarks on a human face.<br /><ul><li>Distance between eyes
  3. 3. Width of the nose
  4. 4. Depth of the eye socket
  5. 5. Cheekbones
  6. 6. Jaw lines
  7. 7. Chin</li></li></ul><li>Why we choose face recognition over other biometric?<br />It requires no physical interaction on behalf of the user.<br />It does not require an expert to interpret the comparison result. <br /> Identify a particular person from large crowd<br />Verification of credit card, personal ID, passport<br />
  8. 8. History of Face Recognition<br />1. In 1960 , scientist (Bledsoe, Helen,Charles) began work on using the computer <br /> to recognize human faces.<br /> 2. Before the middle 90’s- single-face segmentation. <br />3. EBL-Example-based learning approach by Sung and Poggio (1994).<br />4. The neural network approach by Rowley etal. (1998).<br />5.FRVT-Face Recognition Vendor Test-(2002)<br />6. FRGC-Face Recognition Grand Challenges-(2006)<br />7. Polar Rose Technology-Text surrounding photo-(2007)<br /> 3D image<br />
  9. 9. Face recognition: Procedure<br />Input face image(Capture)<br />Face feature <br />extraction<br />Feature Matching<br />Decision maker<br />Face<br />database<br />Output result<br />
  10. 10. 2.0 Face Feature Extraction Methods<br />1. Eigen face or PCA (Principal Component Analysis)<br />Other method;<br />1. EBGM -Elastic Bunch Graph Method.-2D Image<br />2. 3D Face Recognition Method 3D Image<br />
  11. 11. PCA-Principal Component Analysis(Eigen Face Method)<br />1.Create training set of faces and calculate the eigen faces<br /> ( Creating the Data Base)<br />2. Project the new image onto the eigen faces.<br />3. Check closeness to one of the known faces.<br />4. Add unknown faces to the training set and re-calculate<br />
  12. 12. 1.0 Creating training set of images<br />Face Image as I(x,y) be 2 dimensional N by N array of<br /> (8 bit) intensity values.<br />Image may also be considered as a vector of dimension N2.<br /> ( 256x256 image = Vector of Dimension 65,536 )<br /> y Image T1=I1(1,1),I1(1,2)…I1(1,N),I1(2,1)…….., I1(N,N)<br /> x<br />
  13. 13. Training set of face images T1,T2,T3,……TM.-<br /> 1. Average Face of Image =Ψ = 1 ( ∑M Ti ) ; M –no. of images<br /> M i=1<br />Ψ average face <br />
  14. 14. 2. Each Training face defer from average by vector Φ<br />ΦiEigen face<br /> Each Image Average Image<br /> Ti Ψ<br />Φi =Ti - Ψ<br />
  15. 15. Uk Eigen vector ,λk Eigen value of Covariance Matrix C <br />Where A is,<br />λk Eigen value<br /> C= λkUk<br />
  16. 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- <br />Face<br />database<br />
  17. 17. Using Eigen faces Identify the New face image<br /> date base –eigen vectors U <br />ωk = UkTΦ<br />New Image(T) Its Eigen face (Φ) U1<br /> U2<br />X . k Class<br /> .<br />Uk<br />Φ = T – Ψ<br />Ω = ∑k=1m ωk= <br /> minimum ||Ω - Ωk ||<br />
  18. 18. Mathematical equations-Identify new face image.<br /> 1. New face image T transform into it’s eigen face component by<br />Φ = T – Ψ<br /> 2. Find the Patten vector of new image Ω<br />ωk = UkTΦ ; where Ukeigen vectors <br />Ω = ∑k=1m ωk<br /> To determine the which face class provide the best input face image is to find the face class k by <br /> minimum ||Ω - Ωk ||<br /> Face Image Detected in k Face Class.<br />
  19. 19. Usage & Recent Development<br />1.Immigration-US-VISIT- United State Visitor & immigration status Indicator<br />2. Banks-ATM &check cashing security .<br />3.Airport –Detected for registered traveler to verify the traveler.<br />4. Classification of face by Gender, Age,<br />attributes.<br />
  20. 20. Access Control Products<br />Access Control into Bank<br />New Face Reader with LCD<br />Kiosk Lyon Airport, France<br />Face Reader with mirror -ATM <br />
  21. 21. Future of Face Recognition<br />Billboard with face recognition –Advertising<br />Face base Retailing-(Shopping) <br /> retail stores, restaurants, movie theaters, car rental companies, hotels. (You Can pay the bills using your face)<br />Recognition Twins<br /> More High Speed accessing of Database<br />
  22. 22. Thank You.<br />

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