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Face recognition

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Overall view and PCA Algorithm

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Face recognition

  1. 1. Mehrdad Naserdoust Azarbaijan Shahid Madani University of Iran Face
  2. 2. 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. Two Types 1. physiological 2. behavioral characteristics
  3. 3. physiological 1. 2. 3. 4. 5. Finger- Scan Iris Scan Retina Scan Hand Scan Facial Recognition
  4. 4. Facial Recognition • 80 landmarks on a human face. o Distance between eyes o Width of the nose o Depth of the eye socket o Cheekbones o Jaw lines o Chin
  5. 5. First-order features values
  6. 6. Second-order features values
  7. 7. In Facial recognition there are two types of comparisons  VERIFICATION- The system compares the given individual with who they say they are and gives a yes or no decision.  IDENTIFICATION- The system compares the given individual to all the Other individuals in the database and gives a ranked list of matches.
  8. 8. Why we choose face recognition over other biometric?
  9. 9. It requires no physical interaction on behalf of the user
  10. 10. It does not require an expert to interpret the comparison result
  11. 11. Identify a particular person from large crowd
  12. 12. Verification of credit card, personal ID, passport
  13. 13. HOW FACE RECOGNITION SYSTEMS WORKS Face Recognition runs in 3 steps: 1. The digital photo (or scanned photo print) that you provide, is loaded. 2. face detection technology is applied to automatically detect human faces in your photo. 3. Face recognition technology is applied to recognize the faces detected in the previous step. Recognizing faces is done by algorithms that compare the faces in your photo.
  14. 14. Model Based EBGM -Elastic Bunch Graph Method
  15. 15. Model Based 3D Face Recognition Method
  16. 16. PCA-Principal Component Analysis(Eigen Face Method)
  17. 17. 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
  18. 18. 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 2. N ( 256x256 image = Vector of Dimension 65,536 ) y I1(N,N) Image T1=I1(1,1),I1(1,2)…I1(1,N),I1(2,1)……..,
  19. 19. • Training set of face images T1,T2,T3,……TM.- • 1. Average Face of Image =Ψ = 1 ( ∑M Ti ) M i=1 Ψ average face ; M –no. of images
  20. 20. • 2. Each Training face defer from average by vector Φ Φi =Ti - Ψ Φi Eigen face Each Image Ti Average Image Ψ
  21. 21. Uk Eigen vector ,λk Eigen value of Covariance Matrix C Where A is, λk Eigen value C= λk Uk
  22. 22. Face Images using as training images (Ti) -Image must be in same size- Eigen Faces (Uk) U=( U11,…U1n, U21,…U2n,….., Uk1,……Ukn, Um1,……Umn) Face database
  23. 23. Using Eigen faces Identify the New face image 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 ||
  24. 24. 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 ω k = UkT Φ ; where Uk eigen vectors Ω = ∑k=1m ω k To determine the which face class provide the best input face image is to find the face class k by minimum ||Ω – ω k || Face Image Detected in k Face Class.
  25. 25. thanks for patience !
  26. 26. Any Doubts ……

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