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Face Recognition
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Face Recognition



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  • 1. Face Recognition Technology
  • 2. Introduction
    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.
    • Distance between eyes
    • 3. Width of the nose
    • 4. Depth of the eye socket
    • 5. Cheekbones
    • 6. Jaw lines
    • 7. Chin
  • 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
  • 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
    Feature Matching
    Decision maker
    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)
  • 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-
  • 17. Using Eigen faces Identify the New face image
    date base –eigen vectors U
    ωk = UkTΦ
    New Image(T) Its Eigen face (Φ) U1
    X . k Class
    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,
  • 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.