Overall view and PCA Algorithm

1. Mehrdad Naserdoust
Azarbaijan Shahid
Madani University of
Iran
Face

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. physiological
1.
2.
3.
4.
5.
Finger- Scan
Iris Scan
Retina Scan
Hand Scan
Facial Recognition

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. First-order features values

6. Second-order features values

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. Why we choose face recognition over other biometric?

9. It requires no physical interaction
on behalf of the user

10. It does not require an
expert to interpret
the comparison result

11. Identify a particular
person from large
crowd

12. Verification of credit
card, personal ID,
passport

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.

15. Model Based
EBGM -Elastic Bunch Graph Method

16. Model Based
3D Face Recognition Method

17. PCA-Principal Component
Analysis(Eigen Face Method)

18. 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

19. 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)……..,

20. • 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

21. • 2. Each Training face defer from average by
vector Φ
Φi =Ti - Ψ
Φi
Eigen face
Each Image
Ti
Average Image
Ψ

22. Uk Eigen vector ,λk Eigen value of Covariance Matrix C
Where A is,
λk Eigen value
C= λk Uk

23. 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

24. 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 ||

25. 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.

26. thanks for patience !

27. Any Doubts ……