2- this biometrics is based on measurements and data derived from an actiona. Voice-scan b. Signature-scan c. Keystroke-scan
فاصله بین چشمهاپهنای بینیعمق کاسه چشماستخواهای گونهخطوط فکچانه
Madani University of
A biometric is a unique, measurable characteristic of a
being that can be used to automatically recognize an
or verify an individual’s identity.
2. behavioral characteristics
In Facial recognition there are two types of
VERIFICATION- The system compares the given
individual with who they say they are and gives a yes or
IDENTIFICATION- The system compares the given
individual to all the Other individuals in the database
and gives a ranked list of matches.
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
Verification of credit
card, personal ID,
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
3. Face recognition technology is applied to recognize the faces detected in the
Recognizing faces is done by algorithms that compare the
faces in your photo.
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
( 256x256 image = Vector of Dimension 65,536 )
• Training set of face images T1,T2,T3,……TM.-
• 1. Average Face of Image =Ψ = 1 ( ∑M Ti )
Ψ average face
; M –no. of images
• 2. Each Training face defer from average by
Φi =Ti - Ψ
Uk Eigen vector ,λk Eigen value of Covariance Matrix C
Where A is,
λk Eigen value
C= λk Uk
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)
Using Eigen faces Identify the New face
date base –eigen vectors U
ωk = UkT Φ
Its Eigen face (Φ)
Ω = ∑k=1m ωk=
minimum ||Ω - Ωk ||
Mathematical equations-Identify new
1. New face image T transform into it’s eigen face
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.