Humans often use faces to recognize individuals, and advancements in computing capability over the past few decades now enable similar recognitions automatically. Early facial recognition algorithms used simple geometric models, but the recognition process has now matured into a science of sophisticated mathematical representations and matching processes. Major advancements and initiatives in the past 10 to 15 years have propelled facial recognition technology into the spotlight. Facial recognition can be used for both verification and identification.

1. FACE
RECOGNITION
TECHNOLOGY
SHRAVAN HALANKAR
GOA UNIVERSITY
ELECTRONICS DEPT.

2. O U T L I N E
1. Introduction
2. Biometrics
3. Why Face Recognition?
4. Implementation
5. How it works?
6. Strengths & Weaknesses
7. Major Challenges in FRT
8. Applications
9. Future of FRT………

3. I N T R O D U C T I O N
 Everyday actions are increasingly being handled
electronically, instead of ink and paper or face to face.
 This growth in electronic transactions results in great
demand for fast and accurate user identification and
authentication.
 Access codes for buildings, banks accounts and computer
systems often use PIN's for identification and security
clearances.
 Using the proper PIN gains access, but the user of the PIN
is not verified. When credit and ATM cards are lost or
stolen, an unauthorized user can often come up with the
correct personal codes.
 BIOMETRICS technology may solve this problem .

4. 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.
Biometrics system is an automated system of
identifying a person based on person’s physical or
behavioral characteristics.

5. TYPES OF BIOMETRICS
Fingerprints Hand Geometry Retina
Signature Iris FACE RECOGNITION

6. • Face recognition technology is the least intrusive and fastest biometric
technology. It works with the most obvious individual identifier – the
human face.
• It requires no physical interaction on behalf of the user.
• It can use your existing hardware infrastructure, existing cameras and
image capture Devices will work with no problems

7. • It is only Biometric that allow you to perform passive identification in many
environments. (e.g: Identifying terrorist in a busy Airport terminal.)
• It does not require an expert to interpret the comparison result.
ACCURATE!!

8. THE HUMAN FACE
• The face is an important part of who
you are and how people identify you.
• In face recognition there are two
types of comparisons :
Verification Identification
This is where the system
compares the given
individual with who that
individual says they are,
and gives a yes or no
decision.
This is where the system
compares the given individual
to all the Other individuals in
the database and gives a
ranked list of matches.

9. ALL IDENTIFICATION OR AUTHENTICATION TECHNOLOGIES OPERATE
USING THE FOLLOWING FOUR STAGES:
1)Capture
A physical or behavioral sample is
captured by the system during
Enrollment and also in identification or
verification process
2)Extraction
Unique data is extracted from the
sample and a template is created.
3)Comparison
The template is then compared with a
new sample stored in the data base
4)Match /non match
The system decides if the features
extracted from the new Samples are a
match or a non match
IMPLEMENTATION
Input face
image(Capture)
Face feature
extraction
Feature
Matching Decision maker
Output
result
Face
database

10. COMPONENTS OF FACE RECOGNITION SYSTEM
Enrollment Module
An automated mechanism that
scans and captures a digital or
an analog image of a living
personal characteristics.
Database
Another entity which handles
compression, processing,
storage and compression of the
captured data with stored data .
Verification Module
Also consists of a preprocessing system.
In this module the newly obtained sample is preprocessed and
compared with the sample stored in the database.
The decision is taken depending on the match obtained from the
database.

12. Every face has at least 80 distinguishable parts called nodal points. Some of
them are:

13. Every face has at least 80 distinguishable parts called nodal points. Some of
them are:
1. Distance between the eyes

14. Every face has at least 80 distinguishable parts called nodal points. Some of
them are:
1. Distance between the eyes
2. Width of the nose

15. Every face has at least 80 distinguishable parts called nodal points. Some of
them are:
1. Distance between the eyes
2. Width of the nose
3. Depth of eye sockets

16. Every face has at least 80 distinguishable parts called nodal points. Some of
them are:
1. Distance between the eyes
2. Width of the nose
3. Depth of eye sockets
4. Structure of cheek bones

17. Every face has at least 80 distinguishable parts called nodal points. Some of
them are:
1. Distance between the eyes
2. Width of the nose
3. Depth of eye sockets
4. Structure of the cheek bones
5. Length of jaw line

18. A general face recognition software conducts a comparison of
these parameters to the images in its database.
Depending upon the matches found, it determines the result.
This technique is known as feature based matching and it is the
most basic method of facial recognition.

19. A 3D facial recognition model provides greater accuracy
than the feature extraction model.
It can also be used in a dark surroundings and has a ability
to recognize the subject at different view angles.
Using 3D software, the system
Goes through a number of steps
to verify the identity of an
individual.

20. Acquiring an image can be done
through a digital scanning device.
Once it detects the face, the system
determines heads position, size and
pose.

21. The system then measures the
curves of the face on a sub-millimeter
scale and creates a
template.
The system translates this
template into a unique code.

22. The image thus acquired will be
compared to the images in the
data base and if 3D images are
not available to the database,
then algorithms used to get a
straight face are applied to the
3D image to be matched.
Finally in verification, the image
is matched to only one image in
the database and the result is
displayed as shown alongside.

23. The most commonly used unique feature for facial
recognition is iris of the eye. No two human beings,
even twins have exactly similar iris.

24. 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

25. EIGEN FACE OR PCA
(PRINCIPAL COMPONENT ANALYSIS)
• PCA, commonly referred to as the use of eigenfaces,
this technique pioneered by Kirby and Sirivich in
1988.
• The PCA approach typically requires the full frontal
face to be presented each time otherwise the
image results in poor performance.
• The primary advantage of this technique is that it can
reduce the data needed to identify the individual to
1/1000th of the data presented

26. 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

27. 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)
x

28. • Training set of face images T1,T2,T3,……TM.-
• 1. Average Face of Image =Ψ = 1 ( ΣM Ti ) ; M –no. of images
M i=1
Ψ average face

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

30. FACE IMAGES USING AS EIGEN FACES
TRAINING IMAGES
-IMAGE MUST BE IN SAME SIZE-Face
database

31. Elastic Bunch Graph Matching
(EBGM)
• EBGM relies on the concept that real face images have many non-linear
characteristics that are not addressed by the linear analysis
methods .
• Such as variations in illumination(outdoor lighting vs. indoor
fluorescents), pose (standing straight vs. leaning over) and
expression (smile vs. frown).
• A Gabor wavelet transform creates a dynamic link architecture that
projects the face onto an elastic grid.
• The Gabor jet is a node on the elastic grid, notated by circles on
the image below, which describes the image behavior around a
given pixel.

32. Elastic Bunch Graph Matching
(EBGM)
• Recognition is based on the similarity of the Gabor
filter response at each Gabor node.
• The difficulty with this method is the requirement of
accurate landmark localization.

33. STRENGTHS AND WEEKNESSES OF FRT
STRENGTHS
 It has the ability to leverage
existing image acquisition
equipment.
 It can search against static
images such as driver’s
license photographs.
 It is the only biometric able
to operate without user
cooperation.
WEEKNESSES
 Changes in acquisition environment
reduce matching accuracy.
 Changes in physiological
characteristics reduce matching
accuracy.
 It has the potential for privacy
abuse due to no cooperative
enrolment and identification
capabilities.

34. PERFORMANCE OF FRT
False acceptance
rates(FAR)
• The probability that a system will
incorrectly identify an individual
or will fail to reject an imposter.
• FAR= NFA/NIIA
• Where FAR= false acceptance rate
NFA= number of false acceptance
NIIA= number of imposter
identification attempts
False rejection
rates(FRR)
• The probability that a system will
fail to identify an enrollee.
• FRR= NFR/NEIA
• Where FRR= false rejection rates
NFR= number of false rejection rates
NEIA= number of enrollee
identification attempt

35. The only way to overcome this challenge is
better equipment, i.e. basically , use of high
tech cameras.
It is very much essential for the system to
catch the image accurately.

37. The only way to overcome this challenge is better
ALGORITHMS for facial recognitions. If the systems are
programmed for every possible permutation and
combination of the image, an accurate match can be
achieved.

38. APPLICATIONS

39. It is being estimated that facial recognition technology
will be the backbone of all major security, home and
networking service.
With the growth of social networking over the web,
unbelievably accurate facial recognition algorithms and
advanced equipment, a person’s face, no mater ageing
or disguises or damage, can be recognized and data
about that person can be produced.
Twins Recognition.

40. QUESTIONS ?

41. 1.www.biometrics.gov
2.www.wikipedia.com
3.www.howstuffworks.com
4.www.face-rec.org
5.www.facedetection.com
6.Recognizing Face Images with
Disguise Variations
Richa Singh, Mayank Vatsa and Afzel Noore
Lane Department of Computer Science & Electrical Engineering, West Virginia
University,USA