This document provides an overview of biometrics technologies. It begins with an introduction to biometrics and then discusses the history of biometrics from ancient Egyptians and Chinese using fingerprints to modern systems being developed in the 1970s. The document outlines key characteristics biometrics must have such as universality and permanence. It then classifies and describes various biometric technologies including fingerprint, face, iris, voice, and signature recognition. Application examples are presented for areas like gaming, television control, and accessibility switches. The document concludes that biometrics provide a user-friendly way to interact with devices without passwords while continuing to develop as an emerging field.

2. Outline:-
 Introduction
 History
 Characteristics of Biometrics
 Working Principle of Biometrics
 Classification of Biometrics
 System Accuracy
 Comparison of Various Biometrics Technology
 Applications
 Conclusion

3. Introduction
What is biometrics?
Why biometrics?

4. Levels of Security

5. History
 The ancient Egyptians and the Chinese played a large role in
biometrics history.
 Biometrics in practice was a form of finger printing being used
in china in the 14th
century.
 Bertillon developed a technique of multiple body
measurements .
 A system called “Indentimat” which measured shape of the
hand and length of fingers was introduced in 1970s.

6. Characteristics of Biometrics
 Any human characteristic can qualify as a biometric
characteristic as long as it satisfies the following
requirements:-
 Universality
 Distinctiveness
 Permanence
 Collectability

7. Working of Biometrics System

8. Classification of Biometrics
1. Physiological – related to shape of the body.
 Fingerprint
 Facial recognisation
 Hand geometry
 Iris recognisation
2. Behavioral – related to the behavior of the person.
 Speaker recognisation
 Signature recognisation
 Gesture recognisation

9. Fingerprint Recognisation
 A fingerprint is made of a series of ridges and furrows on the
surface of the finger.
 Ridge ending, ridge bifurcation and minutiae points.
 Algorithm is developed to distinguish whorl, arch and loop.

10. Face Recognisation
 Analyze the unique shape, pattern and positioning of the facial
features.
 Face recognition is non-intrusive.
 There are about 80 peaks and valleys on a human face.

11. Continues……
 A face recognition system consists of the following modules:-
 Sensor module.
 Face detection and feature extraction module.
 Classification module.
 A face detection algorithms can be divided into three categories
according to
 Knowledge-based methods.
 Feature invariant approaches.
 Template-based methods.

12. Hand Geometry
 Based on a number of measurements taken from the human
hand.
 The technique is very simple, relatively easy to use, and
inexpensive.
 The physical size of a hand geometry-based system is large.

13. Iris Recognisation
 The iris of each eye of each person is absolutely unique. This
even applies to identical twins.
 Have over 200 unique spots and highly accurate technology.
 The false acceptance rate for iris recognition systems is 1 in 1.2
million.

14. Speaker Recognisation
 Uses individual’s voice for recognisation purposes.
 Voice sample.
 Depending on authentication domain
 Fixed text method.
 Text dependent.
 Text independent.

15. Signature Recognisation
 Measures and analyze the physical activity of signing.
 Banking or finance related applications.

16. Multimodal Biometrics System
 It utilize more than one physiological or behavioral
characteristic for enrollment, verification or identification.
 This system takes advantage of the capabilities of each
individual biometric.
 It can be used to overcome some of the limitations of a
single biometrics.

17. Gesture Recognisation
 Use of motions to communicate.
 Interact naturally without any mechanical devices.
 Depth-aware cameras.
 Stereo cameras.
 Controller based Gestures.

18. System Accuracy
 Accuracy or performance of biometric systems is measured
with three factors:-
 False acceptance rate (FAR)
 False rejection rate (FRR)
 Equal Error Rate (EER)

19. System Accuracy Curve

20. Misidentification Rate
Method Coded Pattern Misidentification
Rate
Iris Recognition Iris pattern 1/1,200,000
Fingerprinting Fingerprints 1/1,000
Facial Recognition Outline, shape and
distribution of eyes and
nose
1/100
Signature
Shape of letters, writing
order, pen pressure
1/100
Voice printing Voice characteristics 1/30

21. Comparison of Biometrics Technology
Biometrics Univers
ality
Uniquen
ess
Permane
nce
Collectab
ility
Perform
ance
Accepta
bility
Circum
vention
Fingerprint M H H M H M H
Face H L M H L H L
Hand
geometry
M M M H M M M
Iris H H H M H L H
Voice M L L M L H L
Signature L L L H L H H

22. Applications
1. Eye-gazed System:-
 The Eye gaze Edge uses the pupil-center/corneal-reflection method to
determine where the user is looking on the screen.

23. Portable Eye gaze System Mounted on Wheelchair

24. 2. Television Controlled by Hand Gestures:-
 Canesta 3D sensor
 CMOS Chip Technology

25. 3. Mimi Switch:-
 It uses infrared sensors.
 It stores and even interpret data.
 Can be used as a safety measure.

26. 4. Controller Free Gaming:-
 Project Natal is the name for a controller free Gaming.
 Using gestures and spoken commands.
 Depth Sensor.

28. Conclusion
 Biometrics is an emerging area with many opportunities for
growth.
 Not to remember passwords.
 User friendliness.
 A new way to interact with devices.

29. References
1. A. Jain et al: “BIOMETRICS: Personal Identification in Networked Society”,
Kluwer Academic Publishers, 1999, ISBN0-7923-8345-1.
2. S. Prabhakar, S. Pankanti, and A. K. Jain, “Biometric Recognition: Security
and Privacy Concerns”, IEEE Security and Privacy Magazine, Vol. 1, No. 2,
pp. 33-42, 2003.
3. http:// www.biometrics.org/
4. http://www.biometricsconsotorium.com
5. http://www.howstufworks.com
6. http://www.youtube.com/watch?v=FCEyHiLxuC8

30. THANK YOU

31. Any Questions