1. Faculty of Science IT Department Lecturer: Raz Dara MA.
Biometrics
Faculty of Science
IT Department
Lecturer: Raz Dara MA.

2. Definition
Biometrics from the worlds “Bio” and “metrics”. Bio means living things. Metrics means measure.
Currently, Biometrics stands for measuring human’s features for personal identification
Biometrics are automated methods of recognizing a person based on a physiological or behavioral characteristic.

3. Physical vs. Behavioral
(Passive)
(Active)

4. Traditional Methods Traditional means of automatic identification
Knowledge-based
Use “something that you know”
Examples: Password, pin
Token-based
Use “something that you have”
Examples: Credit card, Smart card, keys

5. Traditional Methods Problem
Problems that may occur:
Forgot your password!!!
Cards can be lost or stolen
PINs can be guessed

6. Solution: Biometrics Identify the rightful owner of token/knowledge
User convenience
Eliminate rejection claims
Difficult to copy or forge
Enhanced security
Cannot be Lost, forgotten or transferred

7. Biometrics Examples

8. Biometrics Classifications
Biometrics Data:

9. Biometrics Classifications
Biometrics Features:

10. Biometrics Classifications
Biometrics Characteristics:

11. Biometric Operation Modes

12. Feature Extraction The issue of choosing the features to be:
carry unique information about the object
easy to compute in order for the approach to be feasible

13. Feature Extraction
Feature selection for classification of Orange and Apple

14. 𝑋= 𝑥1 𝑥2 𝑥3 Feature Extraction Extracted Features: The color x1
𝑋= 𝑥1 𝑥2 𝑥3
The color x1
The shape x2
The surface smoothness x3
High Dimension
Low Dimension
256 x 256
1 x 3 vector

15. Feature Extraction
Extraction of the features requires the appropriate algorithm to be applied.
Depending on the application, many algorithms had been proposed
Feature Extraction process is very crucial for the overall performance of any pattern recognition system

16. Feature Extraction Some of the popular feature extraction algorithms:
PCA (Principal Component Analysis)
LDA (Linear Discriminant Analysis)
ICA (Independent Component Analysis)
LBP (Local Binary Pattern)
DWT (Discrete Wavelet Transform)
SIFT (Scale Invariant Feature Transform)
SURF (Speeded Up Robust Features)
CWT (Complex Wavelet Transform) …

17. Biometric Authentication
Identification “Who am I?”
Establish a person’s identity (1:N Matching)
Compare biometric template to all records in a database
Verification “Am I who I claim I am?”
Confirm or deny claimed identity (1:1 Matching)
Biometric template in combination with a smart card, username or ID number

18. Biometric Authentication
Comparison of Authentication Protocols
Combination of different methods can give stronger authentication

19. Biometric Authentication
Selection of biometrics technology is Application dependent
Two kinds of criteria

20. Biometrics Applications
Access (Cooperative, known subject)
Logical Access (Access to computer networks, systems, or files)
Physical Access (access to physical places or resources)
Transaction Logging
Surveillance (Non-cooperative, known subject)
Forensics (Non-cooperative or unknown subject)