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

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.

Physical vs. Behavioral (Passive) (Active)

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

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

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

Biometrics Examples

Biometrics Classifications Biometrics Data:

Biometrics Classifications Biometrics Features:

Biometrics Classifications Biometrics Characteristics:

Biometric Operation Modes

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

Feature Extraction Feature selection for classification of Orange and Apple

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

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

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) …

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

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

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

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)