Forensic Biometrics
BSC-FS-304S
Unit-1
Definition, characteristics and operation of biometric system. Classification of biometricsystems
– physiological and behavioral. Strength and weakness of physiological and behavioral
biometrics. Multimodal biometrics. Key biometric processes – enrollment, identification and
verification. Positive and negative identification. Performance measures used in biometric
systems – FAR, FRR, GAR, FTA, FTE and ATV. Biometric versus traditional technologies.
Definition of Biometric
Biometrics is the scientific technique of identifying or verifying individuals based on their
unique physiological or behavioral characteristics.
OR
Biometrics is the automated process of recognizing individuals based on one or more intrinsic
physical or behavioral traits.
Key Components of Biometrics:
"Bio" – Refers to life or biological characteristics.
"Metrics" – Refers to measurement.
So, biometrics = biological measurements used for human recognition.
In forensic science, biometric systems play a crucial role in identifying suspects, verifying
identities, and linking individuals to crime scenes through scientifically measurable traits.
Definition of Biometric Systems
A biometric system is a computer system that implements biometric recognition algorithms. A
typical biometric system consists of sensing, feature extraction, and matching modules.
Biometric sensors (e.g., fingerprint sensor, digital camera for face) capture or scan the biometric
trait of an individual to produce its digital representation. A quality check is generally performed
to ensure that the acquired biometric sample can be reliably processed by the subsequent feature
extraction and matching modules. The feature extraction module discards the unnecessary and
extraneous information from the acquired samples, and extracts salient and discriminatory
information called features that are generally used for matching.
,During matching, the query biometric sample is matched with the reference information stored in
the database to establish the identity associated with the query. A biometric system has two
stages of operation: enrollment and recognition. Enrollment refers to the stage in which the
system stores some biometric reference information about the person in a database. This
reference information may be in the form of a template (features extracted from the biometric
sample or parameters of a mathematical model that best characterizes the extracted features) or
the biometric sample itself (e.g., face or fingerprint image). In many applications, some identity
attributes about the person (name, ID number, etc.) is also stored along with the biometric
reference.
When no personal identity information is available (e.g., unknown latent prints lifted from a
crime scene, anonymous authentication applications, etc.), the reference is usually tagged with a
system-generated ID for future recognition. In the recognition stage, the system scans the user’s
biometric trait, extracts features, and matches them against the reference biometric information
stored in the database. A high similarity score between the query and the reference data results in
the user being authenticated or identified.
Characteristics of Biometric Systems
For any biometric system to be reliable, secure, and effective, the biometric traits it uses must
satisfy certain key characteristics. These define how well a trait can be used for identification or
verification purposes.
1. Universality
Definition: The biometric trait should be present in every individual.
Importance: Ensures that the system can be applied to the entire target population.
Example: Every person has fingerprints and a face, making them universal traits.
2. Uniqueness
Definition: The trait should be sufficiently different among individuals.
Importance: Ensures that one person can be accurately distinguished from another.
Example: Even identical twins have different fingerprints.
3. Permanence
Definition: The trait should remain consistent over time.
Importance: Minimizes the need for re-enrollment and ensures long-term reliability.
Example: Iris patterns and fingerprints tend to be stable throughout life, whereas voice
may change.
4. Collectability (Measurability)
Definition: The trait must be measurable quantitatively and should be captured using
available sensors.
, Importance: Affects ease of data acquisition and system accuracy.
Example: Fingerprints can be easily scanned using a simple optical sensor.
5. Performance
Definition: Indicates the accuracy, speed, and resource efficiency of the system.
Importance: A high-performing system minimizes false matches and delays in
identification.
Key Indicators: False Acceptance Rate (FAR), False Rejection Rate (FRR), and
Average Transaction Time (ATV).
6. Acceptability
Definition: Measures the degree to which people are willing to use the biometric system.
Importance: A biometric system, no matter how accurate, is ineffective if users find it
intrusive or uncomfortable.
Example: Facial recognition is generally more acceptable than retina scanning, which
requires close contact with the eye.
7. Resistance to Circumvention
Definition: The difficulty involved in fooling or spoofing the system.
Importance: Essential for maintaining the security of the system.
Example: Fingerprint systems may be spoofed using silicone molds unless liveness
detection is implemented.
Summary Table
Characteristic Description Example
Universality Present in every individual Everyone has fingerprints
Uniqueness Distinguishable between Fingerprint patterns
individuals
Permanence Consistency over time Iris pattern remains unchanged
Collectability Can be measured and processed Face can be captured via
camera
Performance Accuracy and efficiency of the Low FAR and FRR
system
Acceptability User willingness to cooperate High for face recognition
Resistance to Difficulty in spoofing or deceiving Liveness detection in
Circumvention the system fingerprint scanners
BSC-FS-304S
Unit-1
Definition, characteristics and operation of biometric system. Classification of biometricsystems
– physiological and behavioral. Strength and weakness of physiological and behavioral
biometrics. Multimodal biometrics. Key biometric processes – enrollment, identification and
verification. Positive and negative identification. Performance measures used in biometric
systems – FAR, FRR, GAR, FTA, FTE and ATV. Biometric versus traditional technologies.
Definition of Biometric
Biometrics is the scientific technique of identifying or verifying individuals based on their
unique physiological or behavioral characteristics.
OR
Biometrics is the automated process of recognizing individuals based on one or more intrinsic
physical or behavioral traits.
Key Components of Biometrics:
"Bio" – Refers to life or biological characteristics.
"Metrics" – Refers to measurement.
So, biometrics = biological measurements used for human recognition.
In forensic science, biometric systems play a crucial role in identifying suspects, verifying
identities, and linking individuals to crime scenes through scientifically measurable traits.
Definition of Biometric Systems
A biometric system is a computer system that implements biometric recognition algorithms. A
typical biometric system consists of sensing, feature extraction, and matching modules.
Biometric sensors (e.g., fingerprint sensor, digital camera for face) capture or scan the biometric
trait of an individual to produce its digital representation. A quality check is generally performed
to ensure that the acquired biometric sample can be reliably processed by the subsequent feature
extraction and matching modules. The feature extraction module discards the unnecessary and
extraneous information from the acquired samples, and extracts salient and discriminatory
information called features that are generally used for matching.
,During matching, the query biometric sample is matched with the reference information stored in
the database to establish the identity associated with the query. A biometric system has two
stages of operation: enrollment and recognition. Enrollment refers to the stage in which the
system stores some biometric reference information about the person in a database. This
reference information may be in the form of a template (features extracted from the biometric
sample or parameters of a mathematical model that best characterizes the extracted features) or
the biometric sample itself (e.g., face or fingerprint image). In many applications, some identity
attributes about the person (name, ID number, etc.) is also stored along with the biometric
reference.
When no personal identity information is available (e.g., unknown latent prints lifted from a
crime scene, anonymous authentication applications, etc.), the reference is usually tagged with a
system-generated ID for future recognition. In the recognition stage, the system scans the user’s
biometric trait, extracts features, and matches them against the reference biometric information
stored in the database. A high similarity score between the query and the reference data results in
the user being authenticated or identified.
Characteristics of Biometric Systems
For any biometric system to be reliable, secure, and effective, the biometric traits it uses must
satisfy certain key characteristics. These define how well a trait can be used for identification or
verification purposes.
1. Universality
Definition: The biometric trait should be present in every individual.
Importance: Ensures that the system can be applied to the entire target population.
Example: Every person has fingerprints and a face, making them universal traits.
2. Uniqueness
Definition: The trait should be sufficiently different among individuals.
Importance: Ensures that one person can be accurately distinguished from another.
Example: Even identical twins have different fingerprints.
3. Permanence
Definition: The trait should remain consistent over time.
Importance: Minimizes the need for re-enrollment and ensures long-term reliability.
Example: Iris patterns and fingerprints tend to be stable throughout life, whereas voice
may change.
4. Collectability (Measurability)
Definition: The trait must be measurable quantitatively and should be captured using
available sensors.
, Importance: Affects ease of data acquisition and system accuracy.
Example: Fingerprints can be easily scanned using a simple optical sensor.
5. Performance
Definition: Indicates the accuracy, speed, and resource efficiency of the system.
Importance: A high-performing system minimizes false matches and delays in
identification.
Key Indicators: False Acceptance Rate (FAR), False Rejection Rate (FRR), and
Average Transaction Time (ATV).
6. Acceptability
Definition: Measures the degree to which people are willing to use the biometric system.
Importance: A biometric system, no matter how accurate, is ineffective if users find it
intrusive or uncomfortable.
Example: Facial recognition is generally more acceptable than retina scanning, which
requires close contact with the eye.
7. Resistance to Circumvention
Definition: The difficulty involved in fooling or spoofing the system.
Importance: Essential for maintaining the security of the system.
Example: Fingerprint systems may be spoofed using silicone molds unless liveness
detection is implemented.
Summary Table
Characteristic Description Example
Universality Present in every individual Everyone has fingerprints
Uniqueness Distinguishable between Fingerprint patterns
individuals
Permanence Consistency over time Iris pattern remains unchanged
Collectability Can be measured and processed Face can be captured via
camera
Performance Accuracy and efficiency of the Low FAR and FRR
system
Acceptability User willingness to cooperate High for face recognition
Resistance to Difficulty in spoofing or deceiving Liveness detection in
Circumvention the system fingerprint scanners
