Multi-Biometrics - CSU1530 - Shoolini U

Multi-Biometrics

1. Introduction to Multibiometrics

Multibiometrics involves the use of multiple biometric indicators to identify or verify an individual. By combining different biometric modalities, such as fingerprint, face, voice, or iris, multibiometric systems enhance recognition accuracy and reliability compared to systems relying on a single biometric trait.

Key applications include:

2. Types of Multibiometric Systems

Multibiometric systems can be categorized based on how and where the fusion of biometric data occurs. Fusion can happen at various levels within the biometric recognition process.

2.1 Sensor-Level Fusion

Combines raw data from multiple sensors capturing the same or different biometric traits.

Characteristics:

2.2 Feature-Level Fusion

Integrates feature sets extracted from multiple biometric modalities.

Methods:

Mathematical representation:

Given feature vectors \( \mathbf{f}_1 \in \mathbb{R}^{n} \) and \( \mathbf{f}_2 \in \mathbb{R}^{m} \), the fused feature vector \( \mathbf{F} \) is:

$$ \mathbf{F} = [\mathbf{f}_1; \mathbf{f}_2] \in \mathbb{R}^{n+m} $$

2.3 Score-Level Fusion

Combines matching scores obtained from individual biometric matchers.

Techniques:

Weighted sum formula:

$$ S_{\text{fused}} = w_1 S_1 + w_2 S_2 + \dots + w_k S_k $$

2.4 Decision-Level Fusion

Combines the final decisions (accept/reject) from individual biometric systems.

Approaches:

Example of majority voting:

If three systems provide decisions \( D_1, D_2, D_3 \in \{\text{Accept}, \text{Reject}\} \), the fused decision \( D_{\text{fused}} \) is:

$$ D_{\text{fused}} = \text{mode}(D_1, D_2, D_3) $$

3. Advantages of Multibiometrics

Multibiometric systems offer several benefits over unibiometric systems.

3.1 Increased Accuracy

By combining multiple biometric traits, the system reduces the chance of misidentification.

Benefits:

3.2 Improved Security

Using multiple biometrics makes it more difficult for unauthorized individuals to deceive the system.

Aspects:

3.3 Flexibility and Convenience

Allows users to authenticate using different modalities based on context or accessibility.

Advantages:

4. Challenges in Multibiometrics

Despite the benefits, multibiometric systems face specific challenges.

4.1 Data Synchronization

Ensuring that biometric data from different sources are correctly aligned in time and space.

Considerations:

4.2 Computational Complexity

Processing multiple biometric modalities increases computational demands.

Implications:

4.3 Fusion Strategy Selection

Choosing the appropriate fusion method impacts system performance.

Factors:

5. Fusion Techniques

Different methods are used to combine biometric data at various levels of the recognition process.

5.1 Sensor-Level Fusion Techniques

Combining raw data from multiple sensors.

Methods:

Example:

Combining fingerprint images from multiple fingers to form a single composite image for feature extraction.

5.2 Feature-Level Fusion Techniques

Integrating feature sets into a single feature vector.

Approaches:

PCA transformation:

Given a high-dimensional feature vector \( \mathbf{F} \), PCA projects it onto a lower-dimensional subspace:

$$ \mathbf{F}_{\text{reduced}} = W^T \mathbf{F} $$

5.3 Score-Level Fusion Techniques

Combining matching scores from different biometric systems.

Common rules:

Weights can be assigned based on the performance of each biometric system.

5.4 Decision-Level Fusion Techniques

Combining accept/reject decisions from multiple systems.

Strategies:

Logical AND operation:

$$ D_{\text{fused}} = D_1 \land D_2 \land \dots \land D_k $$

The individual is accepted only if all systems agree on acceptance.

6. Evaluation Metrics

Assessing the performance of multibiometric systems requires appropriate metrics.

6.1 False Acceptance Rate (FAR)

The probability that unauthorized individuals are incorrectly accepted.

Formula:

$$ \text{FAR} = \frac{\text{Number of False Acceptances}}{\text{Total Number of Impostor Attempts}} $$

6.2 False Rejection Rate (FRR)

The probability that authorized individuals are incorrectly rejected.

Formula:

$$ \text{FRR} = \frac{\text{Number of False Rejections}}{\text{Total Number of Genuine Attempts}} $$

6.3 Receiver Operating Characteristic (ROC) Curve

Plots the trade-off between FAR and FRR at various thresholds.

Features:

7. Implementation Example

An example of a multibiometric system combining fingerprint and face recognition at the score level.

7.1 Data Acquisition

Collect fingerprint images and face images for each individual.

7.2 Feature Extraction

Extract features from both modalities.

# Fingerprint feature extraction
fingerprint_features = extract_minutiae(fingerprint_image)

# Face feature extraction using PCA
face_features = pca.transform(face_image_flattened)

7.3 Matching Scores Computation

Compute matching scores for each modality.

# Fingerprint matching score
fingerprint_score = fingerprint_matcher.compare(fingerprint_features, stored_fingerprint_template)

# Face matching score
face_score = face_matcher.compare(face_features, stored_face_template)

7.4 Score-Level Fusion

Combine the scores using the weighted sum rule.

# Assign weights based on modality performance
w_fingerprint = 0.6
w_face = 0.4

# Compute fused score
fused_score = w_fingerprint * fingerprint_score + w_face * face_score

Set a threshold to decide acceptance or rejection.

# Threshold for acceptance
threshold = 0.75

if fused_score >= threshold:
    decision = 'Accept'
else:
    decision = 'Reject'

7.5 System Evaluation

Evaluate the system using test data to calculate FAR, FRR, and plot ROC curves.

from sklearn.metrics import roc_curve

# Compute scores for genuine and impostor attempts
genuine_scores = []
impostor_scores = []

for attempt in genuine_attempts:
    # Compute fused score for genuine attempts
    # Append to genuine_scores
    pass

for attempt in impostor_attempts:
    # Compute fused score for impostor attempts
    # Append to impostor_scores
    pass

# Combine scores and labels
scores = genuine_scores + impostor_scores
labels = [1]*len(genuine_scores) + [0]*len(impostor_scores)

# Compute ROC curve
fpr, tpr, thresholds = roc_curve(labels, scores)

# Plot ROC curve
import matplotlib.pyplot as plt
plt.plot(fpr, tpr)
plt.xlabel('False Positive Rate')
plt.ylabel('True Positive Rate')
plt.title('ROC Curve for Multibiometric System')
plt.show()

8. Applications and Use Cases

Multibiometric systems are employed in various domains requiring high security and reliability.

8.1 Border Control

Enhancing traveler identification by combining multiple biometrics, reducing the risk of identity fraud.

8.2 Secure Access Facilities

Restricting access to sensitive areas by requiring multiple forms of biometric verification.

8.3 Financial Transactions

Securing banking and payment systems through robust user authentication.

9. Conclusion

Multibiometrics leverages the strengths of multiple biometric modalities to improve identification accuracy, security, and user convenience. By understanding the various fusion techniques and addressing associated challenges, effective multibiometric systems can be designed for a wide range of applications.