Deep Learning Model for Biometric Security

Word Count : 3500

Objectives to cover:

• Introduction
  Introduces biometric security and explains the growing importance of deep learning models.

• Overview of Biometric Security Systems
  Describes core biometric types like fingerprint, face, iris, and voice recognition.

• Role of Deep Learning in Biometric Authentication
  Explains how deep learning improves accuracy, feature extraction, and spoof detection.

• Deep Learning Architectures Used in Biometrics
  Covers CNNs, RNNs, Autoencoders, and Siamese Networks used for biometric analysis.

• Dataset Preparation and Pre-processing
  Highlights the need for clean datasets and image enhancement steps before training.

• Attack Vectors and Threats in Biometric Systems
  Discusses threats such as spoofing, deepfakes, replay attacks, and adversarial inputs.

• Deep Learning-Based Anti-Spoofing Techniques
  Explains how models detect fake or manipulated biometric data using liveness checks.

• Performance Evaluation Metrics
  Defines accuracy, FAR, FRR, EER, and latency as key biometric model metrics.

• Challenges and Future Scope
  Identifies limitations like dataset bias, privacy issues, and computational demands.

• Conclusion
  Summarizes how deep learning strengthens biometric security and future advancements.