Fingerprint Regeneration Using GANs – Enhancing Biometric Access
A GAN-based approach that reconstructs degraded fingerprints for reliable authentication.
Overview
The project applies a CNN-based GAN to enhance worn or degraded fingerprints for people and forensic cases.Specialized loss functions and preprocessing focus the model on critical ridge patterns.Rigorous evaluation ensures regenerated prints match target identities.
The Challenges
Biometric systems often fail for individuals with faint fingerprints, leading to access denial:
Senior citizens frequently face lockouts from devices and services due to fingerprint wear.
Forensic investigations are hindered by partial or degraded prints from crime scenes.
Existing solutions lack the ability to intelligently reconstruct fingerprint ridge patterns from low - quality inputs.
Our Solution
We developed a novel deep learning model using a tailored GAN architecture.
Specialized GAN Architecture
Built a CNN-based encoder-decoder Generator to enhance low-quality images, paired with a Discriminator that compares real and generated fingerprint pairs. Used skip connections to preserve critical ridge details.
Custom Loss Functions
Combined GAN loss with a Masked L1 Loss, focusing the model's enhancement efforts precisely on the fingerprint region for higher fidelity regeneration.
Comprehensive Training Strategy
Trained the model on preprocessed fingerprint images (resized, normalized, masked) over 130 epochs with a balanced update ratio between Generator and Discriminator.
Rigorous Evaluation
Assessed model performance using accuracy, F1 score, and visual quality analysis to ensure regenerated prints were both numerically accurate and visually indistinguishable from high-quality targets.
Results
The GAN produced accurate, visually indistinguishable prints that passed biometric verification.
Achieved 90% accuracy in regenerated fingerprints.
High F1 score proved pattern alignment with targets.
Clients regained access for previously excluded users.
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