shimaa22/FingerVeinFeatureEtractionModel

🩺 Finger Vein Feature Extractor using MobileNet

This pretrained model is designed for finger vein recognition. It uses a MobileNet-based feature extractor trained on finger images to extract deep biometric features.

🔧 How It Works:

• The model first extracts features from finger vein images using MobileNet.
• These features are then used to form image pairs.
• A deep neural network (e.g. Siamese) is trained on these pairs to learn a similarity metric.
• Finally, the system classifies whether two finger vein images belong to the same person or not.

📦 Use Cases:

• 🔐 Biometric authentication systems
• 🔍 Finger vein matching or verification
• 🧬 Medical/Forensic identification tasks

🖼️ Input:

• RGB finger vein image (resized to 224×224)
• Normalized to [0, 1]

📤 Output:

• Feature vector (if using encoder only)
• Or: Match / No-match decision (in Siamese setup)

💾 Model Format:

• model.keras — Keras format for MobileNet feature extractor

💾 code Licence:

Alaerjan, A.S., Mostafa, A.M., Mahmoud, A.A. et al. Efficient multi-finger vein recognition using layer-wise progressive MobileNet fine-tuning and a Dense-Head Probabilistic Siamese Network. Sci Rep (2025). https://doi.org/10.1038/s41598-025-32132-5