BioPrint
Fingerprint Enrollment, Matching, and Image Spoof Detection System
We developed a multimodal biometric authentication system with fingerprint and facial recognition, integrated with OCR-based document scanning. The solution enables secure, real-time identity verification across multiple languages and document types, delivering high accuracy and fraud prevention capabilities.
What We Built
A fully integrated identity verification ecosystem using:
- Fingerprint detection algorithm for data capture, enrollment, and matching
- Custom CNN models for facial recognition with liveness detection
- Amazon Textract for OCR-based document scanning
- OpenCV for image preprocessing and liveness analysis
- Custom APIs for modular integration with client systems
- AWS for scalable, secure cloud deployment
The system acts as a secure identity orchestration engine, verifying users across multiple biometric and document-based signals in real time.
Key Features
Fingerprint Auth
- Secure fingerprint data capture and storage
- High-accuracy matching across extensive datasets
- Minutiae-based feature extraction
Face Scan/Liveness
- CNN-based face recognition for accurate identification
- Liveness detection to differentiate live users from spoofing attempts
- Prevents photo, video, and mask-based attacks
ID Verification
- Multi-country & multi-language support
- Extracts key fields: type, issue & expiry date
- Supports passports, licenses, birth certificates, national IDs
Modular API's
- Supports fingerprint, face, liveness & document scanning
- Seamless client integration
- Modular & flexible components
How It Works
User submits fingerprint, facial image, and identity document
Fingerprint module captures and matches minutiae points against stored templates
Facial recognition verifies user identity and liveness detection ensures authenticity
Document scanning module extracts structured data via OCR across supported langs.
All verification results are aggregated and validated against system rules
System returns final authentication decision with confidence scores
Results
93%
verification accuracy across biometric and document scanning modules
71%
reduction in verification time compared to manual processes
Consistent
data accuracy across multimodal inputs
Robust
fraud detection across multiple spoof attack types
Value Dilevered
This project transformed identity verification into a fully automated, secure, and scalable process. By combining fingerprint and facial recognition with liveness detection and OCR-based document scanning, the system provides comprehensive fraud prevention, operational efficiency, and reliable authentication for high-security applications such as border control, banking KYC, and corporate access control.
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#Biometric Authentication #Fingerprint Matching #Facial Recognition #Liveness Detection #OCR #Document Verification #Cybersecurity #AWS