AI Identity Verification
IDVerify AI is an advanced identity verification solution that automates user authentication using artificial intelligence. The system leverages computer vision, OCR, and deep learning models to verify government-issued identity documents and perform secure face recognition with liveness detection, ensuring fraud prevention and compliance for digital onboarding systems.
Reduction in Manual KYC Verification Time
Accuracy in Face & Document Matching
Faster Onboarding for New Users
GDPR Compliance and User Consent Control
Quick Project Snapshot
Austria
AI
B2B Integration Module
Agile
Client Overview
The client required a seamless and secure AI module to integrate within their existing platform to automate user identity verification. IDVerify AI combines document scanning, forgery detection, OCR-based text extraction, facial recognition, and liveness detection — all in one pipeline. The solution ensures authenticity by comparing document data with live facial input in real time while maintaining high accuracy and regulatory compliance.
Detects and localizes ID documents in video frames.
Uses OCR to extract and validate text, barcode, and signature placement.
Verifies holograms and other embedded security features for authenticity.
Captures live face data and compares it with ID document photos.
Embedding-based face similarity scoring ensures accurate identity matching.
Automatic pass/fail thresholds for instant validation.
Detects eye blinks, head turns, and frame consistency for live user checks.
Prevents spoofing attempts using static images or pre-recorded videos.
Integrates lightweight models to keep real-time responsiveness high.
Detects light reflection and hologram effects using motion and pattern analysis.
Captures front and back license scans to ensure physical authenticity.
Identifies altered or fake documents through optical variance checks.
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Project Goals
Replace manual KYC and ID checks with AI-driven automation.
Eliminate forgery and spoofing using document and face analysis.
Maintain full GDPR and consent-based verification.
Provide a plug-and-play module adaptable to any platform or workflow.
Challenges & Solutions
Integrating multiple models (document, face, OCR, liveness) into one flow without increasing latency or accuracy loss was complex.
Developed a modular AI pipeline with optimized routing — allowing independent processing and merging of outputs. Each sub-model communicates through microservices for faster response and scalability.
Variations in license templates between EU and US regions made detection and OCR accuracy inconsistent.
Built a custom document detection model trained on region-specific datasets. Implemented adaptive OCR parsing to recognize text fields dynamically, maintaining accuracy across formats.
Detecting real faces versus spoofed images or videos required lightweight models that could work on live video streams.
Implemented a hybrid liveness model combining blink and head-movement detection with frame quality checks. This achieved reliable anti-spoof performance without heavy computational load.
Extracted text from ID cards sometimes failed due to glare, blur, or low-quality video frames.
Added auto frame selection to capture the clearest image, paired with a multi-pass OCR system. Introduced a forgery check layer using hologram detection and light reflection validation.
Deploying GPU-based AI models with a consistent cost and performance balance required architectural tuning.
Deployed models on AWS g4dn.xlarge GPU instances, achieving 150K+ requests/month at ~$0.005 per request. Integrated auto-scaling logic for load balancing and future growth.
Tech Stack Used
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