What if a fraudster could bypass face authentication just because the biometric system had to process blurry, low-resolution, or poorly lit images?
Different banks and fintech companies are investing $millions in face authentication and fraud prevention tech, overlooking a critical factor: the quality of facial images being processed.
Even though face recognition algorithms work best with high-quality images, many companies unknowingly operate with datasets containing poor images—faces obscured by low lighting, bad angles, or occlusions like masks and glasses. The result? Increased false positives (mistaking one person for another), false negatives (failing to recognize a legitimate customer), and a higher risk of fraud.
The good news? Our quality assessment algorithm (3DiVi QAA), validated by NIST FATE Quality ratings, makes it simple to spot low-quality images.
Our internal tests show that filtering out just 5% of low-quality images with 3DiVi QAA can boost accuracy by over 40%, ensuring fewer authentication failures, smoother customer onboarding, and stronger fraud prevention.
Our internal tests show that filtering out just 5% of low-quality images with 3DiVi QAA can boost accuracy by over 40%, ensuring fewer authentication failures, smoother customer onboarding, and stronger fraud prevention.
How to Get a One-Time Dataset Check-Up
Step 1: Get the instructions and download a script for analysis.
Step 2: Select 1% to 3% of random facial images from your dataset and run the analysis.
Step 3: Receive a CSV file with anonymized image quality data.
Step 4: Send the CSV file and get a quality report with recommendations for further actions.
Cost: One-time dataset check-up and quality report – Free of Charge.
Timeframe: The entire process takes no more than 3 days.
Timeframe: The entire process takes no more than 3 days.
Beyond the Check-Up: 4 Tips for Long-Term Biometric Data Quality
Tip 1: Full Dataset Review – Systematically analyze all stored images in your dataset.
Tip 2: Bad Data Replacement – Remove or improve low-quality images that pose authentication risks.
Tip 3: Continuous Quality Control – Set up ongoing validation to ensure only clear, usable images are captured and stored.
Tip 4: Environmental Adjustments – Ensure proper lighting, camera resolution, and positioning guidelines during image capture.
Want to see how your biometric dataset holds up? Book a consultation with 3DiVi experts to start your free dataset check-up.
Let’s solve the problem of poor images before it costs you $millions.
