ID Card Face Match Verification

ID Card Face Match Verification: Core Features and Principles

When you need to confirm if an ID card belongs to the person holding it, you often face challenges like outdated photos or inefficient manual verification. This tool uses deep learning algorithms to compare the ID card chip photo or scan with a live-captured face photo based on facial landmarks, outputting a 0-100% similarity score and matching conclusion. In facial recognition, 'facial landmarks' refer to a digital fingerprint composed of 128 key biometric indicators, including eye distance, nose bridge angle, and lip contour.

Why Choose Our ID Card Face Verification?

• Supports parsing of standard ID card chip photos, automatically correcting tilt and moiré patterns.
• Utilizes the LResNet100 facial recognition model, achieving 99.7% accuracy in NIST FRVT testing.
• Real-time processing with a response time of <800ms, supporting API integration into business systems.

How to Use

1. Enter the first 17 digits of the ID number (the final 'X' must be capitalized).
2. Upload the ID card chip photo or a high-definition scan (DPI ≥ 300 recommended).
3. Upload a live-captured, front-facing photo of the person without headwear (ensure even lighting and no obstructions).
4. Click "Compare Now" to get a timestamped verification report.

Frequently Asked Questions (FAQ)

Q: Does glare on the ID card photo affect the comparison?
Yes, it reduces accuracy. We recommend using the chip reading feature or adjusting the scanning angle to avoid glare.

Q: What similarity score is considered a match?
The industry standard threshold is 92%: ≥92% is determined as the same person, <85% is determined as not the same person, and 85-92% requires manual review.

Important Notes

Please ensure the ID card is within its validity period. We recommend using a fill light for night photography. For batch verification, please apply for enterprise API access. The results are for reference only and hold no legal validity.

Technical Notes & Best Practices

For remote financial account opening scenarios, we recommend combining this tool with liveness detection. For example: an ID card chip photo compared against a live video frame yields a 96.2% similarity score; when paired with a blink verification, this effectively prevents high-definition photo spoofing attacks. Note that for individuals under 18, facial features change due to growth, so we recommend updating the baseline comparison photo every 2 years.