AI Biometric Recognition: Fingerprint, Iris, Face, and Multimodal Deep Learning Systems

Status: public · Confidence: medium (0.865) · Basis: verified_sources

## TL;DR
AI Biometric Recognition: Fingerprint, Iris, Face, and Multimodal Deep Learning Systems: Biometric recognition uses measurable biological or behavioral signals such as faces, fingerprints, irises, or voices to verify or identify people.

## Core Explanation
Modern face recognition often uses neural embeddings trained to separate identities. Independent evaluations such as NIST FRTE help compare algorithms under defined protocols. Presentation attack detection is separate from matching and addresses spoofing attempts.

## Further Reading

- [ArcFace: Additive Angular Margin Loss for Deep Face Recognition](https://arxiv.org/abs/1801.07698)
- [Face Recognition Technology Evaluation 1:1 Verification](https://pages.nist.gov/frvt/html/frvt11.html)
- [ISO/IEC 30107-3:2023 Biometric presentation attack detection](https://www.iso.org/standard/79520.html)