What is biometric AI and how does it work? (www.techradar.com)

Biometric AI combines multiple biometric modalities—face, voice, speech patterns and behavioral cues—using machine learning to create dynamic identity profiles that recognize not just how someone looks or sounds, but how they express themselves. Driven by rapid market adoption (digital identity was valued at ~$34.5B recently, and software-based facial recognition for payments is used by an estimated 1.4B people this year versus 671M in 2020), these systems fuse multimodal inputs and continuously learn from fresh login data to raise confidence in genuine users and flag anomalies. Technically this involves multimodal neural models, feature fusion, temporal pattern analysis and anomaly detection to make spoofing via recordings or deepfakes much harder. The shift matters because cybercriminals now deploy sophisticated, well-funded operations and deepfakes that can mimic voice and face convincingly—threats contributing to projected global cybercrime costs as high as $10.5T by 2025. AI-enabled biometrics therefore strengthen security for banks, governments and critical infrastructure by improving spoof resilience, enabling real-time liveness and behavioral checks, and even inferring attributes (age, emotion, some health signals) useful for authentication and empathetic agentic AI. The trade-offs: stronger protection and richer user models come with privacy, bias and governance challenges that the community must address as these systems scale.