AI-Enabled Multimodal Biometrics

Abstract

Abstract: AI-Enabled Multimodal Biometrics develops a rigorous, end-to-end framework for identity recognition that fuses complementary physiological and behavioral signals—face, iris, fingerprint, voice, gait, and interaction dynamics—using modern machine learning. The book unifies problem formulations (verification, identification, re-identification, continuous authentication) under a statistical decision-theoretic lens, connecting likelihood-ratio testing, open-set recognition, uncertainty quantification, and cost-sensitive operating points. It advances a taxonomy of fusion (feature, representation, score/decision), synchronized and asynchronous streaming, and edge–cloud partitioning, with practical guidance on sensing, preprocessing, and representation learning (supervised, contrastive, and self-supervised). System engineering chapters translate models into deployable pipelines with MLOps, calibration, quality-aware routing, and segmented evaluation (ROC/DET, EER, CMC, mAP; APCER/BPCER for PAD). Security and privacy are addressed via threat modeling, multi-sensor PAD, template protection, differential privacy, TEEs, and federated learning, mapped to regulatory obligations through reproducible reporting and biometric model/data cards. Cross-regional case studies (public services, finance, transit, healthcare, education) demonstrate how legal, cultural, and infrastructural constraints shape design choices and performance. The volume targets researchers, practitioners, and policymakers with figure-ready checklists, experiment dossiers, and procurement language, bridging scientific rigor with operational accountability to produce trustworthy, inclusive, and sustainable biometric systems. Keywords: AI-enabled biometrics, multimodal fusion, open-set recognition, presentation attack detection, uncertainty calibration, score calibration, domain adaptation, continual learning, federated learning, fairness and bias, evaluation protocols (ROC/DET, CMC, mAP), APCER/BPCER, privacy-enhancing technologies, template protection, differential privacy, explainable AI, edge–cloud partitioning, MLOps, model/data cards, compliance-by-design