combinationbiometric

A combination biometric, or multi-modal biometric system, is an authentication approach that uses two or more distinct biometric traits to verify or identify an individual. By fusing information from multiple sources, these systems aim to improve accuracy, reliability, and resistance to spoofing compared with single-modality biometrics.

Fusion can occur at different stages: sensor-level (combining raw data), feature-level (combining feature sets), score-level (combining individual match scores), or decision-level (combining final decisions). Each level has trade-offs in complexity, privacy, and performance.

Common modality pairs include fingerprint and iris, face and voice, fingerprint and palm vein, or gait and handwriting. Multi-modal systems are used in mobile devices, border control, banking and enterprise access control, and high-security facilities to reduce false accepts and false rejects.

Advantages include improved accuracy, robustness to a poor sample from any single modality, and greater resistance to spoofing. Challenges encompass higher cost, greater data management requirements, enrollment time, and increased privacy and security concerns due to collecting multiple biometric traits.

Security considerations emphasize protecting stored templates, using cancellable biometrics or biometric cryptosystems, and complying with privacy regulations such as GDPR. Liveness detection and anti-spoofing measures are commonly employed for each modality.

Performance is assessed with metrics such as false acceptance rate, false rejection rate, and equal error rate; fusion often reduces these, yielding lower EER under appropriate calibration.