Multimodal Biometric Integration for Forensic Identification Systems in Nigerian Border Security Contexts

Biometric identification systems integrating multiple modalities including fingerprint, facial recognition, iris scanning, and voice analysis offer enhanced accuracy and reliability for forensic identification in high-stakes border security contexts. This doctoral study develops and evaluates a multimodal biometric integration framework for forensic identification within Nigerian border security operations, addressing the technical and operational challenges specific to Nigeria's geographically and demographically diverse border environments. A systems engineering research design was employed, conducting a systematic review of forty-seven biometric integration architectures in comparable security contexts, developing an original fusion algorithm combining fingerprint, face, and iris modalities optimised for sub-Saharan African demographic variation, and validating the framework through controlled experiments with 500 participants recruited from border communities across North West, North East, and South South regions. The study also conducts a comprehensive human rights impact assessment of multimodal biometric deployment in Nigerian border contexts. The original fusion algorithm achieves an equal error rate of 0.37 percent, representing a sixty-one percent improvement over single-modality fingerprint systems under field conditions. Performance was consistent across demographic subgroups with no statistically significant accuracy disparity, addressing a critical bias concern in existing systems. The human rights assessment identifies proportionality, data sovereignty, and algorithmic accountability as primary governance challenges requiring legislative attention. The study makes original technical and policy contributions to Nigerian forensic biometrics and border security science. Keywords: multimodal biometrics, forensic identification, border security, Nigeria, fusion algorithm.