Multi-modal biometric authentication integrating gait and face recognition for mobile security

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dc.contributor.authorNivedita, V.
dc.contributor.authorJoseph, Juno Ann
dc.contributor.authorVarghese, Divya Thankom
dc.contributor.authorNithyanandh, S
dc.contributor.authorSundaram, Jayavelu
dc.contributor.authorAli, Guma
dc.date.accessioned2025-12-19T12:53:12Z
dc.date.available2025-12-19T12:53:12Z
dc.date.issued2025-10-28
dc.descriptionThis paper introduces a blockchain-integrated supply chain management framework using IoT and AI to enhance transparency, traceability, and security in logistics operations. The system leverages smart contracts and predictive analytics to optimize resource allocation and reduce fraud, supporting SDG 9 (Industry, Innovation, and Infrastructure) and SDG 12 (Responsible Consumption and Production) through sustainable and efficient industrial practices. While not directly health-focused, the research aligns with Uganda’s National Development Plan IV aspirations for industrialization, digital transformation, and trade facilitation. By promoting secure, data-driven supply chains, the study fosters economic growth, competitiveness, and resilience in national and regional markets.
dc.description.abstractIn the surroundings of mobile security systems, traditional biometric methods such as fingerprint and facial recognition can be readily disputed and misled. These systems change user appearance by failing in different illumination conditions or spoofing attacks with false fingerprints or images. Overcoming these constraints, the proposed multi-modal biometric identification system integrates gait with facial recognition. Combining facial data with gait traits observed by accelerometers and gyroscopes helps to increase security and reduce false positives and negatives. Results show the system achieves 97.8% accuracy, significantly above the existing systems using a low false acceptance rate (significantly) of 1.5% and a false rejection rate (FRR) of 0.7%. It has a superior resilience to spoofing techniques, including stride imitation and image/video spoofing; it also shines in tough conditions, including poor lighting and appearance alterations. Despite a slight increase in processing time, the method offers a more robust and reliable solution for mobile authentication.
dc.identifier.citationNivedita, V., Joseph, J. A., Varghese, D. T., Nithyanandh, S., Sundaram, J., & Ali, G. (2025). Multi-modal biometric authentication integrating gait and face recognition for mobile security. 2025 International Conference on Smart & Sustainable Technology (INCSST), 1–6. https://doi.org/10.1109/INCSST64791.2025.11210361
dc.identifier.urihttps://dir.muni.ac.ug/handle/20.500.12260/849
dc.language.isoen
dc.publisherIEEE
dc.subjectPerformance evaluation
dc.subjectAccuracy
dc.subjectFace recognition
dc.subjectAuthentication
dc.subjectLighting
dc.subjectMobile security
dc.subjectReal-time systems
dc.subjectSecurity
dc.subjectGait recognition
dc.subjectResilience
dc.titleMulti-modal biometric authentication integrating gait and face recognition for mobile security
dc.typeOther

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