IMPLEMENTATION OF A BIMODAL BIOMETRIC ACCESS CONTROL SYSTEM FOR DATA CENTER

The use of biometrics has become one of the only sure ways to provide secure access control to rooms where vital asset are stored, such as data centers where valuable information are stored. This paper aim at designing and implementing a bimodal biometric access control system for data center using fingerprint and Iris trait of the same person, it is called bimodal biometric system. The system was implemented by integrating hardware components such as PIC18F452 microcontroller, fingerprint and iris sensors and so no with the software programs as such C language and MYSQL interface. On testing, it is found to improve the security and reliability in the access control systems management of the data cente

IMPLEMENTATION OF A BIMODAL BIOMETRIC ACCESS CONTROL SYSTEM FOR DATA CENTER

The use of biometrics has become one of the only sure ways to provide secure access control to rooms where vital asset are stored, such as data centers where valuable information are stored. This paper aim at designing and implementing a bimodal biometric access control system for data center using fingerprint and Iris trait of the same person, it is called bimodal biometric system. The system was implemented by integrating hardware components such as PIC18F452 microcontroller, fingerprint and iris sensors and so no with the software programs as such C language and MYSQL interface. On testing, it is found to improve the security and reliability in the access control systems management of the data center

Mejora de la seguridad y la privacidad de los sistemas biométricos

Tesis doctoral inédita leída en la Universidad Autónoma de Madrid, Escuela Politécnica Superior, Departamento de Tecnología Electrónica y de las Comunicaciones. Fecha de lectura: 02-06-2016This Thesis was printed with the financial support from EPS-UAM and the Biometric Recognition Group-ATVS

Seamless Multimodal Biometrics for Continuous Personalised Wellbeing Monitoring

Artificially intelligent perception is increasingly present in the lives of every one of us. Vehicles are no exception, (...) In the near future, pattern recognition will have an even stronger role in vehicles, as self-driving cars will require automated ways to understand what is happening around (and within) them and act accordingly. (...) This doctoral work focused on advancing in-vehicle sensing through the research of novel computer vision and pattern recognition methodologies for both biometrics and wellbeing monitoring. The main focus has been on electrocardiogram (ECG) biometrics, a trait well-known for its potential for seamless driver monitoring. Major efforts were devoted to achieving improved performance in identification and identity verification in off-the-person scenarios, well-known for increased noise and variability. Here, end-to-end deep learning ECG biometric solutions were proposed and important topics were addressed such as cross-database and long-term performance, waveform relevance through explainability, and interlead conversion. Face biometrics, a natural complement to the ECG in seamless unconstrained scenarios, was also studied in this work. The open challenges of masked face recognition and interpretability in biometrics were tackled in an effort to evolve towards algorithms that are more transparent, trustworthy, and robust to significant occlusions. Within the topic of wellbeing monitoring, improved solutions to multimodal emotion recognition in groups of people and activity/violence recognition in in-vehicle scenarios were proposed. At last, we also proposed a novel way to learn template security within end-to-end models, dismissing additional separate encryption processes, and a self-supervised learning approach tailored to sequential data, in order to ensure data security and optimal performance. (...)Comment: Doctoral thesis presented and approved on the 21st of December 2022 to the University of Port

Handbook of Vascular Biometrics

This open access handbook provides the first comprehensive overview of biometrics exploiting the shape of human blood vessels for biometric recognition, i.e. vascular biometrics, including finger vein recognition, hand/palm vein recognition, retina recognition, and sclera recognition. After an introductory chapter summarizing the state of the art in and availability of commercial systems and open datasets/open source software, individual chapters focus on specific aspects of one of the biometric modalities, including questions of usability, security, and privacy. The book features contributions from both academia and major industrial manufacturers