Efficient Biometric Access Control for Larger Scale Populations
- Brown, Dane L, Bradshaw, Karen L
- Authors: Brown, Dane L , Bradshaw, Karen L
- Date: 2018
- Subjects: To be catalogued
- Language: English
- Type: text , article
- Identifier: http://hdl.handle.net/10962/465667 , vital:76630 , xlink:href="https://www.researchgate.net/profile/Dane-Brown-2/publication/335378829_Efficient_Biometric_Access_Control_for_Larger_Scale_Populations/links/5d61159ea6fdccc32ccd2c8a/Efficient-Biometric-Access-Control-for-Larger-Scale-Populations.pdf"
- Description: Biometric applications and databases are growing at an alarming rate. Processing large or complex biometric data induces longer wait times that can limit usability during application. This paper focuses on increasing the processing speed of biometric data, and calls for a parallel approach to data processing that is beyond the capability of a central processing unit (CPU). The graphical processing unit (GPU) is effectively utilized with compute unified device architecture (CUDA), and results in at least triple the processing speed when compared with a previously presented accurate and secure multimodal biometric system. When saturating the CPU-only implementation with more individuals than the available thread count, the GPU-assisted implementation outperforms it exponentially. The GPU-assisted implementation is also validated to have the same accuracy of the original system, and thus shows promising advancements in both accuracy and processing speed in the challenging big data world.
- Full Text:
- Date Issued: 2018
- Authors: Brown, Dane L , Bradshaw, Karen L
- Date: 2018
- Subjects: To be catalogued
- Language: English
- Type: text , article
- Identifier: http://hdl.handle.net/10962/465667 , vital:76630 , xlink:href="https://www.researchgate.net/profile/Dane-Brown-2/publication/335378829_Efficient_Biometric_Access_Control_for_Larger_Scale_Populations/links/5d61159ea6fdccc32ccd2c8a/Efficient-Biometric-Access-Control-for-Larger-Scale-Populations.pdf"
- Description: Biometric applications and databases are growing at an alarming rate. Processing large or complex biometric data induces longer wait times that can limit usability during application. This paper focuses on increasing the processing speed of biometric data, and calls for a parallel approach to data processing that is beyond the capability of a central processing unit (CPU). The graphical processing unit (GPU) is effectively utilized with compute unified device architecture (CUDA), and results in at least triple the processing speed when compared with a previously presented accurate and secure multimodal biometric system. When saturating the CPU-only implementation with more individuals than the available thread count, the GPU-assisted implementation outperforms it exponentially. The GPU-assisted implementation is also validated to have the same accuracy of the original system, and thus shows promising advancements in both accuracy and processing speed in the challenging big data world.
- Full Text:
- Date Issued: 2018
Enhanced biometric access control for mobile devices
- Brown, Dane L, Bradshaw, Karen L
- Authors: Brown, Dane L , Bradshaw, Karen L
- Date: 2017
- Subjects: To be catalogued
- Language: English
- Type: text , article
- Identifier: http://hdl.handle.net/10962/465678 , vital:76631
- Description: In the new Digital Economy, mobile devices are increasingly 978-0-620-76756-9being used for tasks that involve sensitive and/or financial data. Hitherto, security on smartphones has not been a priority and furthermore, users tend to ignore the security features in favour of more rapid access to the device. We propose an authentication system that can provide enhanced security by utilizing multi-modal biometrics from a single image, captured at arm’s length, containing unique face and iris data. The system is compared to state-of-the-art face and iris recognition systems, in related studies using the CASIA-Iris-Distance dataset and the IITD iris dataset. The proposed system outperforms the related studies in all experiments and shows promising advancements to at-a-distance iris recognition on mobile devices.
- Full Text:
- Date Issued: 2017
- Authors: Brown, Dane L , Bradshaw, Karen L
- Date: 2017
- Subjects: To be catalogued
- Language: English
- Type: text , article
- Identifier: http://hdl.handle.net/10962/465678 , vital:76631
- Description: In the new Digital Economy, mobile devices are increasingly 978-0-620-76756-9being used for tasks that involve sensitive and/or financial data. Hitherto, security on smartphones has not been a priority and furthermore, users tend to ignore the security features in favour of more rapid access to the device. We propose an authentication system that can provide enhanced security by utilizing multi-modal biometrics from a single image, captured at arm’s length, containing unique face and iris data. The system is compared to state-of-the-art face and iris recognition systems, in related studies using the CASIA-Iris-Distance dataset and the IITD iris dataset. The proposed system outperforms the related studies in all experiments and shows promising advancements to at-a-distance iris recognition on mobile devices.
- Full Text:
- Date Issued: 2017
A dynamically weighted multi-modal biometric security system
- Brown, Dane L, Bradshaw, Karen L
- Authors: Brown, Dane L , Bradshaw, Karen L
- Date: 2016
- Subjects: To be catalogued
- Language: English
- Type: text , article
- Identifier: http://hdl.handle.net/10962/473684 , vital:77672 , xlink:href="https://www.researchgate.net/publication/315839228_A_Dynamically_Weighted_Multi-Modal_Biometric_Security_System"
- Description: The face, fingerprint and palmprint feature vectors are automatically extracted and dynamically selected for fusion at the feature-level, toward an improved human identification accuracy. The feature-level has a higher potential accuracy than the match score-level. However, leveraging this potential requires a new approach. This work demonstrates a novel dynamic weighting algorithm for improved image-based biometric feature-fusion. A comparison is performed on uni-modal, bi-modal, tri-modal and proposed dynamic approaches. The proposed dynamic approach yields a high genuine acceptance rate of 99.25% genuine acceptance rate at a false acceptance rate of 1% on challenging datasets and big impostor datasets.
- Full Text:
- Date Issued: 2016
- Authors: Brown, Dane L , Bradshaw, Karen L
- Date: 2016
- Subjects: To be catalogued
- Language: English
- Type: text , article
- Identifier: http://hdl.handle.net/10962/473684 , vital:77672 , xlink:href="https://www.researchgate.net/publication/315839228_A_Dynamically_Weighted_Multi-Modal_Biometric_Security_System"
- Description: The face, fingerprint and palmprint feature vectors are automatically extracted and dynamically selected for fusion at the feature-level, toward an improved human identification accuracy. The feature-level has a higher potential accuracy than the match score-level. However, leveraging this potential requires a new approach. This work demonstrates a novel dynamic weighting algorithm for improved image-based biometric feature-fusion. A comparison is performed on uni-modal, bi-modal, tri-modal and proposed dynamic approaches. The proposed dynamic approach yields a high genuine acceptance rate of 99.25% genuine acceptance rate at a false acceptance rate of 1% on challenging datasets and big impostor datasets.
- Full Text:
- Date Issued: 2016
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