Design of a Message Passing Model for Use in a Heterogeneous CPU-NFP Framework for Network Analytics. Southern Africa Telecommunication Networks and Applications Conference (SATNAC) 2017, 3-10 September 2017
- Pennefather, Sean, Bradshaw, Karen L, Irwin, Barry V W
- Authors: Pennefather, Sean , Bradshaw, Karen L , Irwin, Barry V W
- Date: 2017
- Subjects: To be catalogued
- Language: English
- Type: text , book
- Identifier: http://hdl.handle.net/10962/460011 , vital:75884 , ISBN 9780620767569 , http://dx.doi.org/10.18489/sacj.v31i2.692
- Description: Currently, network analytics requires direct access to network packets, normally through a third-party application, which means that obtaining realtime results is difficult. We propose the NFP-CPU heterogeneous framework to allow parts of applications written in the Go programming language to be executed on a Network Flow Processor (NFP) for enhanced performance. This paper explores the need and feasibility of implementing a message passing model for data transmission between the NFP and CPU, which is the crux of such a heterogeneous framework. Architectural differences between the two domains are highlighted within this context and we present a solution to bridging these differences.
- Full Text:
- Date Issued: 2017
- Authors: Pennefather, Sean , Bradshaw, Karen L , Irwin, Barry V W
- Date: 2017
- Subjects: To be catalogued
- Language: English
- Type: text , book
- Identifier: http://hdl.handle.net/10962/460011 , vital:75884 , ISBN 9780620767569 , http://dx.doi.org/10.18489/sacj.v31i2.692
- Description: Currently, network analytics requires direct access to network packets, normally through a third-party application, which means that obtaining realtime results is difficult. We propose the NFP-CPU heterogeneous framework to allow parts of applications written in the Go programming language to be executed on a Network Flow Processor (NFP) for enhanced performance. This paper explores the need and feasibility of implementing a message passing model for data transmission between the NFP and CPU, which is the crux of such a heterogeneous framework. Architectural differences between the two domains are highlighted within this context and we present a solution to bridging these differences.
- Full Text:
- Date Issued: 2017
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
Feature-fusion guidelines for image-based multi-modal biometric fusion
- 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/10962/460063 , vital:75889 , xlink:href="https://doi.org/10.18489/sacj.v29i1.436"
- Description: The feature level, unlike the match score level, lacks multi-modal fusion guidelines. This work demonstrates a newapproach for improved image-based biometric feature-fusion. The approach extracts and combines the face, fingerprintand palmprint at the feature level for improved human identification accuracy. Feature-fusion guidelines, proposed inour recent work, are extended by adding a new face segmentation method and the support vector machine classifier.The new face segmentation method improves the face identification equal error rate (EER) by 10%. The support vectormachine classifier combined with the new feature selection approach, proposed in our recent work, outperforms otherclassifiers when using a single training sample. Feature-fusion guidelines take the form of strengths and weaknessesas observed in the applied feature processing modules during preliminary experiments. The guidelines are used toimplement an effective biometric fusion system at the feature level, using a novel feature-fusion methodology, reducingthe EER of two groups of three datasets namely: SDUMLA face, SDUMLA fingerprint and IITD palmprint; MUCT Face,MCYT Fingerprint and CASIA Palmprint.
- 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/10962/460063 , vital:75889 , xlink:href="https://doi.org/10.18489/sacj.v29i1.436"
- Description: The feature level, unlike the match score level, lacks multi-modal fusion guidelines. This work demonstrates a newapproach for improved image-based biometric feature-fusion. The approach extracts and combines the face, fingerprintand palmprint at the feature level for improved human identification accuracy. Feature-fusion guidelines, proposed inour recent work, are extended by adding a new face segmentation method and the support vector machine classifier.The new face segmentation method improves the face identification equal error rate (EER) by 10%. The support vectormachine classifier combined with the new feature selection approach, proposed in our recent work, outperforms otherclassifiers when using a single training sample. Feature-fusion guidelines take the form of strengths and weaknessesas observed in the applied feature processing modules during preliminary experiments. The guidelines are used toimplement an effective biometric fusion system at the feature level, using a novel feature-fusion methodology, reducingthe EER of two groups of three datasets namely: SDUMLA face, SDUMLA fingerprint and IITD palmprint; MUCT Face,MCYT Fingerprint and CASIA Palmprint.
- Full Text:
- Date Issued: 2017
Feature-fusion guidelines for image-based multi-modal biometric fusion
- 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/10962/465689 , vital:76632 , xlink:href="https://hdl.handle.net/10520/EJC-90afb1388"
- Description: The feature level, unlike the match score level, lacks multi-modal fusion guidelines. This work demonstrates a new approach for improved image-based biometric feature-fusion. The approach extracts and combines the face, fingerprint and palmprint at the feature level for improved human identification accuracy. Feature-fusion guidelines, proposed in our recent work, are extended by adding a new face segmentation method and the support vector machine classifier. The new face segmentation method improves the face identification equal error rate (EER) by 10%. The support vector machine classifier combined with the new feature selection approach, proposed in our recent work, outperforms other classifiers when using a single training sample. Feature-fusion guidelines take the form of strengths and weaknesses as observed in the applied feature processing modules during preliminary experiments. The guidelines are used to implement an effective biometric fusion system at the feature level, using a novel feature-fusion methodology, reducing the EER of two groups of three datasets namely: SDUMLA face, SDUMLA fingerprint and IITD palmprint; MUCT Face, MCYT Fingerprint and CASIA Palmprint.
- 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/10962/465689 , vital:76632 , xlink:href="https://hdl.handle.net/10520/EJC-90afb1388"
- Description: The feature level, unlike the match score level, lacks multi-modal fusion guidelines. This work demonstrates a new approach for improved image-based biometric feature-fusion. The approach extracts and combines the face, fingerprint and palmprint at the feature level for improved human identification accuracy. Feature-fusion guidelines, proposed in our recent work, are extended by adding a new face segmentation method and the support vector machine classifier. The new face segmentation method improves the face identification equal error rate (EER) by 10%. The support vector machine classifier combined with the new feature selection approach, proposed in our recent work, outperforms other classifiers when using a single training sample. Feature-fusion guidelines take the form of strengths and weaknesses as observed in the applied feature processing modules during preliminary experiments. The guidelines are used to implement an effective biometric fusion system at the feature level, using a novel feature-fusion methodology, reducing the EER of two groups of three datasets namely: SDUMLA face, SDUMLA fingerprint and IITD palmprint; MUCT Face, MCYT Fingerprint and CASIA Palmprint.
- Full Text:
- Date Issued: 2017
Improved Automatic Face Segmentation and Recognition for Applications with Limited Training Data
- Bradshaw, Karen L, Brown, Dane L
- Authors: Bradshaw, Karen L , Brown, Dane L
- Date: 2017
- Subjects: To be catalogued
- Language: English
- Type: text , book
- Identifier: http://hdl.handle.net/10962/460085 , vital:75891 , ISBN 9783319582740 , https://doi.org/10.1007/978-3-319-58274-0_33
- Description: This paper introduces varied pose angle, a new approach to improve face identification given large pose angles and limited training data. Face landmarks are extracted and used to normalize and segment the face. Our approach does not require face frontalization and achieves consistent results. Results are compared using frontal and non-frontal training images for Eigen and Fisher classification of various face pose angles. Fisher scales better with more training samples only with a high quality dataset. Our approach achieves promising results for three well-known face datasets.
- Full Text:
- Date Issued: 2017
- Authors: Bradshaw, Karen L , Brown, Dane L
- Date: 2017
- Subjects: To be catalogued
- Language: English
- Type: text , book
- Identifier: http://hdl.handle.net/10962/460085 , vital:75891 , ISBN 9783319582740 , https://doi.org/10.1007/978-3-319-58274-0_33
- Description: This paper introduces varied pose angle, a new approach to improve face identification given large pose angles and limited training data. Face landmarks are extracted and used to normalize and segment the face. Our approach does not require face frontalization and achieves consistent results. Results are compared using frontal and non-frontal training images for Eigen and Fisher classification of various face pose angles. Fisher scales better with more training samples only with a high quality dataset. Our approach achieves promising results for three well-known face datasets.
- Full Text:
- Date Issued: 2017
“Enhanced biometric access control for mobile devices,” in Proceedings of the 20th Southern Africa Telecommunication Networks and Applications Conference
- Bradshaw, Karen L, Brown, Dane L
- Authors: Bradshaw, Karen L , Brown, Dane L
- Date: 2017
- Subjects: To be catalogued
- Language: English
- Type: text , book
- Identifier: http://hdl.handle.net/10962/460025 , vital:75885 , ISBN 9780620767569
- Description: In the new Digital Economy, mobile devices are increasingly being used for tasks that involve sensitive and/or f inancial 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: Bradshaw, Karen L , Brown, Dane L
- Date: 2017
- Subjects: To be catalogued
- Language: English
- Type: text , book
- Identifier: http://hdl.handle.net/10962/460025 , vital:75885 , ISBN 9780620767569
- Description: In the new Digital Economy, mobile devices are increasingly being used for tasks that involve sensitive and/or f inancial 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
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