Deep Palmprint Recognition with Alignment and Augmentation of Limited Training Samples
- Brown, Dane L, Bradshaw, Karen L
- Authors: Brown, Dane L , Bradshaw, Karen L
- Date: 2022
- Subjects: To be catalogued
- Language: English
- Type: text , article
- Identifier: http://hdl.handle.net/10962/440249 , vital:73760 , xlink:href="https://doi.org/10.1007/s42979-021-00859-3"
- Description: This paper builds upon a previously proposed automatic palmprint alignment and classification system. The proposed system was geared towards palmprints acquired from either contact or contactless sensors. It was robust to finger location and fist shape changes—accurately extracting the palmprints in images without fingers. An extension to this previous work includes comparisons of traditional and deep learning models, both with hyperparameter tuning. The proposed methods are compared with related verification systems and a detailed evaluation of open-set identification. The best results were yielded by a proposed Convolutional Neural Network, based on VGG-16, and outperforming tuned VGG-16 and Xception architectures. All deep learning algorithms are provided with augmented data, included in the tuning process, enabling significant accuracy gains. Highlights include near-zero and zero EER on IITD-Palmprint verification using one training sample and leave-one-out strategy, respectively. Therefore, the proposed palmprint system is practical as it is effective on data containing many and few training examples.
- Full Text:
- Date Issued: 2022
- Authors: Brown, Dane L , Bradshaw, Karen L
- Date: 2022
- Subjects: To be catalogued
- Language: English
- Type: text , article
- Identifier: http://hdl.handle.net/10962/440249 , vital:73760 , xlink:href="https://doi.org/10.1007/s42979-021-00859-3"
- Description: This paper builds upon a previously proposed automatic palmprint alignment and classification system. The proposed system was geared towards palmprints acquired from either contact or contactless sensors. It was robust to finger location and fist shape changes—accurately extracting the palmprints in images without fingers. An extension to this previous work includes comparisons of traditional and deep learning models, both with hyperparameter tuning. The proposed methods are compared with related verification systems and a detailed evaluation of open-set identification. The best results were yielded by a proposed Convolutional Neural Network, based on VGG-16, and outperforming tuned VGG-16 and Xception architectures. All deep learning algorithms are provided with augmented data, included in the tuning process, enabling significant accuracy gains. Highlights include near-zero and zero EER on IITD-Palmprint verification using one training sample and leave-one-out strategy, respectively. Therefore, the proposed palmprint system is practical as it is effective on data containing many and few training examples.
- Full Text:
- Date Issued: 2022
Deep palmprint recognition with alignment and augmentation of limited training samples
- Brown, Dane L, Bradshaw, Karen L
- Authors: Brown, Dane L , Bradshaw, Karen L
- Date: 2022
- Subjects: To be catalogued
- Language: English
- Type: text , article
- Identifier: http://hdl.handle.net/10962/464074 , vital:76473 , xlink:href="https://doi.org/10.1007/s42979-021-00859-3"
- Description: This paper builds upon a previously proposed automatic palmprint alignment and classification system. The proposed system was geared towards palmprints acquired from either contact or contactless sensors. It was robust to finger location and fist shape changes—accurately extracting the palmprints in images without fingers. An extension to this previous work includes comparisons of traditional and deep learning models, both with hyperparameter tuning. The proposed methods are compared with related verification systems and a detailed evaluation of open-set identification. The best results were yielded by a proposed Convolutional Neural Network, based on VGG-16, and outperforming tuned VGG-16 and Xception architectures. All deep learning algorithms are provided with augmented data, included in the tuning process, enabling significant accuracy gains. Highlights include near-zero and zero EER on IITD-Palmprint verification using one training sample and leave-one-out strategy, respectively. Therefore, the proposed palmprint system is practical as it is effective on data containing many and few training examples.
- Full Text:
- Date Issued: 2022
- Authors: Brown, Dane L , Bradshaw, Karen L
- Date: 2022
- Subjects: To be catalogued
- Language: English
- Type: text , article
- Identifier: http://hdl.handle.net/10962/464074 , vital:76473 , xlink:href="https://doi.org/10.1007/s42979-021-00859-3"
- Description: This paper builds upon a previously proposed automatic palmprint alignment and classification system. The proposed system was geared towards palmprints acquired from either contact or contactless sensors. It was robust to finger location and fist shape changes—accurately extracting the palmprints in images without fingers. An extension to this previous work includes comparisons of traditional and deep learning models, both with hyperparameter tuning. The proposed methods are compared with related verification systems and a detailed evaluation of open-set identification. The best results were yielded by a proposed Convolutional Neural Network, based on VGG-16, and outperforming tuned VGG-16 and Xception architectures. All deep learning algorithms are provided with augmented data, included in the tuning process, enabling significant accuracy gains. Highlights include near-zero and zero EER on IITD-Palmprint verification using one training sample and leave-one-out strategy, respectively. Therefore, the proposed palmprint system is practical as it is effective on data containing many and few training examples.
- Full Text:
- Date Issued: 2022
Drowning in data, thirsty for information and starved for understanding: A biodiversity information hub for cooperative environmental monitoring in South Africa
- MacFadyen, Sandra, Allsopp, Nicky, Altwegg, Res, Archibald, Sally, Botha, Judith, Bradshaw, Karen L, Carruthers, Jane, De Klerk, Helen, de Vos, Alta, Distiller, Greg, Foord, Stefan, Freitag-Ronaldson, Stefanie, Gibbs, Richard, Hamer, Michelle, Landi, Pietro, MacFayden, Duncan, Manuel, Jeffrey, Midgley, Guy, Moncrieff, Glenn, Munch, Zahn, Mutanga, Onisimo, Sershen, Nenguda, Rendani, Ngwenya, Mzabalazo, Parker, Daniel M, Peel, Mike, Power, John, Pretorius, Joachim, Ramdhani, Syd, Robertson, Mark P, Rushworth, Ian, Skowno, Andrew, Slingsby, Jasper, Turner, Andrew, Visser, Vernon, van Wageningen, Gerhard, Hui, Cang
- Authors: MacFadyen, Sandra , Allsopp, Nicky , Altwegg, Res , Archibald, Sally , Botha, Judith , Bradshaw, Karen L , Carruthers, Jane , De Klerk, Helen , de Vos, Alta , Distiller, Greg , Foord, Stefan , Freitag-Ronaldson, Stefanie , Gibbs, Richard , Hamer, Michelle , Landi, Pietro , MacFayden, Duncan , Manuel, Jeffrey , Midgley, Guy , Moncrieff, Glenn , Munch, Zahn , Mutanga, Onisimo , Sershen , Nenguda, Rendani , Ngwenya, Mzabalazo , Parker, Daniel M , Peel, Mike , Power, John , Pretorius, Joachim , Ramdhani, Syd , Robertson, Mark P , Rushworth, Ian , Skowno, Andrew , Slingsby, Jasper , Turner, Andrew , Visser, Vernon , van Wageningen, Gerhard , Hui, Cang
- Date: 2022
- Subjects: To be catalogued
- Language: English
- Type: text , article
- Identifier: http://hdl.handle.net/10962/415624 , vital:71271 , xlink:href="https://doi.org/10.1016/j.biocon.2022.109736"
- Description: The world is firmly cemented in a notitian age (Latin: notitia, meaning data) – drowning in data, yet thirsty for information and the synthesis of knowledge into understanding. As concerns over biodiversity declines escalate, the volume, diversity and speed at which new environmental and ecological data are generated has increased exponentially. Data availability primes the research and discovery engine driving biodiversity conservation. South Africa (SA) is poised to become a world leader in biodiversity conservation. However, continent-wide resource limitations hamper the establishment of inclusive technologies and robust platforms and tools for biodiversity informatics. In this perspectives piece, we bring together the opinions of 37 co-authors from 20 different departments, across 10 SA universities, 7 national and provincial conservation research agencies, and various institutes and private conservation, research and management bodies, to develop a way forward for biodiversity informatics in SA. We propose the development of a SA Biodiversity Informatics Hub and describe the essential components necessary for its design, implementation and sustainability. We emphasise the importance of developing a culture of cooperation, collaboration and interoperability among custodians of biodiversity data to establish operational workflows for data synthesis. However, our biggest challenges are misgivings around data sharing and multidisciplinary collaboration.
- Full Text:
- Date Issued: 2022
- Authors: MacFadyen, Sandra , Allsopp, Nicky , Altwegg, Res , Archibald, Sally , Botha, Judith , Bradshaw, Karen L , Carruthers, Jane , De Klerk, Helen , de Vos, Alta , Distiller, Greg , Foord, Stefan , Freitag-Ronaldson, Stefanie , Gibbs, Richard , Hamer, Michelle , Landi, Pietro , MacFayden, Duncan , Manuel, Jeffrey , Midgley, Guy , Moncrieff, Glenn , Munch, Zahn , Mutanga, Onisimo , Sershen , Nenguda, Rendani , Ngwenya, Mzabalazo , Parker, Daniel M , Peel, Mike , Power, John , Pretorius, Joachim , Ramdhani, Syd , Robertson, Mark P , Rushworth, Ian , Skowno, Andrew , Slingsby, Jasper , Turner, Andrew , Visser, Vernon , van Wageningen, Gerhard , Hui, Cang
- Date: 2022
- Subjects: To be catalogued
- Language: English
- Type: text , article
- Identifier: http://hdl.handle.net/10962/415624 , vital:71271 , xlink:href="https://doi.org/10.1016/j.biocon.2022.109736"
- Description: The world is firmly cemented in a notitian age (Latin: notitia, meaning data) – drowning in data, yet thirsty for information and the synthesis of knowledge into understanding. As concerns over biodiversity declines escalate, the volume, diversity and speed at which new environmental and ecological data are generated has increased exponentially. Data availability primes the research and discovery engine driving biodiversity conservation. South Africa (SA) is poised to become a world leader in biodiversity conservation. However, continent-wide resource limitations hamper the establishment of inclusive technologies and robust platforms and tools for biodiversity informatics. In this perspectives piece, we bring together the opinions of 37 co-authors from 20 different departments, across 10 SA universities, 7 national and provincial conservation research agencies, and various institutes and private conservation, research and management bodies, to develop a way forward for biodiversity informatics in SA. We propose the development of a SA Biodiversity Informatics Hub and describe the essential components necessary for its design, implementation and sustainability. We emphasise the importance of developing a culture of cooperation, collaboration and interoperability among custodians of biodiversity data to establish operational workflows for data synthesis. However, our biggest challenges are misgivings around data sharing and multidisciplinary collaboration.
- Full Text:
- Date Issued: 2022
Review of Importance of Weather and Environmental Variables in Agent-Based Arbovirus Models
- Pascoe, Luba, Clemen, Thomas, Bradshaw, Karen L, Nyambo, Devotha G
- Authors: Pascoe, Luba , Clemen, Thomas , Bradshaw, Karen L , Nyambo, Devotha G
- Date: 2022
- Subjects: To be catalogued
- Language: English
- Type: text , article
- Identifier: http://hdl.handle.net/10962/440300 , vital:73764 , xlink:href="https://doi.org/10.3390/ijerph192315578"
- Description: The study sought to review the works of literature on agent-based modeling and the influence of climatic and environmental factors on disease outbreak, transmission, and surveillance. Thus, drawing the influence of environmental variables such as vegetation index, households, mosquito habitats, breeding sites, and climatic variables including precipitation or rainfall, temperature, wind speed, and relative humidity on dengue disease modeling using the agent-based model in an African context and globally was the aim of the study. A search strategy was developed and used to search for relevant articles from four databases, namely, PubMed, Scopus, Research4Life, and Google Scholar. Inclusion criteria were developed, and 20 articles met the criteria and have been included in the review. From the reviewed works of literature, the study observed that climatic and environmental factors may influence the arbovirus disease outbreak, transmission, and surveillance. Thus, there is a call for further research on the area. To benefit from arbovirus modeling, it is crucial to consider the influence of climatic and environmental factors, especially in Africa, where there are limited studies exploring this phenomenon.
- Full Text:
- Date Issued: 2022
- Authors: Pascoe, Luba , Clemen, Thomas , Bradshaw, Karen L , Nyambo, Devotha G
- Date: 2022
- Subjects: To be catalogued
- Language: English
- Type: text , article
- Identifier: http://hdl.handle.net/10962/440300 , vital:73764 , xlink:href="https://doi.org/10.3390/ijerph192315578"
- Description: The study sought to review the works of literature on agent-based modeling and the influence of climatic and environmental factors on disease outbreak, transmission, and surveillance. Thus, drawing the influence of environmental variables such as vegetation index, households, mosquito habitats, breeding sites, and climatic variables including precipitation or rainfall, temperature, wind speed, and relative humidity on dengue disease modeling using the agent-based model in an African context and globally was the aim of the study. A search strategy was developed and used to search for relevant articles from four databases, namely, PubMed, Scopus, Research4Life, and Google Scholar. Inclusion criteria were developed, and 20 articles met the criteria and have been included in the review. From the reviewed works of literature, the study observed that climatic and environmental factors may influence the arbovirus disease outbreak, transmission, and surveillance. Thus, there is a call for further research on the area. To benefit from arbovirus modeling, it is crucial to consider the influence of climatic and environmental factors, especially in Africa, where there are limited studies exploring this phenomenon.
- Full Text:
- Date Issued: 2022
Shrub Detection in High-Resolution Imagery: A Comparative Study of Two Deep Learning Approaches
- James, Katherine M F, Bradshaw, Karen L
- Authors: James, Katherine M F , Bradshaw, Karen L
- Date: 2022
- Subjects: To be catalogued
- Language: English
- Type: text , book
- Identifier: http://hdl.handle.net/10962/440326 , vital:73766 , ISBN 9783030955021 , https://doi.org/10.1007/978-3-030-95502-1_41
- Description: A common task in high-resolution remotely-sensed aerial imagery is the detection of particular target plant species for various ecological and agricultural applications. Although traditionally object-based image analysis approaches have been the most popular method for this task, deep learning approaches such as image patch-based convolutional neural networks (CNNs) have been seen to outperform these older approaches. To a lesser extent, fully convolutional networks (FCNs) that allow for semantic segmentation of images, have also begun to be used in the broader literature. This study investigates patch-based CNNs and FCN-based segmentation for shrub detection, targeting a particular invasive shrub genus. The results show that while a patch-based CNN demonstrates strong performance on ideal image patches, the FCN outperforms this approach on real-world proposed image patches with a 52% higher object-level precision and comparable recall. This indicates that FCN-based segmentation approaches are a promising alternative to patch-based approaches, with the added advantage of not requiring any hand-tuning of a patch proposal algorithm.
- Full Text:
- Date Issued: 2022
- Authors: James, Katherine M F , Bradshaw, Karen L
- Date: 2022
- Subjects: To be catalogued
- Language: English
- Type: text , book
- Identifier: http://hdl.handle.net/10962/440326 , vital:73766 , ISBN 9783030955021 , https://doi.org/10.1007/978-3-030-95502-1_41
- Description: A common task in high-resolution remotely-sensed aerial imagery is the detection of particular target plant species for various ecological and agricultural applications. Although traditionally object-based image analysis approaches have been the most popular method for this task, deep learning approaches such as image patch-based convolutional neural networks (CNNs) have been seen to outperform these older approaches. To a lesser extent, fully convolutional networks (FCNs) that allow for semantic segmentation of images, have also begun to be used in the broader literature. This study investigates patch-based CNNs and FCN-based segmentation for shrub detection, targeting a particular invasive shrub genus. The results show that while a patch-based CNN demonstrates strong performance on ideal image patches, the FCN outperforms this approach on real-world proposed image patches with a 52% higher object-level precision and comparable recall. This indicates that FCN-based segmentation approaches are a promising alternative to patch-based approaches, with the added advantage of not requiring any hand-tuning of a patch proposal algorithm.
- Full Text:
- Date Issued: 2022
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