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
Linking scales and disciplines: an interdisciplinary cross-scale approach to supporting climate-relevant ecosystem management
- Berger, Christian, Bieri, Mari, Bradshaw, Karen L, Brümmer, Christian, Clemen, Thomas, Hickler, Thomas, Kutsch, Werner Leo, Lenfers, Ulfia A, Martens, Carola, Midgley, Guy F, Mukwashi, Kanisios, Odipo, Victor, Scheiter, Simon, Schmullius, Christiane, Baade, Jussi, du Toit, Justin C, Scholes, Robert J, Smit, Izak P, Stevens, Nicola, Twine, Wayne
- Authors: Berger, Christian , Bieri, Mari , Bradshaw, Karen L , Brümmer, Christian , Clemen, Thomas , Hickler, Thomas , Kutsch, Werner Leo , Lenfers, Ulfia A , Martens, Carola , Midgley, Guy F , Mukwashi, Kanisios , Odipo, Victor , Scheiter, Simon , Schmullius, Christiane , Baade, Jussi , du Toit, Justin C , Scholes, Robert J , Smit, Izak P , Stevens, Nicola , Twine, Wayne
- Date: 2019
- Subjects: To be catalogued
- Language: English
- Type: text , article
- Identifier: http://hdl.handle.net/10962/460589 , vital:75967 , xlink:href="https://doi.org/10.1007/s10584-019-02544-0"
- Description: Southern Africa is particularly sensitive to climate change, due to both ecological and socio-economic factors, with rural land users among the most vulnerable groups. The provision of information to support climate-relevant decision-making requires an understanding of the projected impacts of change and complex feedbacks within the local ecosystems, as well as local demands on ecosystem services. In this paper, we address the limitation of current approaches for developing management relevant socio-ecological information on the projected impacts of climate change and human activities. We emphasise the need for linking disciplines and approaches by expounding the methodology followed in our two consecutive projects. These projects combine disciplines and levels of measurements from the leaf level (ecophysiology) to the local landscape level (flux measurements) and from the local household level (socio-economic surveys) to the regional level (remote sensing), feeding into a variety of models at multiple scales. Interdisciplinary, multi-scaled, and integrated socio-ecological approaches, as proposed here, are needed to compliment reductionist and linear, scale-specific approaches. Decision support systems are used to integrate and communicate the data and models to the local decision-makers.
- Full Text:
- Date Issued: 2019
- Authors: Berger, Christian , Bieri, Mari , Bradshaw, Karen L , Brümmer, Christian , Clemen, Thomas , Hickler, Thomas , Kutsch, Werner Leo , Lenfers, Ulfia A , Martens, Carola , Midgley, Guy F , Mukwashi, Kanisios , Odipo, Victor , Scheiter, Simon , Schmullius, Christiane , Baade, Jussi , du Toit, Justin C , Scholes, Robert J , Smit, Izak P , Stevens, Nicola , Twine, Wayne
- Date: 2019
- Subjects: To be catalogued
- Language: English
- Type: text , article
- Identifier: http://hdl.handle.net/10962/460589 , vital:75967 , xlink:href="https://doi.org/10.1007/s10584-019-02544-0"
- Description: Southern Africa is particularly sensitive to climate change, due to both ecological and socio-economic factors, with rural land users among the most vulnerable groups. The provision of information to support climate-relevant decision-making requires an understanding of the projected impacts of change and complex feedbacks within the local ecosystems, as well as local demands on ecosystem services. In this paper, we address the limitation of current approaches for developing management relevant socio-ecological information on the projected impacts of climate change and human activities. We emphasise the need for linking disciplines and approaches by expounding the methodology followed in our two consecutive projects. These projects combine disciplines and levels of measurements from the leaf level (ecophysiology) to the local landscape level (flux measurements) and from the local household level (socio-economic surveys) to the regional level (remote sensing), feeding into a variety of models at multiple scales. Interdisciplinary, multi-scaled, and integrated socio-ecological approaches, as proposed here, are needed to compliment reductionist and linear, scale-specific approaches. Decision support systems are used to integrate and communicate the data and models to the local decision-makers.
- Full Text:
- Date Issued: 2019
An investigation of face and fingerprint feature-fusion guidelines
- 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/10962/473751 , vital:77678 , xlink:href="https://doi.org/10.1007/978-3-319-34099-9_45"
- Description: There are a lack of multi-modal biometric fusion guidelines at the feature-level. This paper investigates face and fingerprint features in the form of their strengths and weaknesses. This serves as a set of guidelines to authors that are planning face and fingerprint feature-fusion applications or aim to extend this into a general framework. The proposed guidelines were applied to the face and fingerprint to achieve a 91.11 % recognition accuracy when using only a single training sample. Furthermore, an accuracy of 99.69 % was achieved when using five training samples.
- 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/10962/473751 , vital:77678 , xlink:href="https://doi.org/10.1007/978-3-319-34099-9_45"
- Description: There are a lack of multi-modal biometric fusion guidelines at the feature-level. This paper investigates face and fingerprint features in the form of their strengths and weaknesses. This serves as a set of guidelines to authors that are planning face and fingerprint feature-fusion applications or aim to extend this into a general framework. The proposed guidelines were applied to the face and fingerprint to achieve a 91.11 % recognition accuracy when using only a single training sample. Furthermore, an accuracy of 99.69 % was achieved when using five training samples.
- Full Text:
- Date Issued: 2016
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