The Experts below are selected from a list of 2232 Experts worldwide ranked by ideXlab platform
Sourabh Vivek - One of the best experts on this subject based on the ideXlab platform.
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EaZy Learning: An Adaptive Variant of Ensemble Learning for Fingerprint Liveness Detection
2021Co-Authors: Agarwal Shivang, Chowdary C. Ravindranath, Sourabh VivekAbstract:In the field of biometrics, fingerprint recognition systems are vulnerable to presentation attacks made by artificially generated spoof fingerprints. Therefore, it is essential to perform liveness detection of a fingerprint before authenticating it. Fingerprint liveness detection mechanisms perform well under the within-dataset environment but fail miserably under cross-sensor (when tested on a fingerprint acquired by a new sensor) and cross-dataset (when trained on one dataset and tested on another) settings. To enhance the generalization abilities, robustness and the interoperability of the fingerprint spoof detectors, the learning models need to be adaptive towards the data. We propose a generic model, EaZy learning which can be considered as an adaptive midway between eager and lazy learning. We show the usefulness of this adaptivity under cross-sensor and cross-dataset environments. EaZy learning examines the properties intrinsic to the dataset while generating a pool of hypotheses. EaZy learning is similar to ensemble learning as it generates an ensemble of Base classifiers and integrates them to make a prediction. Still, it differs in the way it generates the Base classifiers. EaZy learning develops an ensemble of entirely Disjoint Base classifiers which has a beneficial influence on the diversity of the underlying ensemble. Also, it integrates the predictions made by these Base classifiers Based on their performance on the validation data. Experiments conducted on the standard high dimensional datasets LivDet 2011, LivDet 2013 and LivDet 2015 prove the efficacy of the model under cross-dataset and cross-sensor environments
Agarwal Shivang - One of the best experts on this subject based on the ideXlab platform.
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EaZy Learning: An Adaptive Variant of Ensemble Learning for Fingerprint Liveness Detection
2021Co-Authors: Agarwal Shivang, Chowdary C. Ravindranath, Sourabh VivekAbstract:In the field of biometrics, fingerprint recognition systems are vulnerable to presentation attacks made by artificially generated spoof fingerprints. Therefore, it is essential to perform liveness detection of a fingerprint before authenticating it. Fingerprint liveness detection mechanisms perform well under the within-dataset environment but fail miserably under cross-sensor (when tested on a fingerprint acquired by a new sensor) and cross-dataset (when trained on one dataset and tested on another) settings. To enhance the generalization abilities, robustness and the interoperability of the fingerprint spoof detectors, the learning models need to be adaptive towards the data. We propose a generic model, EaZy learning which can be considered as an adaptive midway between eager and lazy learning. We show the usefulness of this adaptivity under cross-sensor and cross-dataset environments. EaZy learning examines the properties intrinsic to the dataset while generating a pool of hypotheses. EaZy learning is similar to ensemble learning as it generates an ensemble of Base classifiers and integrates them to make a prediction. Still, it differs in the way it generates the Base classifiers. EaZy learning develops an ensemble of entirely Disjoint Base classifiers which has a beneficial influence on the diversity of the underlying ensemble. Also, it integrates the predictions made by these Base classifiers Based on their performance on the validation data. Experiments conducted on the standard high dimensional datasets LivDet 2011, LivDet 2013 and LivDet 2015 prove the efficacy of the model under cross-dataset and cross-sensor environments
Chowdary C. Ravindranath - One of the best experts on this subject based on the ideXlab platform.
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EaZy Learning: An Adaptive Variant of Ensemble Learning for Fingerprint Liveness Detection
2021Co-Authors: Agarwal Shivang, Chowdary C. Ravindranath, Sourabh VivekAbstract:In the field of biometrics, fingerprint recognition systems are vulnerable to presentation attacks made by artificially generated spoof fingerprints. Therefore, it is essential to perform liveness detection of a fingerprint before authenticating it. Fingerprint liveness detection mechanisms perform well under the within-dataset environment but fail miserably under cross-sensor (when tested on a fingerprint acquired by a new sensor) and cross-dataset (when trained on one dataset and tested on another) settings. To enhance the generalization abilities, robustness and the interoperability of the fingerprint spoof detectors, the learning models need to be adaptive towards the data. We propose a generic model, EaZy learning which can be considered as an adaptive midway between eager and lazy learning. We show the usefulness of this adaptivity under cross-sensor and cross-dataset environments. EaZy learning examines the properties intrinsic to the dataset while generating a pool of hypotheses. EaZy learning is similar to ensemble learning as it generates an ensemble of Base classifiers and integrates them to make a prediction. Still, it differs in the way it generates the Base classifiers. EaZy learning develops an ensemble of entirely Disjoint Base classifiers which has a beneficial influence on the diversity of the underlying ensemble. Also, it integrates the predictions made by these Base classifiers Based on their performance on the validation data. Experiments conducted on the standard high dimensional datasets LivDet 2011, LivDet 2013 and LivDet 2015 prove the efficacy of the model under cross-dataset and cross-sensor environments
David Lutzer - One of the best experts on this subject based on the ideXlab platform.
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Point countability in generalized ordered spaces
Topology and its Applications, 1996Co-Authors: Harold Bennett, David LutzerAbstract:Abstract In this paper we introduce a property that is a necessary and sufficient condition for a generalized ordered space X with a point-countable Base to have a σ-Disjoint Base. The property is that there are subsets U(n) and D(n) of X such that U(n) is open in X and D(n) is a discrete-in-itself, relatively closed subset of U(n) such that if p is a point of an open set G, then for some n, we have p ϵ U(n) and D(n) ∩ G ≠ φ. This property is hereditary in a generalized ordered space X and implies hereditary paracompactness of X. We give examples to show that our results are the sharpest possible in ordered spaces and describe the role of Property III in general spaces.
Harold Bennett - One of the best experts on this subject based on the ideXlab platform.
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Point countability in generalized ordered spaces
Topology and its Applications, 1996Co-Authors: Harold Bennett, David LutzerAbstract:Abstract In this paper we introduce a property that is a necessary and sufficient condition for a generalized ordered space X with a point-countable Base to have a σ-Disjoint Base. The property is that there are subsets U(n) and D(n) of X such that U(n) is open in X and D(n) is a discrete-in-itself, relatively closed subset of U(n) such that if p is a point of an open set G, then for some n, we have p ϵ U(n) and D(n) ∩ G ≠ φ. This property is hereditary in a generalized ordered space X and implies hereditary paracompactness of X. We give examples to show that our results are the sharpest possible in ordered spaces and describe the role of Property III in general spaces.