ProxyFAUG: Proximity-based Fingerprint Augmentation
- URL: http://arxiv.org/abs/2102.02706v1
- Date: Thu, 4 Feb 2021 15:59:30 GMT
- Title: ProxyFAUG: Proximity-based Fingerprint Augmentation
- Authors: Grigorios G. Anagnostopoulos and Alexandros Kalousis
- Abstract summary: ProxyFAUG is a rule-based, proximity-based method of fingerprint augmentation.
The best performing positioning method on this dataset is improved by 40% in terms of median error and 6% in terms of mean error, with the use of the augmented dataset.
- Score: 81.15016852963676
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: The proliferation of data-demanding machine learning methods has brought to
light the necessity for methodologies which can enlarge the size of training
datasets, with simple, rule-based methods. In-line with this concept, the
fingerprint augmentation scheme proposed in this work aims to augment
fingerprint datasets which are used to train positioning models. The proposed
method utilizes fingerprints which are recorded in spacial proximity, in order
to perform fingerprint augmentation, creating new fingerprints which combine
the features of the original ones. The proposed method of composing the new,
augmented fingerprints is inspired by the crossover and mutation operators of
genetic algorithms. The ProxyFAUG method aims to improve the achievable
positioning accuracy of fingerprint datasets, by introducing a rule-based,
stochastic, proximity-based method of fingerprint augmentation. The performance
of ProxyFAUG is evaluated in an outdoor Sigfox setting using a public dataset.
The best performing published positioning method on this dataset is improved by
40% in terms of median error and 6% in terms of mean error, with the use of the
augmented dataset. The analysis of the results indicate a systematic and
significant performance improvement at the lower error quartiles, as indicated
by the impressive improvement of the median error.
Related papers
- Latent fingerprint enhancement for accurate minutiae detection [8.996826918574463]
We propose a novel approach that uses generative adversary networks (GANs) to redefine Latent Fingerprint Enhancement (LFE)
By directly optimising the minutiae information during the generation process, the model produces enhanced latent fingerprints that exhibit exceptional fidelity to ground-truth instances.
Our framework integrates minutiae locations and orientation fields, ensuring the preservation of both local and structural fingerprint features.
arXiv Detail & Related papers (2024-09-18T08:35:31Z) - Unbalanced Fingerprint Classification for Hybrid Fingerprint Orientation Maps [1.6229112905195138]
We target the cause of missed detection by identifying the fingerprints at an early stage among dry, standard, and wet.
Scanned images are classified based on clarity correlated with the proposed feature points.
It was also found that the new approach performs better than the neural-network based classification methods.
arXiv Detail & Related papers (2024-09-01T16:53:02Z) - Fusion of Minutia Cylinder Codes and Minutia Patch Embeddings for Latent Fingerprint Recognition [1.534667887016089]
We propose a fusion based local matching approach towards latent fingerprint recognition.
Proposed approach would integrate these handcrafted features with a recently proposed deep neural network embedding features in a multi-stage fusion approach.
arXiv Detail & Related papers (2024-03-24T14:29:41Z) - Cluster-level pseudo-labelling for source-free cross-domain facial
expression recognition [94.56304526014875]
We propose the first Source-Free Unsupervised Domain Adaptation (SFUDA) method for Facial Expression Recognition (FER)
Our method exploits self-supervised pretraining to learn good feature representations from the target data.
We validate the effectiveness of our method in four adaptation setups, proving that it consistently outperforms existing SFUDA methods when applied to FER.
arXiv Detail & Related papers (2022-10-11T08:24:50Z) - FIGO: Enhanced Fingerprint Identification Approach Using GAN and One
Shot Learning Techniques [0.0]
We propose a Fingerprint Identification approach based on Generative adversarial network and One-shot learning techniques.
First, we propose a Pix2Pix model to transform low-quality fingerprint images to a higher level of fingerprint images pixel by pixel directly in the fingerprint enhancement tier.
Second, we construct a fully automated fingerprint feature extraction model using a one-shot learning approach to differentiate each fingerprint from the others in the fingerprint identification process.
arXiv Detail & Related papers (2022-08-11T02:45:42Z) - Invariance Learning in Deep Neural Networks with Differentiable Laplace
Approximations [76.82124752950148]
We develop a convenient gradient-based method for selecting the data augmentation.
We use a differentiable Kronecker-factored Laplace approximation to the marginal likelihood as our objective.
arXiv Detail & Related papers (2022-02-22T02:51:11Z) - Weakly Supervised Change Detection Using Guided Anisotropic Difusion [97.43170678509478]
We propose original ideas that help us to leverage such datasets in the context of change detection.
First, we propose the guided anisotropic diffusion (GAD) algorithm, which improves semantic segmentation results.
We then show its potential in two weakly-supervised learning strategies tailored for change detection.
arXiv Detail & Related papers (2021-12-31T10:03:47Z) - An Unsupervised Deep-Learning Method for Fingerprint Classification: the
CCAE Network and the Hybrid Clustering Strategy [2.370553892492642]
We propose a new and efficient unsupervised deep learning method that can extract fingerprint features and classify fingerprint patterns automatically.
A set of experiments in the NIST-DB4 dataset shows that the proposed unsupervised method exhibits the efficient performance on fingerprint classification.
arXiv Detail & Related papers (2021-09-12T14:35:59Z) - Responsible Disclosure of Generative Models Using Scalable
Fingerprinting [70.81987741132451]
Deep generative models have achieved a qualitatively new level of performance.
There are concerns on how this technology can be misused to spoof sensors, generate deep fakes, and enable misinformation at scale.
Our work enables a responsible disclosure of such state-of-the-art generative models, that allows researchers and companies to fingerprint their models.
arXiv Detail & Related papers (2020-12-16T03:51:54Z) - Heuristic Semi-Supervised Learning for Graph Generation Inspired by
Electoral College [80.67842220664231]
We propose a novel pre-processing technique, namely ELectoral COllege (ELCO), which automatically expands new nodes and edges to refine the label similarity within a dense subgraph.
In all setups tested, our method boosts the average score of base models by a large margin of 4.7 points, as well as consistently outperforms the state-of-the-art.
arXiv Detail & Related papers (2020-06-10T14:48:48Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.