Towards Open-set Touchless Palmprint Recognition via Weight-based Meta Metric Learning.
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TLDR
In this paper, a novel Weight-based Meta Metric Learning (W2ML) method is proposed for accurate open-set touchless palmprint recognition, where only a part of categories is seen during training.About:
This article is published in Pattern Recognition.The article was published on 2022-01-01 and is currently open access. It has received 10 citations till now. The article focuses on the topics: Biometrics & Computer science.read more
Citations
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Journal ArticleDOI
Transfer Learning and Deep Metric Learning for Automated Galaxy Morphology Representation
TL;DR: In this article , the authors compare the performance of the crowd-sourced and expert models at predicting the Hubble types of galaxies using the Revised Shapley-Ames (RSA) catalogue of bright galaxies.
Journal ArticleDOI
Palmprint recognition system for mobile device based on circle loss
TL;DR: Zhang et al. as discussed by the authors proposed a novel palmprint recognition method towards mobile devices by incorporating MobileFaceNet and Circle loss (IMC), which can speed up operation while maintaining high accuracy using a lightweight model.
Journal ArticleDOI
Distribution alignment for cross-device palmprint recognition
TL;DR: Wang et al. as discussed by the authors proposed a novel distribution-based loss to narrow the representation gap across devices, and established a new cross-device benchmark based on existing palmprint recognition datasets.
Journal ArticleDOI
Data Protection in Palmprint Recognition via Dynamic Random Invisible Watermark Embedding
TL;DR: Wang et al. as mentioned in this paper proposed an active biometric data protection model for securing palmprint images in transmission or storage scenarios, which implicitly embeds a watermark in each original palmprint ROI image, and then separates the embedded watermark from the watermarked image before identification.
Book ChapterDOI
Cross-dataset Image Matching Network for Heterogeneous Palmprint Recognition
TL;DR: Wang et al. as mentioned in this paper proposed a cross-dataset image matching network (CDMNet) for heterogeneous palmprint recognition, where feature representations specific to a certain domain are learned in the shallow layer of the network, and feature styles are continuously aligned to narrow the gap between domains.
References
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TL;DR: In this article, the authors proposed a residual learning framework to ease the training of networks that are substantially deeper than those used previously, which won the 1st place on the ILSVRC 2015 classification task.
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A Survey on Transfer Learning
Sinno Jialin Pan,Qiang Yang +1 more
TL;DR: The relationship between transfer learning and other related machine learning techniques such as domain adaptation, multitask learning and sample selection bias, as well as covariate shift are discussed.
Posted Content
Improving neural networks by preventing co-adaptation of feature detectors
TL;DR: The authors randomly omits half of the feature detectors on each training case to prevent complex co-adaptations in which a feature detector is only helpful in the context of several other specific feature detectors.
Proceedings Article
Prototypical Networks for Few-shot Learning
TL;DR: Prototypical Networks as discussed by the authors learn a metric space in which classification can be performed by computing distances to prototype representations of each class, and achieve state-of-the-art results on the CU-Birds dataset.
Journal ArticleDOI
An introduction to biometric recognition
TL;DR: A brief overview of the field of biometrics is given and some of its advantages, disadvantages, strengths, limitations, and related privacy concerns are summarized.