Joint feature fusion and optimization via deep discriminative model for mobile palmprint verification

Izadpanahkakhk, Mahdieh; Razavi, Seyyed Mohammad; Taghipour-Gorjikolaie, Mehran; Zahiri, Seyyed Hamid; Uncini, Aurelio

doi:10.1117/1.jei.28.4.043026

Cited by 6 publications

(2 citation statements)

References 39 publications

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“…Izadpanahkakhk et al . [31] proposed a deep mobile palmprint verification framework via an effective weighted loss function, which could extract discriminative features with high accuracy. Recently, there are also some researches focusing on cross-database palmprint recognition, such as [32] and [33] .…”

Section: Related Workmentioning

confidence: 99%

Towards open-set touchless palmprint recognition via weight-based meta metric learning

Shao

Zhong

2022

Pattern Recognition

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Touchless biometrics has become significant in the wake of novel coronavirus 2019 (COVID-19). Due to the convenience, user-friendly, and high-accuracy, touchless palmprint recognition shows great potential when the hygiene issues are considered during COVID-19. However, previous palmprint recognition methods are mainly focused on close-set scenario. 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. Deep metric learning-based feature extractor is learned in a meta way to improve the generalization ability. Multiple sets are sampled randomly to define support and query sets, which are further combined into meta sets to constrain the set-based distances. Particularly, hard sample mining and weighting are adopted to select informative meta sets to improve the efficiency. Finally, embeddings with obvious inter-class and intra-class differences are obtained as features for palmprint identification and verification. Experiments are conducted on four palmprint benchmarks including fourteen constrained and unconstrained palmprint datasets. The results show that our W2ML method is more robust and efficient in dealing with open-set palmprint recognition issue as compared to the state-of-the-arts, where the accuracy is increased by up to 9.11% and the Equal Error Rate (EER) is decreased by up to 2.97%.

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Section: Related Workmentioning

confidence: 99%

Towards open-set touchless palmprint recognition via weight-based meta metric learning

Shao

Zhong

2022

Pattern Recognition

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“…The fifth branch is the learning-based branch, which has attracted much attention in recent years. Most approaches in this branch simply make use of off-the-shelf CNN structures as palmprint feature encoders [47,48,49,50,51,52,53,54]. Only a few of them aimed to design new CNN architectures to embed the special traits of palmprint images [55,56,57,58].…”

Section: Approaches For Palmprint Verificationmentioning

confidence: 99%

Towards Palmprint Verification On Smartphones

Zhang

Zhang²,

Li³

et al. 2020

Preprint

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With the rapid development of mobile devices, smartphones have gradually become an indispensable part of people's lives. Meanwhile, biometric authentication has been corroborated to be an effective method for establishing a person's identity with high confidence. Hence, recently, biometric technologies for smartphones have also become increasingly sophisticated and popular. But it is noteworthy that the application potential of palmprints for smartphones is seriously underestimated. Studies in the past two decades have shown that palmprints have outstanding merits in uniqueness and permanence, and have high user acceptance. However, currently, studies specializing in palmprint verification for smartphones are still quite sporadic, especially when compared to face-or fingerprint-oriented ones. In this paper, aiming to fill the aforementioned research gap, we conducted a thorough study of palmprint verification on smartphones and our contributions are twofold. First, to facilitate the study of palmprint verification on smartphones, we established an annotated palmprint dataset named MPD, which was collected by multi-brand smartphones in two separate sessions with various backgrounds and illumination conditions. As the largest dataset in this field, MPD contains 16,000 palm images collected from 200 subjects. Second, we built a DCNN-based palmprint verification system named DeepMPV+ for smartphones. In DeepMPV+, two key steps, ROI extraction and ROI matching, are both formulated as learning problems and then solved naturally by modern DCNN models. The efficiency and efficacy of DeepMPV+ have been corroborated by extensive experiments. To make our results fully reproducible, the labeled dataset and the relevant source codes have been made publicly available at https://cslinzhang.github.io/MobilePalmPrint/.

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Prototype Correction via Contrastive Augmentation for Few-Shot Unconstrained Palmprint Recognition

Jing,

Zhang,

Zhang

et al. 2023

IEEE Trans.Inform.Forensic Secur.

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Joint feature fusion and optimization via deep discriminative model for mobile palmprint verification

Cited by 6 publications

References 39 publications

Towards open-set touchless palmprint recognition via weight-based meta metric learning

Towards open-set touchless palmprint recognition via weight-based meta metric learning

Towards Palmprint Verification On Smartphones

Prototype Correction via Contrastive Augmentation for Few-Shot Unconstrained Palmprint Recognition

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