PalmGAN for Cross-Domain Palmprint Recognition

Shao, Huikai; Zhong, Dexing; Li, Yuhan

doi:10.1109/icme.2019.00241

Cited by 19 publications

(15 citation statements)

References 18 publications

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“… [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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“…Using this method, pores, which are a level 3 feature, can be exploited even when low-resolution images are input in a fingerprint recognition system. Shao et al proposed PalmGAN, which generates synthetic data using a palmprint dataset with labels [ 15 ]. Fake labeled data were generated using the palmprint dataset without labels as the target and the palmprint dataset with labels as the source.…”

Section: Related Workmentioning

confidence: 99%

Finger-Vein Recognition Using Heterogeneous Databases by Domain Adaption Based on a Cycle-Consistent Adversarial Network

Noh

Choi

Hong

et al. 2021

Sensors

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The conventional finger-vein recognition system is trained using one type of database and entails the serious problem of performance degradation when tested with different types of databases. This degradation is caused by changes in image characteristics due to variable factors such as position of camera, finger, and lighting. Therefore, each database has varying characteristics despite the same finger-vein modality. However, previous researches on improving the recognition accuracy of unobserved or heterogeneous databases is lacking. To overcome this problem, we propose a method to improve the finger-vein recognition accuracy using domain adaptation between heterogeneous databases using cycle-consistent adversarial networks (CycleGAN), which enhances the recognition accuracy of unobserved data. The experiments were performed with two open databases—Shandong University homologous multi-modal traits finger-vein database (SDUMLA-HMT-DB) and Hong Kong Polytech University finger-image database (HKPolyU-DB). They showed that the equal error rate (EER) of finger-vein recognition was 0.85% in case of training with SDUMLA-HMT-DB and testing with HKPolyU-DB, which had an improvement of 33.1% compared to the second best method. The EER was 3.4% in case of training with HKPolyU-DB and testing with SDUMLA-HMT-DB, which also had an improvement of 4.8% compared to the second best method.

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“…A promising strategy for cross-device palmprint matching was recently proposed by Shao et al [143] with PalmGAN, where a cycle Generative Adversarial Network (cycle GAN) [153] was used to perform cross-domain transformation between palmprint ROIs. A proof of concept was evaluated on the HKPU-Multispectral (HKPU-MS) palmprint dataset containing palm images acquired at several wavelengths, as well a semi-unconstrained dataset acquired with several devices.…”

Section: ) Training Dnnsmentioning

confidence: 99%

“…[11], [26], [29], [30], [137]), Siamese networks (e.g. [33], [37], [140], [143]), but there are or also entirely linear networks (PCANet [81] and PalmNet [134]).…”

Section: Palmprint Feature Extractionmentioning

confidence: 99%

“…This is first investigated in [10], with impressive results being obtained in [37] and [30]. The cross-domain conversion of a palmprint ROI using a generative approach [143] also represents a promising direction of research. A GAN-based architecture might benefit from the ROI pose-normalization approach introduced by Matkowski et al [30], where the ROI extraction network contains a Spatial Transform Network [78].…”

Section: Palmprint Feature Extractionmentioning

confidence: 99%

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Toward Unconstrained Palmprint Recognition on Consumer Devices: A Literature Review

2020

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PalmGAN for Cross-Domain Palmprint Recognition

Cited by 19 publications

References 18 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

Finger-Vein Recognition Using Heterogeneous Databases by Domain Adaption Based on a Cycle-Consistent Adversarial Network

Toward Unconstrained Palmprint Recognition on Consumer Devices: A Literature Review

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