Democratic voting downsampling for coding‐based palmprint recognition

Leng, Lu; Yang, Ziyuan; Min, Weidong

doi:10.1049/iet-bmt.2020.0106

Cited by 17 publications

(6 citation statements)

References 41 publications

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“…The palmprint database PolyU [33] was used in the experiments, which contains 7752 grayscale palmprint images taken from 386 palms in two sessions. The database was divided into two parts: PalmBigDatabaseA and PalmBigDatabaseB.…”

Section: Dataset and Palmprint Recognition Methodsmentioning

confidence: 99%

“…A palmprint is a promising and representative biometric modality. Palmprint recognition methods can be roughly categorized into subspace-based [28,29], statistical-based [30,31], deep-learning-based [32], and coding-based [33] methods. Coding-based methods are free from training and have a low storage cost and fast computational speed, so they are popular for palmprint recognition.…”

Section: Palmprint Recognitionmentioning

confidence: 99%

See 1 more Smart Citation

Reinforced Palmprint Reconstruction Attacks in Biometric Systems

Sun

Leng

Jin

et al. 2022

Sensors

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Biometric signals can be acquired with different sensors and recognized in secure identity management systems. However, it is vulnerable to various attacks that compromise the security management in many applications, such as industrial IoT. In a real-world scenario, the target template stored in the database of a biometric system can possibly be leaked, and then used to reconstruct a fake image to fool the biometric system. As such, many reconstruction attacks have been proposed, yet unsatisfactory naturalness, poor visual quality or incompleteness remains as major limitations. Thus, two reinforced palmprint reconstruction attacks are proposed. Any palmprint image, which can be easily obtained, is used as the initial image, and the region of interest is iteratively modified with deep reinforcement strategies to reduce the matching distance. In the first attack, Modification Constraint within Neighborhood (MCwN) limits the modification extent and suppresses the reckless modification. In the second attack, Batch Member Selection (BMS) selects the significant pixels (SPs) to compose the batch, which are simultaneously modified to a slighter extent to reduce the matching number and the visual-quality degradation. The two reinforced attacks can satisfy all the requirements, which cannot be simultaneously satisfied by the existing attacks. The thorough experiments demonstrate that the two attacks have a highly successful attack rate for palmprint systems based on the most state-of-the-art coding-based methods.

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Section: Dataset and Palmprint Recognition Methodsmentioning

confidence: 99%

Section: Palmprint Recognitionmentioning

confidence: 99%

Reinforced Palmprint Reconstruction Attacks in Biometric Systems

Sun

Leng

Jin

et al. 2022

Sensors

Self Cite

View full text Add to dashboard Cite

show abstract

“…Algebraic combiners perform the mean, max, or product operations. These methods are simple, non-trainable, and widely adopted in the palmprint recognition task [7][8]. Decision Templates (DT) [9] average the decision profiles of each class based on the training data.…”

Section: Decision-making Strategiesmentioning

confidence: 99%

Decision-Making for Multi-View Single Object Detection with Graph Convolutional Networks

Wang,

Kim,

Lee

et al. 2023

J Multimed Inf Syst

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Aggregating predicted outputs from multiple views helps boost multi-view single object detection performance. Decision-making strategies are flexible to perform this result-level aggregation. However, the relationship among multiple views is not exploited in aggregation. This study proposes a novel decision-making model with graph convolutional networks (DM-GCN) to address this issue by establishing a relationship among predicted outputs with graph convolutional networks. Through training, the proposed DM-GCN learns to make a correct decision by enhancing the contributions from informative views. DM-GCN is light, fast, and can be applied to any object detector with a negligible computational cost. Moreover, a real captured dataset named Yogurt10 with a new metric is proposed to investigate the performance of DM-GCN in the multi-view single object detection task. Experimental results show that DM-GCN achieves superior performance compared to classical decision-making strategies. A visual explanation is also provided to interpret how DM-GCN makes a correct decision.

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“…Texture-based methods, such as double Gabor orientation Weber local descriptor (DG-WLD) [5], multi-scale Gabor orientation Weber local descriptors (MOGWLD) [6], difference 2 of 17 of block means (DBM) [7], democratic voting down-sampling (DVD) [8], and various local binary pattern [9] (LBP) variants mentioned in [10], extract information about the direction, frequency, and phase of palm vein texture as features for matching and recognition. However, these methods are limited by the inadequate richness and clarity of texture information in palm vein images, which can result in decreased recognition performance.…”

Section: Introductionmentioning

confidence: 99%

Identity Recognition System Based on Multi-Spectral Palm Vein Image

Wu,

Li,

Zhang

et al. 2023

Electronics

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A multi-spectral palm vein image acquisition device based on an open environment has been designed to achieve a highly secure and user-friendly biometric recognition system. Furthermore, we conducted a study on a supervised discriminative sparse principal component analysis algorithm that preserves the neighborhood structure for palm vein recognition. The algorithm incorporates label information, sparse constraints, and local information for effective supervised learning. By employing a robust neighborhood selection technique, it extracts discriminative and interpretable principal component features from non-uniformly distributed multi-spectral palm vein images. The algorithm addresses challenges posed by light scattering, as well as issues related to rotation, translation, scale variation, and illumination changes during non-contact image acquisition, which can increase intra-class distance. Experimental tests are conducted using databases from the CASIA, Tongji University, and Hong Kong Polytechnic University, as well as a self-built multi-spectral palm vein dataset. The results demonstrate that the algorithm achieves the lowest equal error rates of 0.50%, 0.19%, 0.16%, and 0.1%, respectively, using the optimal projection parameters. Compared to other typical methods, the algorithm exhibits distinct advantages and holds practical value.

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Democratic voting downsampling for coding‐based palmprint recognition

Cited by 17 publications

References 41 publications

Reinforced Palmprint Reconstruction Attacks in Biometric Systems

Reinforced Palmprint Reconstruction Attacks in Biometric Systems

Decision-Making for Multi-View Single Object Detection with Graph Convolutional Networks

Identity Recognition System Based on Multi-Spectral Palm Vein Image

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