An overview of palmvein biometrics

Abukmeil, Mohanad; Marcialis, Gian Luca; Marcel, Sébastien

doi:10.1049/pbse008e_ch10

Cited by 2 publications

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“…It could be interesting to evaluate different parameters of the BSIF algorithm applied to the CASIA database, because it is reasonable to hypothesize that increasing the number of BSIF filters should further improve the performance. If this study was already done in [5] for LBP, to the best of our knowledge this has not yet been done for BSIFs, although they were investigated and cited in [22], [23], [24].…”

Section: B Resultsmentioning

confidence: 99%

A multiple classifiers-based approach to palmvein identification

Micheletto

Orrù

Rida

et al. 2018

2018 Eighth International Conference on Image Processing Theory, Tools and Applications (IPTA)

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The usual trend for the conventional palmvein recognition techniques is first to extract discriminative handcrafted feature representations from the raw images, and then feed a classifier with them. Unfortunately, it is not yet clear how the effectiveness of such features may be held in case of a large user population or in environments where the variability among acquisitions of the same person may increase. In order to face with this problem, it may be considered that the use of multiple classifiers may increase the recognition performance with respect to that of the best individual classifier, and also may handle the problem of an effective feature extraction step. In this paper, we explore the ensemble classifier approach based on Random Subspace Method (RSM), where the basic feature space is derived after a preliminary feature reduction step on the source image, and compare results achieved with and without the use of hand-crafted features. Experimental results allow us concluding that this approach leads to better results under different environmental conditions.

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Section: B Resultsmentioning

confidence: 99%

A multiple classifiers-based approach to palmvein identification

Micheletto

Orrù

Rida

et al. 2018

2018 Eighth International Conference on Image Processing Theory, Tools and Applications (IPTA)

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“…No The adopted local dense descriptors offered publicly by The Center for Machine Vision Re-search (CMV) 3 , and have been evaluated in Matlab environment.…”

Section: Protocolmentioning

confidence: 99%

“…Among others, palmvein recognition is a promising technology which received considerable interest recently. The vein pattern residing in the palm have several characteristics such as complex and rich structure, simultaneously and automatically assure the liveness in the presented sample, and difficult to imitate [3].…”

Section: Introductionmentioning

confidence: 99%

“…In this context, the contributions of this paper are threefold: (1) we test extensively a subset of recent hand crafted local descriptor, namely, Local Binary Patterns (LBP [26]), Local Phase Quantization (LPQ [27]), Local Tera Pattern (LTP [10]), Local Directional Pattern which didn't used yet for palmvein (LDP [11]), and Binarized Statistical Image Features (BSIF [19]) in both verification and identification use-cases; (2) we empirically investigated at which extent the multi-snapshot fusion of palmvein images, at feature level, can further improve the verification and identification performance; (3) we explored a statistical driven analysis based on recommended performance evaluation indicators released by ISO [1] and [16] to reach comparable and fair reproducible research. The recommended indicators are: (i) Equal Error Rate EER which presents the single index measure when FAR and FRR intersect.…”

Section: Introductionmentioning

confidence: 99%

“…Its main use has been so far in palmvein verification, for example, checking the football team fans identity 1 , but, as we will show in this paper beside verification, palmvein identification should be considered for immigrants checking and forensics investigation as well. In fact, palmvein can't be faked or canceled as fingerprints (immigrants tend to hide their fingerprints in order to impede the possibility to be identified) [3].…”

Section: Introductionmentioning

confidence: 99%

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Experimental results on palmvein-based personal recognition by multi-snapshot fusion of textural features

Abukmeil¹,

Marcialis²

2020

Preprint

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In this paper, we investigate multiple snapshot fusion of textural features for palmvein recognition including identification and verification. Although the literature proposed several approaches for palmvein recognition, the palmvein performance is still affected by identification and verification errors. As well-known, palmveins are usually described by line-based methods which enhance the vein flow. This is claimed to be unique from person to person. However, palmvein images are also characterized by texture that can be pointed out by textural features, which relies on recent and efficient hand crafted algorithms such as Local Binary Patterns, Local Phase Quantization, Local Tera Pattern, Local directional Pattern, and Binarized Statistical Image Features (LBP, LPQ, LTP, LDP and BSIF, respectively), among others. Finally, they can be easily managed at feature-level fusion, when more than one sample can be acquired for recognition. Therefore, multi-snapshot fusion can be adopted for exploiting these features complementarity. Our goal is to show that this is confirmed for palmvein recognition, thus allowing to achieve very high recognition rates on a well-known benchmark data set.

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An overview of palmvein biometrics

Cited by 2 publications

References 0 publications

A multiple classifiers-based approach to palmvein identification

A multiple classifiers-based approach to palmvein identification

Experimental results on palmvein-based personal recognition by multi-snapshot fusion of textural features

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