Cost‐effective and accurate palm vein recognition system based on multiframe super‐resolution algorithms

Kilian, Venance; Ally, Nassor; Nombo, Josiah; Abdalla, Abdi T.; Maiseli, Baraka

doi:10.1049/iet-bmt.2019.0016

Cited by 6 publications

(4 citation statements)

References 50 publications

(72 reference statements)

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“…Researchers have reported an accuracy of upto 99.4% reinforcing the theoretical claims of high uniqueness of vein patterns (Das et al, 2018). However, the requirement of using sophisticated acquisition devices for obtaining the biometric data makes this modality relatively less popular (Kilian et al, 2020). Moreover, using vein patterns can be a challenge in some cases because of the physiological changes taking place due to ageing and various medical conditions (Oloyede & Hancke, 2016).…”

Section: Wrinkles Delta Pointsmentioning

confidence: 90%

Hand-based multibiometric systems: state-of-the-art and future challenges

Aftab

Khan

et al. 2021

PeerJ Computer Science

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The traditional methods used for the identification of individuals such as personal identification numbers (PINs), identification tags, etc., are vulnerable as they are easily compromised by the hackers. In this paper, we aim to focus on the existing multibiometric systems that use hand based modalities for the identification of individuals. We cover the existing multibiometric systems in the context of various feature extraction schemes, along with an analysis of their performance using one of the performance measures used for biometric systems. Later, we cover the literature on template protection including various cancelable biometrics and biometric cryptosystems and provide a brief comment about the methods used for multibiometric template protection. Finally, we discuss various open issues and challenges faced by researchers and propose some future directions that can enhance the security of multibiometric templates.

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Section: Wrinkles Delta Pointsmentioning

confidence: 90%

Hand-based multibiometric systems: state-of-the-art and future challenges

Aftab

Khan

et al. 2021

PeerJ Computer Science

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“…Geometry-based approaches introduced by Cho etl. [12], Greitans et al [16], Hernández-García et al [21], Kilian et al [28], Lee [32,33], Moravec [45], Wang et al [64], Wu et al [67,69,70], Zhang et al [75], Zhou and Kumar [84,85] are based on the geometrical locations of the vein pattern. Feature extraction methods such as local thresholding, multiscale matched filtering, complex matched filtering, localized Radon transform, directional coding, directional filter bank, etc., are geometry-based approaches.…”

Section: Related Workmentioning

confidence: 99%

Ensembling Scale Invariant and Multiresolution Gabor Scores for Palm Vein Identification

Ananthi¹,

Sekar²,

Arivazhagan³

2022

ITC

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Biometric recognition based on palm vein trait has the advantages of liveness detection and high level of security. An improved human palm vein identification system based on ensembling the scores computed from scale invariant features and multiresolution adaptive Gabor features is proposed. In the training phase, from the input palm vein images, the interested palm regions are segmented using 3-valley point maximal palm extraction strategy, an improved method that extracts the maximal region of interest (ROI) easily and properly. Extracted ROI is enhanced using contrast limited adaptive histogram equalization method. From the enhanced image, local invariant features are extracted by applying scale invariant feature transform (SIFT). The texture and multiresolution features are extracted by employing adaptive Gabor filter over the enhanced image. These two features, scale invariant and multiresolution Gabor features act as the templates. In the testing phase, for the test images, ROI extraction, image enhancement, and two different feature extractions are performed. Using cosine similarity and match count-based classification, the score, Ss is computed for the SIFT features. Another score, Sg is computed using the normalized Hamming distance measure for the Gabor features. Both these scores are ensembled using the weighted sum rule to produce the final score, SF for identifying the person. Experiments conducted with CASIA multispectral palmprint image database version 1.0 and VERA palm vein database show that, the proposed method achieves equal error rate of 0.026% and 0.0205% respectively. For these databases, recognition rate of 99.73% and 99.89% respectively are obtained which is superior to the state-of-the-art methods in authentication and identification. The proposed work is suitable for applications wherein the authenticated person should not be considered as imposter.

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“…With regard to palm vein recognition, most of the traditional methods are based on the skeleton structure of the subcutaneous vein [10]. Chen et al [11] used a Gaussian Matched Filter (GMF) for feature extraction and then employed an Iterative Closest Point (ICP) algorithm for matching.…”

Section: Introductionmentioning

confidence: 99%

“…With regard to palm vein recognition, most of the traditional methods are based on the skeleton structure of the subcutaneous vein [10]. Chen et al.…”

Section: Introductionmentioning

confidence: 99%

A deep biometric hash learning framework for three advanced hand‐based biometrics

Shao

Zhong

2021

IET Biometrics

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Hand‐based biometrics has undergone extensive research in recent decades. Besides fingerprint, which is commonly used in personal authentication, there are other three advanced hand‐based biometrics that are further researched worldwide, such as palmprint, palm vein, and dorsal hand vein. However, the academics mainly focus on their unimodal or multimodal recognitions, and few researchers conduct comprehensive comparisons for them to guide practical applications, that is, to assess which one is the suitable biometric modality. Inspired by deep hashing network (DHN) and transfer learning, we propose a deep biometric hash learning (DBHL) framework to uniformly analyse and deal with these three biometrics. An end‐to‐end network is involved to convert images into binary codes. Pre‐trained network is employed for fine‐tuning, and the hamming distance is adopted to measure the similarity between the codes of query and registration images. Through experiments on benchmarks, equal error rate (EER) and the maximum or minimum distance of genuines or imposters are obtained as performance metrics. In experiments, EERs of palmprint and palm vein recognitions reach 0%, and EER of dorsal hand vein recognition is as low as 0.196%. It shows that the DBHL framework is effective to construct hand‐based biometric systems. Furthermore, a plain comparison among them shows that palmprint is more suitable in practical applications.

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Cost‐effective and accurate palm vein recognition system based on multiframe super‐resolution algorithms

Cited by 6 publications

References 50 publications

Hand-based multibiometric systems: state-of-the-art and future challenges

Hand-based multibiometric systems: state-of-the-art and future challenges

Ensembling Scale Invariant and Multiresolution Gabor Scores for Palm Vein Identification

A deep biometric hash learning framework for three advanced hand‐based biometrics

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