A Study on Liveness Analysis for Palmprint Recognition System

Aydoğdu, Özge; Sadreddini, Zhaleh; Ekinci, Murat

doi:10.1109/tsp.2018.8441311

Cited by 4 publications

(2 citation statements)

References 7 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Spoofing attacks, involving the presentation of fake biometric data such as displayed or printed images, pose challenges to verifying the authenticity of presented data, jeopardizing the privacy and security of palmprint biometrics. Despite the issue's importance, limited attention has been given to spoofing attacks on palmprint biometrics, as evident from the sparse existing studies [99][100][101][102].…”

Section: Exploring Liveness Detection and Mitigating Vulnerability To...mentioning

confidence: 99%

Palmprint Recognition: Extensive Exploration of Databases, Methodologies, Comparative Assessment, and Future Directions

Amrouni,

Benzaoui,

Zeroual

2023

Applied Sciences

View full text Add to dashboard Cite

This paper presents a comprehensive survey examining the prevailing feature extraction methodologies employed within biometric palmprint recognition models. It encompasses a critical analysis of extant datasets and a comparative study of algorithmic approaches. Specifically, this review delves into palmprint recognition systems, focusing on different feature extraction methodologies. As the dataset wields a profound impact within palmprint recognition, our study meticulously describes 20 extensively employed and recognized palmprint datasets. Furthermore, we classify these datasets into two distinct classes: contact-based datasets and contactless-based datasets. Additionally, we propose a novel taxonomy to categorize palmprint recognition feature extraction approaches into line-based approaches, texture descriptor-based approaches, subspace learning-based methods, local direction encoding-based approaches, and deep learning-based architecture approaches. Within each class, most foundational publications are reviewed, highlighting their core contributions, the datasets utilized, efficiency assessment metrics, and the best outcomes achieved. Finally, open challenges and emerging trends that deserve further attention are elucidated to push progress in future research.

show abstract

Section: Exploring Liveness Detection and Mitigating Vulnerability To...mentioning

confidence: 99%

Palmprint Recognition: Extensive Exploration of Databases, Methodologies, Comparative Assessment, and Future Directions

Amrouni,

Benzaoui,

Zeroual

2023

Applied Sciences

View full text Add to dashboard Cite

show abstract

“…In today's world, where security is becoming more important, personal authentication methods are also updating themselves to close existing security gaps with the developing technology. Although some Biometric Recognition methods, such as fingerprint [1][2][3], palmprint [4,5], retina [6][7][8], iris [8,9], and face recognition [10,11], used for this purpose, are used for tracking, entry-exit access permission control, and security purposes, they have some disadvantages compared to FV recognition. Among them, fingerprint and palmprint recognition systems create a security vulnerability because of unable to control whether the tissue is alive or not, being on the exterior surface of the body, and copied easily.…”

Section: Introductionmentioning

confidence: 99%

Finger Vein Recognition Based on Multi-Features Fusion

Titrek¹,

Baykan²

2023

View full text Add to dashboard Cite

Biometric Recognition Systems allow individuals to be automatically authenticated or identified by using their unique characteristics. Finger vein (FV), widely used for this purpose, has a crucial place among biometric systems because of its advantages, which are user-friendliness, ability to detect living tissue, high reliability, low system cost, and less area requirement in installation. It has a wide usage area, especially in places where personal safety is at the forefront. In this study, we examine the effect of the Horizontal and Vertical Total Proportion (HVTP) feature extraction algorithm on the success rate when the fusion technique is applied. Homomorphic Filter (HF) and Perona-Malik Anisotropic Diffusion (PMAD) are used to remove the noise and light scattering issue in the FV databases, and Gray Level Run Length Matrices (GLRLM), Gray Level Co-occurrence Matrices (GLCM), Segmentation-based Fractal Texture Analysis (SFTA), Horizontal Total Proportion (HTP), and Vertical Total Proportion (VTP) methods are applied to describe texture features. The fusion of multiple features instead of using only one type of feature can improve the accuracy of FV recognition systems. The novelty of the study is the fusion of HTP and VTP with the GLRLM, GLCM, and SFTA features by using Yang finger vein databases (Database_1) and MMCBNU_6000 (Database_2). Experimental results reveal that the HTP and VTP significantly improved the classification success in these FV image databases. The best success rate achieved in the Ensemble classifier is 99.7% using Database_1 and 97.6% using Database_2.

show abstract

Biometric Jammer: A Prevention of Fake Acquisition of Fingerprint for Security Enhancement

Deshmukh¹,

Mohod²

2020

2020 International Conference on Emerging Trends in Information Technology and Engineering (Ic-Etite)

View full text Add to dashboard Cite

A Study on Liveness Analysis for Palmprint Recognition System

Cited by 4 publications

References 7 publications

Palmprint Recognition: Extensive Exploration of Databases, Methodologies, Comparative Assessment, and Future Directions

Palmprint Recognition: Extensive Exploration of Databases, Methodologies, Comparative Assessment, and Future Directions

Finger Vein Recognition Based on Multi-Features Fusion

Biometric Jammer: A Prevention of Fake Acquisition of Fingerprint for Security Enhancement

Contact Info

Product

Resources

About