Biometric Privacy through Hand Geometry- A Survey

Prlhodova, Katerina; Hub, Miloslav

doi:10.1109/dt.2019.8813660

Cited by 13 publications

(10 citation statements)

References 46 publications

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“…The system should not authorize an unauthorized person. It is calculated using (6). 𝐿𝑢 = 𝐴 𝐵 (6) In (6), A represents the number of unsuccessful trials among unauthorized and B represents total unauthorized trials.…”

Section: Resultsmentioning

confidence: 99%

A hybrid learning scheme towards authenticating hand-geometry using multi-modal features

Shivabasappa

Sheela²

2023

IJECE

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<span lang="EN-US">Usage of hand geometry towards biometric-based authentication mechanism has been commercially practiced since last decade. However, there is a rising security problem being surfaced owing to the fluctuating features of hand-geometry during authentication mechanism. Review of existing research techniques exhibits the usage of singular features of hand-geometric along with sophisticated learning schemes where accuracy is accomplished at the higher cost of computational effort. Hence, the proposed study introduces a simplified analytical method which considers multi-modal features extracted from hand geometry which could further improve upon robust recognition system. For this purpose, the system considers implementing hybrid learning scheme using convolution neural network and Siamese algorithm where the former is used for feature extraction and latter is used for recognition of person on the basis of authenticated hand geometry. The main results show that proposed scheme offers 12.2% of improvement in accuracy compared to existing models exhibiting that with simpler amendment by inclusion of multi-modalities, accuracy can be significantly improve without computational burden.</span>

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Section: Resultsmentioning

confidence: 99%

A hybrid learning scheme towards authenticating hand-geometry using multi-modal features

Shivabasappa

Sheela²

2023

IJECE

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“…This paper is an extension of work originally presented in International Conference on Information and Digital Technologies [1]. In today's modern society, there is an increasing emphasis on working with information.…”

Section: Introductionmentioning

confidence: 99%

Hand-Based Biometric Recognition Technique – Survey

Prihodova¹,

Hub²

2020

Adv. sci. technol. eng. syst. j.

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In today's modern world, reliable automatic personal recognition is a crucial area of discussion, primarily because of the increased security risks. A large number of systems first require the recognition of a person before they can access their services. Biometric recognition can be used, which can be understood as automatic identification or automatic verification of persons based on physiological or behavioural characteristics. Examples of biometric characteristics may be the fingerprint, face, signature, hand geometry. There are many approaches, and each has its pros and cons. However, there is currently no current extensive research and evaluation of hand-based biometric systems. Given the userfriendliness of these systems and the needs of society, this article aims to compare different methods of biometric recognition based on hand, so that the article reduces the entry barrier into this area of research. Furthermore, the article aims to determine the research gap and suggest possible directions for research in the future.

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“…Hand geometry is well-established biometrics, as it has been employed in applications for many decades throughout the world [2]. Its main merits include universality, invariance for a long time, collectability, and acceptability, while its distinctiveness is not among the best.…”

Section: Introductionmentioning

confidence: 99%

Multimodal Biometric Recognition Based on 3D Ultrasound Palmprint-Hand Geometry Fusion

Iula

Micucci

2022

IEEE Access

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In recent years, multimodal biometric systems are increasingly employed in many application fields due to several advantages in terms of universality, recognition rate. and security. Among various acquisition technologies, Ultrasound shows important merits, because it allows obtaining volumetric images of the human body and hence a more accurate description of characteristics and to verify liveness. In this work, a multimodal ultrasound recognition system based on the fusion between 3D hand geometry and 3D palmprint features is proposed and experimentally evaluated. The system acquires a volumetric image of the whole hand and for both characteristics, several 2D images are extracted at different depth levels. From each image, 2D features are extracted and then properly combined to achieve a 3D template. Recognition performances are evaluated through verification and identification experiments by employing a homemade database. Experiments are carried out first for the two unimodal biometrics and successively, by fusing the two modalities at score level. Results have shown that fusion is able to dramatically improve the recognition performances of the single biometrics, achieving an Equal Error Rate of 0.08% and an identification rate of 100%.

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Biometric Privacy through Hand Geometry- A Survey

Cited by 13 publications

References 46 publications

A hybrid learning scheme towards authenticating hand-geometry using multi-modal features

A hybrid learning scheme towards authenticating hand-geometry using multi-modal features

Hand-Based Biometric Recognition Technique – Survey

Multimodal Biometric Recognition Based on 3D Ultrasound Palmprint-Hand Geometry Fusion

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