A secure palm vein recognition system

Wu, Kuang‐Shyr; Lee, Jen-Chun; Lo, Tsung-Ming; Chang, Ko-Chin; Chang, Chien-Ping

doi:10.1016/j.jss.2013.06.065

Cited by 69 publications

(44 citation statements)

References 12 publications

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“…In the papers [10], there is a description of the construction of a biometric system based on the palm vein, which uses a two-dimensional Gaussian function to create filters with which the image is being searched to find as many blood vessels as possible. The Hamming distance was used for pattern matching.…”

Section: Related Workmentioning

confidence: 99%

Verification of people based on palm vein with the roi automatic appointment

Smorawa

2016

J. Appl. Math. Comput. Mech.

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Abstract. In the paper, the new approach to identification and verification of people based on the palm vein pattern will be presented. To create the feature vector, palm vein shape geometric arrangement will be applied. For detailed features will be considered similarly to minutiae which was applied in finger lines analysis in vein endings and bifurcation. The methods of vein location on the inner hand, methods of minutiae location, and features coding will be discussed in detail. To extract specific features a two-dimension density function will be used. The method to determine minutiae-vein bifurcation and endings will be presented.

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Section: Related Workmentioning

confidence: 99%

Verification of people based on palm vein with the roi automatic appointment

Smorawa

2016

J. Appl. Math. Comput. Mech.

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“…Physiological biometric relies on something the users are. It performs authentication base on physical characteristics such as facial features [18][19][20], palm prints [23,24,26], retina patterns [39,13], finger print [13,25], iris pattern [20], as well as hand geometry [13,27]. In other words, physiological biometrics is based on measurements and data derived from direct measurement of a part of the human body.…”

Section: Literature Reviewmentioning

confidence: 99%

Keystroke Dynamic Authentication in Mobile Cloud Computing

Babaeizadeh¹,

Bakhtiari²,

Maarof³

2014

IJCA

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One of the important challenges in Mobile Cloud Computing (MCC) is related to the authentication of users. There is increasing demand for suitable authentication method for accessing to the shared information via the Internet through Cloud Service Provider (CSP). Personal identification number is the most common mechanism for authentication in mobile devices; however it is not secure way for authenticating users. This work presents a method of authentication which able to identify users based on Keystroke Dynamic Authentication (KDA). Furthermore, keystrokes duration is considered as an attribute for measuring keystrokes of mobile's users. This paper proposed strong method of authentication in the password authentication scheme by combining it with keystroke authentication, which is a type of behavioral biometric mechanism. Experimental results show, the proposed method can work 97.014% correctly, due to the keystroke duration of each user depends on their behavioral characteristic and it can be measured up to milliseconds. On the other hand, if unauthorized person knows the username and password of legal user can not gain access rights because of difference between their keystroke duration. Therefore, it is hard for an attacker to pretend as an owner, and this method enhances the security of authentication in MCC

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“…For example, texture analysis is combined with a new shape representation method for dorsal hand veins pattern retrieval in [5] and [18]. Novel texture feature measurements, namely Centroid-based Circular Key-point Grid (CCKG), have recently been proposed in [6] for personal authentication through dorsal hand vein model.…”

Section: Introductionmentioning

confidence: 99%

Dorsal Hand Vein Pattern Analysis and Neural Networks for Biometric Authentication

Belean¹,

Streza²,

Crisan³

et al. 2017

STUD INFORM CONTROL

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Over the past several years, the subcutaneous blood vessels have emerged as a new solution for identity management. Biometric systems based on hand veins are considered to be very promising for high security environments. In this paper, we propose a novel user authentication approach based on dorsal hand vein pattern analysis and multi-layer perceptron neural network classification. For image processing two different techniques are employed: rotation invariant Hough transform and clustering based segmentation and mathematic morphology. Both approaches lead to binary images containing the vein patterns. The vessel structure corresponding to hand image samples of the same user are used to extract the final features, independent of hand rotation and distance to the camera lens during acquisition. These characteristics are used to train the neural network, whereas are computed for the new input images to be classified as corresponding to one of the legitimate subjects or not. The experimental analysis shows that user classification with an equal error rate of 0.83% can be attained, bringing the advantages of proposed image processing techniques for vein detection and neural network classification into complete synergy.

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A secure palm vein recognition system

Cited by 69 publications

References 12 publications

Verification of people based on palm vein with the roi automatic appointment

Verification of people based on palm vein with the roi automatic appointment

Keystroke Dynamic Authentication in Mobile Cloud Computing

Dorsal Hand Vein Pattern Analysis and Neural Networks for Biometric Authentication

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