Score level fusion in multi-biometric identification based on zones of interest

Aizi, Kamel; Ouslim, Mohamed

doi:10.1016/j.jksuci.2019.09.003

Cited by 23 publications

(20 citation statements)

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“…In this paper, we use BCC in [8] to do the score level fusion and do some modification in the process of calculating weighted sum.…”

Section: ) Min(mentioning

confidence: 99%

“…Fusion of scores was achieved either by combination [7] or by classification [5]. In [8], it proposed the combination of these two methods which is called BCC. K-means algorithm is used to divide the score range into zones of interest [8].…”

Section: Introductionmentioning

confidence: 99%

“…In [8], it proposed the combination of these two methods which is called BCC. K-means algorithm is used to divide the score range into zones of interest [8]. However, the combined method did not give a convinced demonstration about how to allocate weight to each score from unimodal identification system.…”

Section: Introductionmentioning

confidence: 99%

“…After the generation of scores of matching values from each unimodal identification system, it will be the fusion process. In [8], the method achieves the classification by the decision tree combined to the weighted sum (BCC) in the paper. And it generates a new score level fusion method.…”

Section: Introductionmentioning

confidence: 99%

“…And it generates a new score level fusion method. However, in [8], it does not give a convincing demonstration about the allocation of weight and it just gives fixed weights to iris scores and fingerprint weight. As we all know, different data distributions should have different weights.…”

Section: Introductionmentioning

confidence: 99%

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A Score Level Fusion Method on Fingerprint and Finger Vein

Peng

Chen

2020

E3S Web Conf.

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In this paper, we represent a score level fusion method on fingerprint and finger vein. Each unimodal identification system carries out processes of image preprocessing, feature extraction and feature matching to generate a vector of score. And we apply clustering analysis to split the score range into zones of interest. Then a decision tree and weighted-sum approach are used to make the decision. We test the proposed method on standard biometric database. Three metrics, namely, False Accept Rate, False Reject Rate, Recognition Rate, are used to evaluate experimental results. And experimental results show that the fusion system has a better performance than unimodal identification system.

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“…In this paper, we use BCC in [8] to do the score level fusion and do some modification in the process of calculating weighted sum.…”

Section: ) Min(mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A Score Level Fusion Method on Fingerprint and Finger Vein

Peng

Chen

2020

E3S Web Conf.

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A multimodal architecture using Adapt‐HKFCT segmentation and feature‐based chaos integrated deep neural networks (Chaos‐DNN‐SPOA) for contactless biometricpalm vein recognition system

Rajalakshmi

Annapurani

2021

Int J of Intelligent Sys

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In the recent past, the fusion of various unimodal biometrics has gained increasing attention from researchers dedicated to the use of practical biometrics. In this paper, a Chaos Integrated Deep Neural Networks (Chaos-DNN) using Sandpiper Optimization Algorithm (SPOA) is proposed to enhance the perfor- | INTRODUCTIONThe biometric system is used to measure the signature points of the individual persons, such as behaviour of a person's identity. [1][2][3] Biometric systems are used for security purposes to control the data or information for future use. It is also used for internet access, securing computer systems, electronic passport control security, banking, cellular phones, credit cards, security access to buildings, health and social services, terrorist determination and corpse recognition. [4][5][6] The biometric technologies are developed using human traits, namely, DNA, ear morphology, facial characteristics, fingerprint, gait, hand geometry, finger geometry, iris, keystroke, smell, palm print, handwriting with signature, voice and so forth. Among them palm venal traits are gaining more attention and are comparatively accurate and secure. 7,8 Multimodal systems are widely used in defence to secure the data compared with the unimodal systems. The imagery of vein is caught in various portions of the hand, like, palm, palm dorsal, the wrist, finger and so forth, which are widely used in recent years of evaluation. 9,10 The vein images, like palm and dorsal part are helps to build low-cost devices. Also the image capture method is more user-friendly, less harm, rapid, lack of contact and easy to use more secure for authentication systems. Hand-based parameters are widely used because of ease to work, availability of various parameters, like, geometrical features, hand thickness, fingers, joints and RAJALAKSHMI AND ANNAPURANI PANAIYAPPAN | 1847 How to cite this article: Rajalakshmi M, Annapurani Panaiyappan K. A multimodal architecture using Adapt-HKFCT segmentation and feature-based chaos integrated deep neural networks (Chaos-DNN-SPOA) for contactless biometricpalm vein recognition system.

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