Multimodal Biometric Authentication Algorithm Using Iris, Palm Print, Face and Signature with Encoded DWT

Sujatha, E.; Chilambuchelvan, A.

doi:10.1007/s11277-017-5034-1

Cited by 21 publications

(7 citation statements)

References 8 publications

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“…Therefore from above mentioned things in 12,13 attains very low Recognition accuracy, 14 offer lack of continuous monitoring, 15 suggest techniques have poor performances in privacy, [16][17][18] imply Biometric data enrollment deprivation, 19,20 lack of Resistance on spoof attacks, hence there is great need to develop an effectual strategy in an emerge field of biometric system.…”

Section: Literature Surveymentioning

confidence: 99%

“…Sujatha et al, 12 in this paper, to recognize individuals as authentication, identification, and verification of claimed identity, a multimodal biometric system is employed. Multimodal biometrics increases the security level accuracy, spoof of attacks, noise in collected data, intra‐class variations, inter‐class variations, nonuniversality, and so forth.…”

Section: Literature Surveymentioning

confidence: 99%

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An ArmurMimus multimodal biometric system for Khosher authentication

Bokade¹,

Kanphade²

2022

Concurrency and Computation

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One of the basic requirements of our modern day society is personal authentication. Biometric recognition should make a human‐like identity determination by identifying its physiological and/or behavioral characteristics. In comparison to traditional knowledge‐based approaches, biometric identification systems have the potential to bring benefits. However, because of the difficulties in extracting non‐class discriminative features, the lack of protection during storage of extracted features, and poor recognition accuracy, most frequently used biometric systems lack model protection and robustness. This research proposed a Mimus multimodal biometric system focused on the combination of multiple modalities and optimal level fusion of features to resolve these problems. Initially, the novel Blob‐funk method extracts the complementary non‐class discriminatory information among different modalities, which accomplishes the biometric data enrollment. Thus, it extracts the different properties by comparing surrounding regions based on finding the local maxima and minima of the function. After extracting the features, they need to be stored in a secure manner in a database. Therefore, the paper incorporates the new code block protection strategy to achieve an effectual protection of continuous monitoring via the generation of non‐invertible features, which is used to create the templates, thus storing them in a database. Finally, the novel Lucynomial logistic regression system incorporates user authentication and thus achieves greater recognition accuracy through estimation of threshold value with confrontation of spoof attacks. Hence, compared to the existing techniques such as SVM, PCA, and DBN, the outcome of the proposed work attains 97.53% accuracy, 0.020% FAR, 96.44% recall, and 97.85% precision, thus exemplifying the competence of the novel system.

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Section: Literature Surveymentioning

confidence: 99%

Section: Literature Surveymentioning

confidence: 99%

An ArmurMimus multimodal biometric system for Khosher authentication

Bokade¹,

Kanphade²

2022

Concurrency and Computation

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“…In [14] proposes a multi-signature mapping approach that reduces the false acceptance rate without having to increase the false rejection rate in addition to eliminating the need to use a threshold that is empirically adjusted. On the other hand, in [15] a multimodal biometric identification system is proposed considering the iris, palm print, face, and signature using the discrete wavelet transform, thus obtaining integrated biometric features and eliminating the problems presented by unimodal biometric systems. In [16] a system is presented to securely store confidential documentation using a mobile device where a set of documents associated where the access policy is encrypted.Confidentiality is achieved through a biometric key binding scheme with facial recognition and the use of hardware-provided security primitives.…”

Section: Related Workmentioning

confidence: 99%

A Brief Review About Biometrics Systems in Modern Context

Caballero¹

2022

ijarcs

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Authentication systems employ various access mechanisms for the data validation process.Nowadays there are many proposals focused on solving the traditional problems that exist to validate the records of installations in public or private places. With the appearance of COVID-19, the use of technology has intensified to avoid contact with physical devices and achieve successful access. This article shows a compilation of work dedicated to methods and techniques for access to facilities by reading modern biometric systems.

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“…A unimodal system is based on a single trait. This model is widely used due to its simplicity; however, it suffers from several limitations, such as noise in the sensor when the data are collected, intraclass variations, non-universality, and spoof attacks [6]. To overcome the limitations of the unimodal system and to increase the security, a multimodal system was suggested.…”

Section: Introductionmentioning

confidence: 99%

Multimodal Recognition System Based on High-Resolution Palmprints

et al. 2020

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High-resolution palmprint recognition is a challenging problem due to deficiencies in images, such as poor quality, skin distortion, and unallocated images. Considering the importance of high-resolution palmprints in forensic applications, this study proposes a novel multimodal palmprint scheme that combines the left and right palmprints using feature-level fusion by exploiting the similarity between the left and right palmprints using high-resolution images. The proposed system accepts as input palmprints that were captured at 500 ppi, which is the standard in forensic applications. The system is implemented by employing a statistical gray-level co-occurrence matrix (GLCM) as the texture feature extraction algorithm. Then, the features are ranked based on their probability distribution functions (PDFs) to select the most significant features. Finally, an enhanced probabilistic neural network (PNN) is used to estimate the recognition system. The benchmark THUPALMLAB database is used to conduct experiments, the results of which demonstrate that the proposed method can yield satisfactory results. INDEX TERMS GLCM, high-resolution palmprint, multimodal, PNN, ranking features.

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Multimodal Biometric Authentication Algorithm Using Iris, Palm Print, Face and Signature with Encoded DWT

Cited by 21 publications

References 8 publications

An ArmurMimus multimodal biometric system for Khosher authentication

An ArmurMimus multimodal biometric system for Khosher authentication

A Brief Review About Biometrics Systems in Modern Context

Multimodal Recognition System Based on High-Resolution Palmprints

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