Robust multimodal biometric authentication algorithms using fingerprint, iris and voice features fusion

Autor:	Mohamed S. El_Tokhy
Rok vydání:	2021
Předmět:	Statistics and Probability Fusion Computer science business.industry Fingerprint (computing) General Engineering 020206 networking & telecommunications Pattern recognition 02 engineering and technology ComputingMethodologies_PATTERNRECOGNITION Artificial Intelligence Multimodal biometrics 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing IRIS (biosensor) Artificial intelligence business Data Authentication Algorithm
Zdroj:	Journal of Intelligent & Fuzzy Systems. 40:647-672
ISSN:	1875-8967 1064-1246
DOI:	10.3233/jifs-200425
Popis:	Development of a robust triple multimodal biometric approach for human authentication using fingerprint, iris and voice biometric is the main objective of this manuscript. Accordingly, three essential algorithms for biometric authentication are presented. The extracted features from these multimodals are combined via feature fusion center (FFC) and feature scores. These features are trained through artificial neural network (ANN) and support vector machine (SVM) classifiers. The first algorithm depends on boundary energy method (BEM) extracted features from fingerprint, normalized combinational features from iris and dimensionality reduction methods (DRM) from voice using sum/average FFC. The second proposed algorithm uses extracted features from zoning method of fingerprint, SIFT of iris and higher order statistics (HOS) of voice signals. The third proposed algorithm consists of extracted features from zoning method for fingerprint, SIFT from iris and DRM from voice signals. Classification accuracy of implemented algorithms is estimated. Comparison between proposed algorithms is introduced in terms of equal error rate (EER) and ROC curves. The experimental results confirm superiority of second proposed algorithm which achieves a classification rate of 100% using SVM classifier and sum FFC. From computational point of view, the first algorithm consumes the lowest time using SVM classifier. On other hand, the lowest EER is achieved by first proposed algorithm for extracted features from Karhunen-Loeve transform (KLT) method of DRM. Additionally, the lowest ROC curves are accomplished respectively for extracted features from multidimensional scaling (MDS), generated ARMA synthesis and Isomap features. Their accuracy is improved with SVM. Also, the sum FFC introduces efficient results compared to average FFC. These algorithms have the advantages of robustness and the strength of selecting unimodal, double and triple biometric authentication. The obtained results accomplish a remarkable accuracy for authentication and security within multi practical applications.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::08e626d7dc9c99a79dd5b9e67f51f6ca https://doi.org/10.3233/jifs-200425 Zobrazit plný text záznamu Plný text ve formátu PDF Plný text ve formátu HTML
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