Multimodal biometric systems based on different fusion levels of ECG and fingerprint using different classifiers

• Published in: Soft computing (Berlin, Germany) Vol. 24; no. 16; pp. 12599 - 12632
• Main Author: El_Rahman, Sahar A.
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.08.2020; Springer Nature B.V
• Subjects: Artificial Intelligence; Biometrics; Classification; Classifiers; Computational Intelligence; Control; Decision trees; Electrocardiography; Engineering; Fingerprints; Law enforcement; Likelihood ratio; Mathematical Logic and Foundations; Mechatronics; Methodologies and Application; Neural networks; Performance evaluation; Physiology; Robotics; Standard error; Support vector machines; Classification algorithms; Features extraction; Fingerprint recognition system; Multimodal biometrics; ECG recognition system
• DOI: 10.1007/s00500-020-04700-6
• ISSN: 1432-7643

Multimodal biometric system can be accomplished at different levels of fusion and achieve higher recognition performance than the unimodal system. This paper concerned to study the performance of different classification techniques and fusion rules in the context of unimodal and multimodal biometric systems based on the electrocardiogram (ECG) and fingerprint. The experiments are conducted on ECG and fingerprint databases to evaluate the performance of the proposed biometric systems. MIT-BIH database is utilized for ECG, FVC 2004 database is utilized for the fingerprint, and further experiments are being performed to evaluate the proposed multimodal system with 47 subjects from virtual multimodal database. The performance of the proposed unimodal and multimodal biometric systems is measured using receiver operating characteristic (ROC) curve, AUC (area under the ROC curve), sensitivity, specificity, efficiency, standard error of the mean, and likelihood ratio. The findings indicate AUC up to 0.985 for sequential multimodal system, and up to 0.956 for parallel multimodal system, as compared to the unimodal systems that achieved AUC up to 0.951, and 0.866, for the ECG and fingerprint biometrics, respectively. The overall performance of the proposed multimodal systems is better than that of the unimodal systems based on different classifiers and different fusion levels and rules.