Cluster Computing

, Volume 22, Supplement 1, pp 215–228 | Cite as

Prognostic evaluation of multimodal biometric traits recognition based human face, finger print and iris images using ensembled SVM classifier

  • J. RajaEmail author
  • K. Gunasekaran
  • R. Pitchai


Biometric recognition is an effective method for discovering a person’s identity. Multimodal biometric recognition employs multiple sources of information about a human for authentication. Recently, many research works are designed for multimodal biometric recognition using classification techniques. However, the performance of conventional techniques was not efficient for achieving higher recognition rate. In order to overcome such limitations, an ensembled support vector machine based kernel mapping (ESVM-KM) technique is proposed for multimodal biometric recognition. The ESVM-KM technique is designed for improving the accuracy of multimodal biometric recognition with human face, finger print and iris images. The ESVM-KM technique initially performs the preprocessing in order to remove noise and to improve the image quality for human recognition. After that, ESVM-KM technique carried outs Gabor wavelet transformation based feature extraction process in which features of human face, finger print and iris images are efficiently extorted for classification. Finally, the ESVM-KM technique used ensembled SVM classifier for enhancing the recognition rate of multimodal biometric system. The ESVM-KM technique conducts simulation work on the metrics such as computational time, recognition rate, and true positive rate. The simulation results demonstrate that the ESVM-KM technique is able to improve the recognition rate and also reduces computational time of multimodal biometric recognition system when compared to state-of-the-art works. The results got through ESVM-KM are stored in cloud environment for easy and future access.


Ensembled SVM classifier Face Feature extraction Finger print Gabor wavelet transformation Iris Multimodal biometric recognition Preprocessing Cloud 


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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Department of Electronics and Communication EngineeringAdhiparasakthi Engineering CollegeChennaiIndia
  2. 2.Anna UniversityChennaiIndia
  3. 3.Department of Computer Science and EngineeringB.V. Raju Institute of TechnologyNarsapurIndia

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