Literature review on multimodal biometrics

  • Authors

    • S Arunarani
    • R Gobinath
    2018-05-07
    https://doi.org/10.14419/ijet.v7i2.26.12529
  • Multimodal Biometrics, Fusion, Fingerprint, Face, Ear Biometrics
  • As technological reformation is widen, biometric systems substitute knowledge based and token based recognition systems. Confidential data are accessed by the user after the user is recognized by biometric systems. Efforts have been made to acquire more suitable prototype for recognizing human as multimodal biometrics has more severe concern because of noise in the sample and malfunctioning sensing devices. This paper gives a dual study related to multimodal biometrics, including a literature review of the prior work in authentication and the proposed evaluation approaches. First, we classify few epitome studies considered in last decades to show how this problem has been solved until now. Second, the paper gives a introduction about basic principles of the associated evaluation approaches, and then provide an extended evaluation framework based on the enrollment selection and also statistically convincing measures for evaluating quality metrics.

     

     

  • References

    1. [1] He, M., S.J. Horng, P. Fan, R.S. Run, R.J. Chen, J.L. Lai, M.K. Khan and K.O. Sentosa, 2010. Performance evaluation of score level fusion in multimodal biometric systems. Pattern Recogn, 43(5): 1789-1800.

      [2] Pflug, A. and C. Busch, 2012. Ear biometrics: A survey of detection, feature extraction and recognition methods. IET Biometrics, 1(2): 114-129.

      [3] Huang, Z., Y. Liu, C. Li, M. Yang and L. Chen, 2013. A robust face and ear based multimodal biometric system using sparse representation. Pattern Recogn., 46(8): 2156-2168.

      [4] Islam, S.M.S., R. Davies, M. Bennamoun, R.A. Owens and A.S. Mian, 2013. Multibiometric human recognition using 3D ear and face features. Pattern Recogn., 46(3): 613-627.

      [5] Aarohi Vora, Chirag Paunwala, Mita Paunwala, “Improved Weight Assignment Approach for Multimodal Fusionâ€, IEEE International Conference on Circuits, Systems, Communication and Information Technology Applications, CSCITA, pp.70- 74,April 2014.

      [6] Aarohi Vora, Chirag Paunwala, Mita Paunwala, “Nonlinear SVM Fusion of Multimodal Biometric Systemâ€, International Multi Conference on Innovations in Engineering and Technology, IMCIET 2014 under International Conference on Communication and Computing track, ICCC 2014, Elsevier, pp. 30-35, August 2014.

      [7] Aarohi Vora, Chirag Paunwala, Mita Paunwala, “Statistical analysis of various kernel parameters on SVM based multimodal fusion,â€Annual IEEE India Conference (INDICON), 2014, pp.1-5, Dec. 2014.

      [8] A. Jain, K. Nandakumar, A. Ross, “Score Normalization in Multimodal Biometric Systemsâ€, Pattern Recognition, vol. 38, no.12, pp. 2270-2285, December 2005.

      [9] Arun Ross, Anil Jain, “Information fusion in biometricsâ€, Pattern Recognition Letters, Elsevier, vol. 24, no.13, pp. 2115- 2125, September 2003.

      [10] Mohamad Abdolahi, Majid Mohamadi, Mehdi Jafari,†Multimodal biometric system fusion using fingerprint and iris with fuzzy logicâ€, International Journal of soft computing and engineering, Vol.2, Issue-6, 2013.

      [11] Gayathri umakant bokade, ashok M.sapkal, “Feature level fusion of palm and face for secure recognitionâ€, International Journal of Computer and Electrical Engineering, Vol.4, No.2, 2012.

      [12] Hema.C.R, Paulraj.M.P & Ramkumar.S, “Classification of Eye Movements Using Electrooculography and Neural Networksâ€, International Journal of Human Computer Interaction, Vol.5 (4), pp.51-63, 2014.

      [13] Hema, C. R., Ramkumar, S., & Paulraj, M. P. , “Idendifying Eye Movements using Neural Networks for Human Computer Interactionâ€, International Journal of Computer Applications, 105(8), pp 18-26, 2014.

      [14] S.Ramkumar, K.SatheshKumar, G.Emayavaramban, â€EOG Signal Classification Using Neural Network for Human Computer Interactionâ€, International Journal of Computer Theory and Applications, Vol.9(24) , pp.223-231, 2016

      [15] Ramkumar, Dr.K.Satheshkumar and G.Emayavaramban†Nine States HCI using Electrooculogram and Neural Networksâ€, IJET, Vol. 8(6), pp. 3056-3064, Jan 2017.

      [16] S.Ramkumar, K.Sathesh Kumar G.Emayavaramban,†A Feasibility Study on Eye Movements Using Electrooculogram Based HCI†IEEE- International Conference on Intelligent Sustainable Systems, pp.384-388, Dec-2017.

      [17] G.Emayavaramban, S.Ramkumar, A.Amudha and K.Sathesh Kumar “Classification Of Hand Gestures Using FFNN And TDNN Networksâ€, International Journal of Pure And Applied Mathematics, Vol.118 (8) Pp. 27-32, Jan 2018.

      [18] S.Ramkumar , K.Sathesh Kumar, T.Dhiliphan Rajkumar,

      M.Ilayaraja, K.Shankar, “A review-classification of electrooculogram based human computer interfacesâ€, Biomedical Research, 29 (6), Pp. 1078-1084, April 2018.

  • Downloads

  • How to Cite

    Arunarani, S., & Gobinath, R. (2018). Literature review on multimodal biometrics. International Journal of Engineering & Technology, 7(2.26), 31-34. https://doi.org/10.14419/ijet.v7i2.26.12529