852 647
Full Length Article
Fusion: Practice and Applications
Volume 1 , Issue 2, PP: 79-91 , 2020 | Cite this article as | XML | Html |PDF

Title

Multimodal Image Fusion in Biometric Authentication

  Uma Maheshwari 1 * ,   Kalpanaka Silingam 2

1  Hindusthan Institute of Technology, Coimbatore, India
    (umamaheshwari@hit.edu.in)

2  Hindusthan Institute of Technology, Coimbatore, India
    (kalpanakasilingam81@gmail.com)


Doi   :   https://doi.org/10.54216/FPA.010203

Received: February 14, 2020 Accepted: April 18, 2020

Abstract :

During this study, a unique multimodal biometric system was constructed. This system incorporated a variety of unimodal biometric inputs, including fingerprints, palmprints, knuckle prints, and retina images. The multimodal system generated the fused template via feature-level fusion, which combined several different biometric characteristics. The Gabor filter extracted the features from the various biometric aspects. The fusion of the extracted information from the fingerprint, knuckle print, palmprint, and retina into a single template, which was then saved in the database for authentication, resulted in a reduction in both the spatial and temporal complexity of the process. A novel technique for safeguarding fingerprint privacy has been developed to contribute to the study. This system integrates the unique fingerprints of the thumb, index finger, and middle finger into a single new template. It was suggested that the Fixed-Size Template (FEFST) technique may be used might develop a novel strategy for the extraction of fingerprint features. From each of the fingerprints, the minute locations of the ridge end and ridge bifurcations as well as their orientations relative to the reference points were retrieved. The primary template was derived from the fingerprint that included the greatest number of ridge ends. For the purpose of generating the combined minutiae template, the templates of the other two fingerprints were incorporated into this template. The merged minutiae template that was developed was then saved in a database so that registration could take place. During the authentication process, the system received the three query fingerprints, and those fingerprints were compared to the previously saved template.

Keywords :

wavelet-based image fusion; sum absolute difference; hazy images; Kalman filter.

References :

[1] Ratha, N. K., Connell, J. H., & Bolle, R. M. (2001). Enhancing security and privacy in

biometrics-based authentication systems. IBM systems Journal, 40(3), 614-634

[2] Wayman, J., Jain, A., Maltoni, D., & Maio, D. (2005). An introduction to biometric

authentication systems. Biometric Systems, 1-20

[3] Jain, A. K., Ross, A., & Pankanti, S. (2006). Biometrics: a tool for information security.

IEEE transactions on information forensics and security, 1(2), 125-143

[4] Jain, A. K., & Nandakumar, K. (2012). Biometric Authentication: System Security and

User Privacy. IEEE Computer, 45(11), 87-92

[5] Scarfo, P. (2013). Achieving assured authentication in the digital age. Biometric

Technology Today, 2013(9), 9-11

[6] Hossain, S. M. E., & Chetty, G. (2011). Human Identity Verification by Using

Physiological and Behavioural Biometric Traits. International Journal of Bioscience,

Biochemistry and Bioinformatics, 1(3), 199

[7] Alsaadi, I. M. (2015). Physiological Biometric Authentication Systems, Advantages,

Disadvantages And Future Development: A Review. International Journal of Scientific

& Technology Research, 4(8), 285-289

[8] Gamassi, M., Lazzaroni, M., Misino, M., Piuri, V., Sana, D., & Scotti, F. (2005).

Quality assessment of biometric systems: a comprehensive perspective based on

accuracy and performance measurement. IEEE Transactions on Instrumentation and

Measurement, 54(4), 1489-1496

[9] Lourde, M., & Khosla, D. (2010). Fingerprint Identification in Biometric

SecuritySystems. International Journal of Computer and Electrical Engineering, 2(5),

852

[10] Hashad, F. G., Halim, T. M., Diab, S. M., Sallam, B. M., & El-Samie, F. A. (2010).

Fingerprint recognition using mel-frequency cepstral coefficients. Pattern Recognition

and Image Analysis, 20(3), 360-369

[11] Akram, M. U., Tariq, A., Khan, S. A., & Nasir, S. (2008). Fingerprint image: pre-and

post-processing. International Journal of Biometrics, 1(1), 63-80 145

[12] Sarfraz, M. S., & Hellwich, O. (2008). An efficient front-end facial pose estimation

system for face recognition. Pattern Recognition and Image Analysis, 18(3), 434-441

[13] Turk, M. A., & Pentland, A. P. (1991). Face recognition using eigenfaces. IEEE

Conference on Computer Vision and Pattern Recognition, pp. 586-591

[14] Wright, J., Yang, A. Y., Ganesh, A., Sastry, S. S., & Ma, Y. (2009). Robust face

recognition via sparse representation. IEEE transactions on pattern analysis and

machine intelligence, 31(2), 210-227

[15] Zhao, W., Chellappa, R., Phillips, P. J., & Rosenfeld, A. (2003). Face recognition: A

literature survey. ACM computing surveys (CSUR), 35(4), 399-458

[16] Jain, A. K., & Li, S. Z. (2011). Handbook of face recognition. New York: springer

[17] Ross, A., & Jain, A. K. (2004). Multimodal biometrics: An overview. IEEE Signal

Processing Conference, pp. 1221-1224.

[18] Snelick, R., Uludag, U., Mink, A., Indovina, M., & Jain, A. (2005). Large-scale

evaluation of multimodal biometric authentication using state-of-the-art systems. IEEE

transactions on pattern analysis and machine intelligence, 27(3), 450-455

[19] ud Din, M. (2011). Data Acquisition System For Fingerprint Ultrasonic Imaging Devic

[20] Jayasree, P. S., & Kumar, P. (2013). A fast novel algorithm for salt and pepper impulse

noise removal using B-Splines for finger print forensic images. IEEE International

Conference on Image Information Processing, pp. 427-431


Cite this Article as :
Style #
MLA Uma Maheshwari, Kalpanaka Silingam. "Multimodal Image Fusion in Biometric Authentication." Fusion: Practice and Applications, Vol. 1, No. 2, 2020 ,PP. 79-91 (Doi   :  https://doi.org/10.54216/FPA.010203)
APA Uma Maheshwari, Kalpanaka Silingam. (2020). Multimodal Image Fusion in Biometric Authentication. Journal of Fusion: Practice and Applications, 1 ( 2 ), 79-91 (Doi   :  https://doi.org/10.54216/FPA.010203)
Chicago Uma Maheshwari, Kalpanaka Silingam. "Multimodal Image Fusion in Biometric Authentication." Journal of Fusion: Practice and Applications, 1 no. 2 (2020): 79-91 (Doi   :  https://doi.org/10.54216/FPA.010203)
Harvard Uma Maheshwari, Kalpanaka Silingam. (2020). Multimodal Image Fusion in Biometric Authentication. Journal of Fusion: Practice and Applications, 1 ( 2 ), 79-91 (Doi   :  https://doi.org/10.54216/FPA.010203)
Vancouver Uma Maheshwari, Kalpanaka Silingam. Multimodal Image Fusion in Biometric Authentication. Journal of Fusion: Practice and Applications, (2020); 1 ( 2 ): 79-91 (Doi   :  https://doi.org/10.54216/FPA.010203)
IEEE Uma Maheshwari, Kalpanaka Silingam, Multimodal Image Fusion in Biometric Authentication, Journal of Fusion: Practice and Applications, Vol. 1 , No. 2 , (2020) : 79-91 (Doi   :  https://doi.org/10.54216/FPA.010203)