i-manager Publications

Multimodal Biometrics: Most Appropriate for Person Identification

*Associate Professor, MATS School of IT, MATS University, Raipur (CG), India.

Periodicity:September - November'2017
DOI : https://doi.org/10.26634/jpr.4.3.13881

Abstract

Biometrics is an important application of digital image processing of biometric modalities. There are several types of biometric security technologies such as face recognition, iris recognition, fingerprint matching etc. However, there are some prominent challenges in all biometrics dealing with single modality such as reliability, robustness etc. Therefore, multimodal biometrics is recommended, which involves more than one modality in the system. AADHAAR card is an example of multimodal biometrics.

Multimodal biometrics aims at increasing the reliability of biometric systems. In this research, four traits such as face, ear, iris and foot were used and worked on matching score level and decision level to get advantage of the multimodal approaches. The work was tested over self created database of 100 persons. Different classifier approaches for different modalities were applied; principle component analysis for face, eigen images for ear, hamming distance based technique for iris and modified sequential Haar transform for foot traits. Each biometric trait processed its information independently to calculate weights of individuals. The fusion scheme was applied on all possible combination of traits and the matching score was calculated. The integrated results of different biometric traits increased the recognition performance of the multimodal biometric system considerably

Keywords

Multimodal Biometrics, Principal Component Analysis, Modified Haar Wavelet.

How to Cite this Article?

Barde, S. (2017). Multimodal Biometrics: Most Appropriate for Person Identification. i-manager’s Journal on Pattern Recognition, 4(3), 1-8. https://doi.org/10.26634/jpr.4.3.13881

References

[1]. Barde, S., Zadgaonkar, A. S., & Sinha, G. R. (2014a). Multimodal Biometrics using Face, Ear and Iris Modalities. International Journal of Computer Applications (IJCA), Recent Advances in Information Technology NCRAIT, (2), 9-15.

[2]. Barde, S. & Zadgaonkar, A. S. (2014b). Ear and Foot Biometrics Best for Identification. International Journal of Novel Research in Computer Science and Software Engineering,1(1), 11-19.

[3]. Barde, S., Zadgaonkar, A. S., & Sinha, G. R. (2014c). PCA based Multimodal Biometrics using Ear and Face Modalities. International Journal of Information Technology and Computer Science(IJITCS), 6(5), 43-49.

[4]. Barde S. (2015). Person Identification using Face and Foot Modalities. International Journal of Advanced Engineering Research and Studies (IJAERS), 7(2) 160-163.

[5]. Barde, S. (2016). Person Identification using Ear Modalities. International Journal of Modern Trends in Engineering and Research (IJMTER), 3(5), 8-13.

[6]. Bigun, J., Fierrez-Aguilar, J., Ortega-Garcia, J., & Gonzalez-Rodriguez, J. (2005). Combining biometric evidence for person authentication. Lecture Notes in Computer Science (LNCS), 3161, 1-18.

[7]. Jing, X. Y., Yao, Y. F., Zhang, D., Yang, J. Y., & Li, M. (2007). Face and palm-print pixel level fusion and kernel DCV-RBF classifier for small sample biometric recognition. Pattern Recognition. 40(11), 3209-3224.

[8]. Linas, J., Bowman, C., Rogova, G., Steinberg, A., Waltz, E., & White, F. (2004). Revisiting the JDL data fusion model II. In proc. of 7 International Conference on Information Fusion, Stockholm, Sweden.

[9]. Rattani, A., Kisku, D. R., & Bicego, M. (2010). Feature level fusion of face and fingerprint biometrics. In proc. of 1 IEEE International Conference on Biometrics: Theory, Applications and Systems, (pp. 1-6), IEEE.

[10]. Ross, A. & Jain, A. K. (2004). Multimodal biometrics: An overview. In Signal Processing Conference (EUSIPCO), 12 European (pp.1221-1224). IEEE.

[11]. Ross, A. & Jain, A. K. (2003). Information fusion in biometrics. Pattern Recognition Letters, 24(13), 2115 - 2125.

[12]. Sanderson, C. & Paliwal, K. K. (2001). Information fusion for robust speaker verification. In proc. of Seventh European Conference on Speech Communication and Technology, Aalbo: 755-758.

[13]. 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.

[14]. Yao, Y., Jing, X., & Wong, H. S. (2007). Face and palmprint feature level fusion for single sample biometric recognition. Nerocomputing. 70(7), 1582 -1586.

If you have access to this article please login to view the article or kindly login to purchase the article

Single Article

	North Americas,UK, Middle East,Europe		India	Rest of world
	USD	EUR	INR	USD-ROW
Online	15	15

ADD TO CART

Options for accessing this content:

If you would like institutional access to this content, please recommend the title to your librarian.
If you already have i-manager's user account: Login above and proceed to purchase the article.
New Users: Please register, then proceed to purchase the article.