NOISE-ROBUST HAND BASED MULTIMODAL BIOMETRIC SYSTEM USING ICA FEATURES

Supreetha Gowda HD, Mohammad Imran, Hemantha Kumar G

Abstract


Achieving performance and robustness simultaneously for an automated biometric system is crucial to prove that it is robust to real time noise encounters which determines the expected behavior of the system with the employed feature extraction algorithms, feature selection rules, classiers. We have evaluated our system on two hand based modalities such as palmprint and handvein. The robustness is checked and the systems security level is explored on both unimodal and multimodal (pre and post classication fusion techniques) systems by tabulating the comparative results got from clean and noisy data (Gaussian, salt and pepper and speckle noise) under the standard benchmark threshold values 0.01%, 0.1%, 1%.

Keywords


Hand-based robust; multimodal system ; ICA ; Corrupted ;noise.

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References


M. Bellaaj, R. Boukhris, A. Damak and D. Sellami, "Possibilistic modeling palmprint and ngerprint based multimodal biometric recognition system," 2016 International Image Processing, Applications and Systems (IPAS), Hammamet, 2016, pp. 1-8.

S. Gunasekar, J. Ghosh and A. C. Bovik, "Face Detection on Distorted Images Augmented by Perceptual Quality-Aware Features," in IEEE Transactions on Information Forensics and Security, vol. 9, no. 12, pp. 2119-2131, Dec. 2014.

D. Miao, Z. Sun and Y. Huang, "Fusion of Multibiometrics Based on a New Robust Linear Programming," 2014 22nd International Conference on Pattern Recognition, Stockholm, 2014, pp. 291-296.

Yi Rong, Shengwu Xiong, Yongsheng Gao, Low-rank double dictionary learning from corrupted data for robust image classification, Pattern Recognition, Volume 72, 2017, Pages 419-432, ISSN 0031-3203

C. Liu, R. Szeliski, S. Bing Kang, C. L. Zitnick and W. T. Freeman, "Automatic Estimation and Removal of Noise from a Single Image," in IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 30, no. 2, pp. 299-314, Feb. 2008.

N. McLaughlin, J. Ming and D. Crookes, "Robust Multimodal Person Identication

With Limited Training Data," in IEEE Transactions on Human-Machine Systems,

vol. 43, no. 2, pp. 214-224, March 2013.

S. L. Fernandes, G. J. Bala, P. Nagabhushan and S. K. Mandal, "Robust Face

Recognition in the Presence of Noises and Blurring Eects by Fusing Appearance

Based Techniques and Sparse Representation," 2013 2nd International Conference

on Advanced Computing, Networking and Security, Mangalore, 2013, pp. 84-89.

C. Kauba and A. Uhl, "Robustness Evaluation of Hand Vein Recognition Systems,"

International Conference of the Biometrics Special Interest Group (BIOSIG),

Darmstadt, 2015, pp. 1-5.

J. Dai, J. Feng and J. Zhou, "Robust and Ecient Ridge-Based Palmprint Match-

ing," in IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 34,

no. 8, pp. 1618-1632, Aug. 2012.

Le qing Zhu and San yuan Zhang. Multimodal biometric identication system

based on nger geometry, knuckle print and palm print. Pattern Recognition Letters,

(12):16411649, 2010.

Mayank Vatsa, Richa Singh, Afzel Noore, and Arun Ross. On the dynamic selection

of biometric fusion algorithms. IEEE Transactions on Information Forensics and

Security, 5(3):470479, 2010




DOI: https://doi.org/10.26483/ijarcs.v9i3.5920

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