Personal Authentication Using FDOST in Finger Knuckle-Print Biometrics

Authors: N. B. Mahesh Kumar, K. Premalatha

Abstract:

The inherent skin patterns created at the joints in the finger exterior are referred as finger knuckle-print. It is exploited to identify a person in a unique manner because the finger knuckle print is greatly affluent in textures. In biometric system, the region of interest is utilized for the feature extraction algorithm. In this paper, local and global features are extracted separately. Fast Discrete Orthonormal Stockwell Transform is exploited to extract the local features. Global feature is attained by escalating the size of Fast Discrete Orthonormal Stockwell Transform to infinity. Two features are fused to increase the recognition accuracy. A matching distance is calculated for both the features individually. Then two distances are merged mutually to acquire the final matching distance. The proposed scheme gives the better performance in terms of equal error rate and correct recognition rate.

Keywords: Hamming distance, Instantaneous phase, Region of Interest, Recognition accuracy.

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1338138

References:

[1] A. Kumar, and C. Ravikanth, “Personal authentication using finger knuckle surface”, IEEE T INF FOREN SEC, vol. 4, no. 1, pp. 98-109, 2009.
[2] Z. Lin, Personal authentication using finger knuckle print, doctoral diss., The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong, 2011.
[3] Y. Wanknou, S. Changyin, and S. Zhongxi, “Finger-Knuckle-Print Recognition Using Gabor Feature and OLDA”, in Proceedings of the 30th Chinese Control Conference (CCC ‘11), pp. 2975-2978, Yantai, July 2011.
[4] X. Jing, W. Li, C. Lan, Y. Yao, X. Cheng, and L. Han, “Orthogonal Complex Locality Preserving Projections based on Image Space Metric for Finger-Knuckle-Print Recognition”, in Proceedings of the International Conference on Hand-Based Biometrics (ICHB ‘11), pp. 1- 6, Hong Kong, November 2011.
[5] L. Zhang, L. Zhang, and D. Zhang, “Monogenic Code: A Novel Fast Feature Coding Algorithm with Applications to Finger-Knuckle-Print Recognition”, in Proceedings of the International Workshop on Emerging Techniques and Challenges for Hand Based Biometrics (ETCHB 10’), 2010.
[6] S. Neware, K. Mehta, and A.S. Zadgaonkar, “Finger Knuckle Identification using Principal Component Analysis and Nearest Mean Classifier”, International Journal of Computer Applications, vol. 70, no.9, pp. 18-23, 2013.
[7] K. Ito, T. Aoki, H. Nakajima, K. Kobayashi, and T. Higuchi, “A palmprint recognition algorithm using phase-only correlation”, IEICE T FUND ELECTR, vol. E91-A, no.4, pp.1023–1030, 2008.
[8] K. Ito, H. Nakajima, K. Kobayashi, T. Aoki, and T.Higuchi, “A fingerprint matching algorithm using phase-only correlation”, IEICE T FUND ELECTR, vol. E91-A, no.3, pp. 682–691, 2004.
[9] “Finger Knuckle-print Database”, http://www4.comp.polyu.edu.hk/~biometrics
[10] W.K. Kong, and D. Zhang, “Competitive coding scheme for palmprint verification”, in Proceedings of 17th International Conference on Pattern Recognition (ICPR ’04), vol. 1, pp. 520–523, August 2004.
[11] L.Zhang, L.Zhang, David Zhang, and H.Zhu, “Ensemble of local and global information for finger-knuckle print recognition”, PATTERN RECOGN, vol. 44, pp. 1990-1998, 2011.