MUL-TIBIOMETRIC PATTERN RETRIEVAL USING INDEX CODE TO IMPROVE RESPONSE TIME
In a biometric identification system, the identity corresponding to the input data (probe) is typically determined by comparing it against the templates of all identities in a database (gallery). Exhaustive matching against a large number of identities increases the response time of the system and may also reduce the accuracy of identification. Onaway to reduce the response time is by designing biometric templates that allow for rapid matching, as in the case of Iris Codes. An alternative approach is to limit the number of identities against which matching is performed based on criteria that are fast to evaluate. We propose a method for generating fixed-length codes for indexing biometric databases. An index code is constructed by computing match scores between a biometric image and a fixed set of reference images. Candidate identities are retrieved based on the similarity between the index code of the probe image and those of the identities in the database. The proposed technique can be easily extended to retrieve pertinent identities from multimodal databases. Experiments on a chimeric face and fingerprint bimodal database resulted in an 84% average reduction in the search space at a hit rate of 100%. These results suggest that the proposed indexing scheme has the potential to substantially reduce the response time without compromising the accuracy of identification.
Anil K. Jain, Arun Ross Member, IEEE, and Salil Prabhakar, Member, “An Introduction to Biometric Recognition” IEEE transactions on circuits and systems for video technology, vol. 14, no. 1, January 2004. DOI: https://doi.org/10.1109/TCSVT.2003.818349
A. Mhatre, S. Palla, S. Chikkerur, and V. Govindaraju, “Efficient search and retrieval in biometric databases,” Biometric Technol.Human Identification II, vol. 5779, no. 1, pp. 265–273, 2005.
Ashish Mishra, Multimodal Biometrics it is: Need for Future Systems, International Journal of Computer Applications (0975 – 8887), Volume 3 – No.4, June 2010. DOI: https://doi.org/10.5120/720-1012
Arun Ross “An introduction to multibiometrics”, West Virginia University, Morgantown, WV 26506 USA firstname.lastname@example.org, http://www.csee.wvu.edu/˜ross, September 2007.
A. Gyaourova and A. Ross, “A coding scheme for indexing multimodal biometric databases,” in Proc.IEEE Computer Society Workshop on Biometrics at the Computer Vision and Pattern Recognition(CVPR) Conf., Miami, FL, Jun. 2009. DOI: https://doi.org/10.1109/CVPRW.2009.5204311
Aglika Gyaourova, Arun Ross “Index codes for Multi-biometric pattern retrieval”, IEEE transactions oninformation forensics and security,Vol.7,No.2,April 2012. DOI: https://doi.org/10.1109/TIFS.2011.2172429
Bir Bhanu, Fellow, IEEE, and Xuejun Tan, Student Member, Fingerprint Indexing Based on Novel Features of Minutiae Triplets IEEE transactions on pattern analysis and machine intelligence, vol. 25, no. 5, may 2003. DOI: https://doi.org/10.1109/TPAMI.2003.1195995
B. Takács, “Comparing face images using the modified Hausdorff distance, Pattern Recognition”., vol. 31, no. 12, pp. 1873–1881, 1998.
K.-H. Lin, K.-M. Lam, X. Xie, and W.-C. Siu, “An efficient human face indexing scheme using eigenfaces,” in Proc. Int. Conf. Neural Networks And Signal Processing, Dec. 2003, vol. 2, pp. 920–923.
Lin Hong and Anil Jain, Fellow, “Integrating Faces and Fingerprints for Personal Identification” IEEE transactions on pattern analysis and machine intelligence, vol. 20, no. 12, December 1998. DOI: https://doi.org/10.1109/34.735803
Lihong Zheng and Xiangjian , “Classification Techniques in Pattern Recognition” Faculty of IT, University of Technology, Sydney PO Box 123, Broadway NSW 2007, Sydney, Australia.
Lan;shi-Qiang Gao;Hui Tang;Xiao-Yuan Jing,” Multi-Modal Biometrics Pixel Level Fusion and KPCARBF Feature Classification for Single Sample Recognition Problem “,Image and signalprocessing,2009,CISP’09.
R. S. Germain, A. Califano, and S. Colville. Fingerprint matching using transformation parameter clustering. IEEE Computational Science & Engineering, 4(4):42–49, 1997. DOI: https://doi.org/10.1109/99.641608
A new signature veri®cation technique based on a two-stage neural network classi®er, H. Baltzakis , N. Papamarkos
Offline signature verification and recognition by support vector machine Emre Özgündüz,Tülin Şentürk and M. Elif KarslıgilComp.Engg Department, Yıldız Technical University Yıldız , Istanbul,
Off-line Signature Verification Based on Fusion of Grid and Global Feat Ures.Using Neural Networks shashi kumarDR1,,K BRaja2,RK Chho taray3 Sabyasachi Pattanaik41Deptof CSE, C IOT , Bangalore 2Dept of ECE,University VCOE, Bangalore University, Bangalore 3Dept of CSE, SECM,Orissa 4Department of Computer Science, F.M. University, Balasore, Orissa
Handwritten Signature Verification using Instance Based Learning Priya Metri , Ashwinder Kaur Department of Computer Engineering MIT COE , Pune
Aha, 1989] D. W. Aha. Incremental, instance-based learning of independent and graded concept descriptions. In Sixth International Workshop on Machine Learning, Detroit, MI, 1989. Morgan Kaufmann. DOI: https://doi.org/10.1016/B978-1-55860-036-2.50098-9
Md. It rat Bin Shams, “Signature Recognition by Segmentation and Regular Line Detection” TENCON 2007 -2007 IEEE Region 10 Conference Volume, Issue,Page(s):1 – 4, Oct. 30, 2007- Nov. 2, 2007. DOI: https://doi.org/10.1109/TENCON.2007.4428999
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