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Quad-search: Modified Binary Search Algorithm for Efficient Fingerprint Biometrics Identification in Multicore Architecture

Oriola Oluwafemi, Orimoloye Segun Michael. Published in Algorithms

International Journal of Applied Information Systems
Year of Publication: 2016
Publisher: Foundation of Computer Science (FCS), NY, USA
Authors: Oriola Oluwafemi, Orimoloye Segun Michael
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  1. Oriola Oluwafemi and Orimoloye Segun Michael. Quad-search: Modified Binary Search Algorithm for Efficient Fingerprint Biometrics Identification in Multicore Architecture. International Journal of Applied Information Systems 10(10):28-32, May 2016. URL, DOI BibTeX

    	author = "Oriola Oluwafemi and Orimoloye Segun Michael",
    	title = "Quad-search: Modified Binary Search Algorithm for Efficient Fingerprint Biometrics Identification in Multicore Architecture",
    	journal = "International Journal of Applied Information Systems",
    	issue_date = "May 2016",
    	volume = 10,
    	number = 10,
    	month = "May",
    	year = 2016,
    	issn = "2249-0868",
    	pages = "28-32",
    	numpages = 5,
    	url = "",
    	doi = "10.5120/ijais2016451559",
    	publisher = "Foundation of Computer Science (FCS), NY, USA",
    	address = "New York, USA"


Linear Search Algorithm is often applied to Fingerprint Biometrics Identification in traditional computing. With the advent of multicore systems, more attention must be placed on Binary Search Algorithms for efficient Fingerprint Biometric Identification in large databases. However, Binary Search Algorithms can conveniently be parallelized for two cores. Therefore, this study assumes that a variant of Binary Search Algorithm, which supports multi-parallelization, will take better advantage of advances in multicore systems and improve efficiency of Biometric Identification. This paper hence presents a Modified Binary Search algorithm known as Quad-search for Biometric Identification. The Fingerprint Identification Algorithm is developed by splitting the fingerprint array at the midpoint and parallelizing the search in each split to form four sub-arrays. To evaluate the proposed algorithm, a real-life Fingerprint Biometric Identification System of tested fingerprint images is used. The execution time of the Modified Binary Search Algorithm is compared with both Binary Search and Linear Search Algorithms in an Intel Quadcore Architecture. The results show that at near, middle, and far fingerprint positions in the database, the Modified Search Algorithm is executed at lower time compared to both the Parallelized Binary Search Algorithm and Linear Search Algorithm, but the parallelized Binary Search Algorithm executes at lower time at mid fingerprints locations compared to Linear Search Algorithm. The running time is estimated as O(Log(logN)) The Modified Binary Search Algorithm resulted in efficient Fingerprint Biometrics Identification in multicore architecture.


  1. Baba, K. and Egawa S. 2013, “A data structure for efficient biometric identification,” in Proc. of the 2013 International Conference on Information and Communication Technology (ICT-EurAsia’13), Yogyakarta, Indonesia, LNCS, vol. 7804. Springer-Verlag, March 2013, pp. 528–533.
  2. Baba, K. and Egawa, S. 2013. Journal of Wireless Mobile Networks, Ubiquitous Computing, and Dependable Applications, volume: 4, number: 2, pp. 97-103
  3. Bayram, S. 2012. Applications of Multimedia Forensics. PhD Thesis. Polytechnic Institute of New York University, United States of America.
  4. Bayram, S., Sencar, H. T. and Memon, N. 2014. Sensor Fingerprint Identification Through Composite Fingerprints and Group Testing IEEE Transactions on Information Forensics and Security .Volume 10, Issue 3.
  5. Jain, A.K., Prabhakar, S., Hong, L. and Pankanti, S.2000. Filterbank-based fingerprint matching. IEEE Trans on Image Processing, 2000, 9(5): 846-859.
  6. Maeda, T., Matsushita, V., and Sasakawa, K. 2001. Identification algorithm using a matching score matrix. IEICE Transactions on Information and Systems, vol. E84-D, no. 7, pp. 819–824, 2001.
  7. Meersman, R., Tari, Z., and Herrero, P. 2006. An Efficient Algorithm for Fingercode-Based Biometric Identification OTM Workshops 2006, LNCS 4277, pp. 469–478, 2006.
  8. Pan, Z., Kotani, K., and Ohmi, T., 2005. Improved fast encoding method for vector quantization based on subvector technique. IEEE International Symposium on Circuits and Systems, 2005: 6332-6335.
  9. Pan, Z., Kotani, K., and Ohmi. 2004. “A memory-efficient fast encoding method for vector quantization using 2-pixel-merging sum pyramid”, 2004 IEEE Int’l Conf on Acoustics, Speech and Signal Processing, 2004, 3: 669-672.
  10. You, J., Li, W., and Zhang, D. 2001. Hierarchical palmprint identification via multiple feature Extraction. Pattern Recognition 35 (2002) 847–859.


Biometrics, Fingerprint, Identification, Modified Binary Search, Multi-parallelized