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Adaptive and Faster Approach to Fingerprint Minutiae Extraction and Validation
Iwasokun gabriel Babatunde, Akinyokun, Oluwole Charles, Alese Boniface kayode, Olabode Olatunbosun
Pages - 414 - 424     |    Revised - 01-09-2011     |    Published - 05-10-2011
Volume - 5   Issue - 4    |    Publication Date - September / October 2011  Table of Contents
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KEYWORDS
AFIS, Pattern Recognition, Pattern Matching, Fingerprint, Post-Processing Algorithm, Crossing Number
ABSTRACT
Fingerprint has remained a very vital index in the field of security where series of Automatic Fingerprint Identification System (AFIS) have been developed for human identification. Many of these systems involve matching each of the features of a template image with each of the features in the feature sets in the reference database to determine the level of match between the template and the reference images. Matching is done on the basis of preset parameters such as feature type, location, orientation and so on. Obtaining the features from the template image and for building a reference database involves the implementation of a sound fingerprint feature detection and extraction algorithm. In this paper, the process of detecting and extracting false and valid features contained in a fingerprint image is discussed. Some of the existing fingerprint features extraction algorithms were firstly modified and the resulting algorithms were implemented. The implementation was carried out in an environment characterized by Window Vista Home Basic as platform and Matrix Laboratory (MatLab) as frontend engine. Fingerprints images of different qualities obtained from the manual (ink and paper) and electronic (fingerprint scanner) methods were used to test the adequacy of the resulting algorithms. The results obtained show that only valid and true minutiae points were extracted from the images.
CITED BY (31)  
1 Iwasokun, G. B., & Akinyokun, O. C. Singular-minutiae points relationship-based approach to fingerprint matching.
2 Iwasokun, G. B., Udoh, S. S., & Akinyokun, O. K. (2015). Multi-Modal Biometrics: Applications, Strategies and Operations. Global Journal of Computer Science and Technology, 15(2).
3 Ahmad, F., Darbari, M., & Asthana, R. (2015). Different Approaches of Soft Computing Techniques (Inference System) which are used in Clinical Decision Support System for Risk based Prioritization. Asian Journal of Computer and Information Systems, 3(1).
4 Narayanan, R. C. Reduction of False Acceptance Rate Using Cross Validation for Fingerprint Recognition Biometric System.
5 Chowdhury, C. R., & Saha, B. (2015). Efficient Fingerprint Matching Based Upon Minutiae Extraction. International Journal of Advanced Computer Research, 5(21), 347.
6 Patel, A. (2015). Risk based prioritization of Asthma Burden Using Artificial Neuro Fuzzy Inference System.
7 Babatunde, I. G. (2015). Fingerprint Matching Using Minutiae-Singular Points Network. International Journal of Signal Processing, Image Processing and Pattern Recognition, 8(2), 375-388.
8 Babatunde, I. G., Lange, M. J., Charles, A. O., & Olumuyiwa, D. J. (2014, August). Experimental study of thumbprint-based authentication framework for ATM machines. In Science and Information Conference (SAI), 2014 (pp. 505-514). IEEE.
9 Bifari, E. N., & Elrefaei, L. (2014, September). Automated Fingerprint Identification System based on weighted feature points matching algorithm. In Advances in Computing, Communications and Informatics (ICACCI, 2014 International Conference on (pp. 2212-2217). IEEE.
10 Akinyokun Oluwole, C., Alese Boniface, K., & Iwasokun Gabriel, B. (2014). Fingerprint Matching Using Spatial Characteristics. In Proceedings of the World Congress on Engineering (Vol. 1).
11 Iwasokun, G. B., Akinyokun, O. C., & Dehinbo, O. J. (2014). Fingerprint Matching Using Features Mapping. In Proceedings of the World Congress on Engineering and Computer Science (Vol. 1).
12 Iwasokun, G. B., & Ojo, S. O. (2014). Review and Evaluation of Fingerprint Singular Point Detection Algorithms. British Journal of Applied Science & Technology, 4(35), 4918.
13 Iwasokun, G. B., & Akinyokun, O. C. False Minutiae’Impact on Fingerprint Matching.
14 Babatunde, I. G., Charles, A. O., & Olusegun, O. S. (2014). An Investigation into the Impact of False Minutiae Points on Fingerprint Matching. International Journal of Database Theory and Application, 7(3), 159-178.
15 Babatunde, I. G. Review and Evaluation of Fingerprint Singular Point Detection Algorithms.
16 Iwasokun, G. B., Akinyokun, O. C., & Angaye, C. O. (2014). Spatial Relation Approach to Fingerprint Matching. In Intelligent Systems for Science and Information (pp. 87-110). Springer International Publishing.
17 Sahu, M., & Shukla, N. Rotation Invariant Approach of SURF for Unimodal Biometric System.
18 Gabriel, I., Charles, A. O., & Officer, A. C. (2013). Fingerprint Matching using Neighbourhood Distinctiveness.
19 Babatunde, I. G., & Charles, A. O. (2013). A Fingerprint-based Authentication Framework for ATM Machines. J Comput Eng Inf Technol 2: 3. doi: http://dx. doi. org/10.4172/2324, 9307, 2.
20 Babatunde, I. G., Charles, A. O., & Officer, A. C. (2013, October). Fingerprint matching by neighbourhood characteristics. In Science and Information Conference (SAI), 2013 (pp. 434-442). IEEE.
21 Angaye, C. O., Akinyokun, O. C., & Iwasokun, g. b. experimental study of minutiae based algorithm for fingerprint matching.
22 Han, S. W., Jeoune, D. S., & Yoon, Y. W. (2013). New Minutiae Detection Algorithm from Fingerprint Image using the Improved Tracing on Ridge Curve. International Journal of Security and Its Applications, 7(4), 343-352.
23 Babatunde, I. G., Charles, A. O., & Olatunbosun, O. (2013). Uniformity Level Approach to Fingerprint Ridge Frequency Estimation. International Journal of Computer Applications, 62(22).
24 Jain, N., Sharma, S. K., & Pal, B. L. (2013). Development of a Multimodal Biometric Identification and Verification System using Two Fingerprints. International Journal of Computer Applications, 74(12).
25 Peng xi, peng xiao-qi, zhong yunfei, tang ying, & left the dragon. (2013). extraction of minutiae identity detection method.computer applications and software, 30 (6), 132-136.
26 Subha, M., & Vanithaasri, S. (2012). A study on authenticated admittance of ATM Clients using biometrics based cryptosystem. International Journal of Advances in Engineering & Technology, 4(2), 456-463.
27 Babatunde, I. G., Charles, A. O., & Olatubosun, O. (2012). A Block Processing Approach to Fingerprint Ridge-Orientation Estimation. Computer Technology and Application, 3(6).
28 Iwasokun, G. B., Akinyokun, O. C., & Dehinbo, O. J. Minutiae Inter-Distance Measure for Fingerprint Matching.
29 Akinyokun, o. c. strategy for software knowledge incubation and skill development for nigerian students.
30 Pradhan, M. P., & Ghose, M. K. (2012). automatic techniques for identification of minutiae’s in morphology study of terrain. European Scientific Journal, 8(16).
31 Babatunde, I. G., Charles, A. O., & Olatunbosun, O. A Mathematical Modeling Method for Fingerprint Ridge Segmentation and Normalization.
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Mr. Iwasokun gabriel Babatunde
Federal University of Technology - Nigeria
maxtunde@yahoo.com
Mr. Akinyokun, Oluwole Charles
- Nigeria
Dr. Alese Boniface kayode
Federal University of Technology, Akure - Nigeria
Dr. Olabode Olatunbosun
Federal University of Technology, Akure - Nigeria


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