Fingerprint Authentication using Shark Smell Optimization Algorithm
DOI:
https://doi.org/10.21928/uhdjst.v4n2y2020.pp28-39Keywords:
Fingerprint Authentication, Feature Extraction, Swarm Intelligent, Shark Smell Optimization, ZKT eco deviceAbstract
Recognition of people relying on biometric characteristics is a common phenomenon in our society. It has increased in recent years in most areas of life such as government, department, companies, and banks. Fingerprint identification is one of the most common and credible personal biometric identification methods. Extracting features are one of the most important steps in the fingerprint identification; the strength of any system depends mainly on this step, where whenever the features obtained are accurate whenever the identification process is more accurate. Therefore, an effective and efficient method must be used to extract the features. This paper solved two main problems that were (1) improving security by designing and implementing an accurate, efficient, and fast authentication system for the identification and verification process using an intelligent algorithm to extract the best features from the fingerprint image and (2) evaluating the strength of the Shark Smell Optimization (SSO) in the search space with a chosen set of metrics. This paper aims to extract the best features of the fingerprint image using an algorithm that depends on nature for its movement and work; therefore, the SSO was used. In this paper, the SSO algorithm is used to extract the features. SSO is a new meta-heuristic algorithm that uses smart methods and random movements to get its prey. These methods and movements were used to extract features from the fingerprint image which will be used later for identification and verification process. The proposed method is implemented through four phases, namely, create a database to store and organize data, image pre-processing using median filter, feature extraction using SSO algorithm, and matching process using euclidean distance. The results demonstrated the strength, accurate, credible, and effectiveness of the algorithm used by applying it on (150) real fingerprint samples taken from university students, where the results of false acceptation rate, false rejection rate, and correct verification rate were 0.00, 0.00666, and 99.334%, respectively.
References
[2] C. Wang, Y. Wang, Y. Chen, H. Liu and J. Liu. “User authentication on mobile devices: Approaches, threats and trends”. Computer Networks, vol. 170, pp. 107-118, 2020.
[3] C. Rathgeb and A. Uhl. “A survey on biometric cryptosystems and cancelable biometrics”. EURASIP Journal on Information Security, vol 1, p. 3, 2011.
[4] E. Chandra and K. Kanagalakshmi. “Noise elimination in fingerprint image using median filter”. The International Journal of Advanced Networking and Applications, vol. 2, no. 6, pp. 950-955, 2011. Available from: http://www.citeseerx.ist.psu.edu/viewdoc/downlo ad?doi=10.1.1.419.1632&rep=rep1&type=pdf. [Last accessed on 2019 Nov 01].
[5] S. K. Sahoo, T. Choubisa and S. M. Prasanna. “Multimodal biometric person authentication: A review”. IETE Technical Review, vol. 29, no. 1, pp. 54-75, 2012.
[6] M. G. Abdul-Haleem, L. E. George and H. M. Al-Bayti. “Fingerprint recognition using haar wavelet transform and local ridge attributes only”. International Journal of Advanced Research in Computer Science Software Engineering, vol. 4, no. 1, 2014. Available from: https://www.researchgate.net/publication/335025795_Fingerprint_ Recognition_Using_Haar_Wavelet_transform_and_local_ridge_ attributes_only. [Last accessed on 2019 Sep 17].
[7] A. Çalışkan and Ö. F. Ertuğrul. “Wavelet Transform Based Fingerprint Recognition”. In 2015 23nd Signal Processing and Communications Applications Conference, pp. 1481-1484, 2015.
[8] U. H. L. Akter. “Speeded-up Robust Feature Extraction and Matching for Fingerprint Recognition”. 2nd International Conference on Electrical Engineering and Information and Communication Technology, 2015.
[9] V. H. Mahale, M. M. Ali, P. L. Yannawar and A. T. Gaikwad. “Fingerprint Recognition for Personal Identification and Verification Based on Minutiae Matching”. In: IEEE 6th International Conference on Advanced Computing, pp. 332-339, 2016.
[10] H. R. Su, K. Y. Chen, W. J. Wong and S. H. Lai. “A Deep Learning Approach Towards Pore Extraction for High-Resolution Fingerprint Recognition”. In: 2017 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 2057-2061, 2017.
[11] Z. J. Ahmed and L. E. George. “Fingerprints recognition using the local energy distribution over haar wavelet subbands”. International Journal of Science Research, vol. 6, no. 9, pp. 979-986, 2017.
[12] I. G. Dakhil and A. A. Ibrahim. “Design and implementation of fingerprint identification system based on KNN neural network”. Journal of Computer Communications, vol. 6, no. 3, pp. 1-18, 2018.
[13] A. Kyaw and Z. L. Aung. “A robust fingerprint recognition technique applying minutiae extractors and neural network”. International Journal of Engineering Research and Advanced Technology, vol. 5, no. 3, pp. 78-87, 2019.
[14] H. Kaur and H. S. Pannu. “Zernike moments-based fingerprint recognition using weighted-support vector machine”. Modern Physics Letters B, vol. 33, no. 21, pp. 1950245.
[15] M. Mavrovouniotis, C. Li and S. Yang. “A survey of swarm intelligence for dynamic optimization: Algorithms and applications”. Journal of Swarm and Evolutionary Computation, vol. 33, pp. 1-22, 2017.
[16] S. Mohammad-Azari, O. Bozorg-Haddad and X. Chu. “Shark smell optimization (SSO) algorithm”. In: Advanced Optimization by Nature-Inspired Algorithms, Springer, Berlin, pp. 93-103, 2018.
[17] M. Ehteram, H. Karami, S. F. Mousavi, A. El-Shafie and Z. Amini. “Optimizing dam and reservoirs operation based model utilizing shark algorithm approach”. Journal Knowledge-Based Systems, vol. 122, pp. 26-38, 2017.
[18] O. W. Salami, I. J. Umoh, E. A. Adedokun, M. B. Mu’azu and L. A. Ajao. “Efficient Method for Discriminating Flash Event from DoS Attack during Internet Protocol Traceback using Shark Smell Optimization Algorithm. In: 2019 2nd International Conference of the IEEE Nigeria Computer Chapter, pp. 1-10, 2019.
[19] H. Hosseinzadeh and M. Sedaghat. “Brain image clustering by wavelet energy and CBSSO optimization algorithm”. Journal of Mind and Medical Sciences, vol. 6, no. 1, pp. 110-120, 2019.
[20] N. Gnanasekaran, S. Chandramohan, P. S. Kumar and A. M. Imran. “Optimal placement of capacitors in radial distribution system using shark smell optimization algorithm”. Ain Shams Engineering Journal, vol. 7, no. 2, pp. 907-916, 2016.
[21] O. Abedinia, N. Amjady and A. Ghasemi. “A new metaheuristic algorithm based on shark smell optimization”. Complexity, vol. 21, no. 5, pp. 97-116, 2016.
[22] H. Hosseinzadeh. “Automated skin lesion division utilizing Gabor filters based on shark smell optimizing method”. Evolving Systems, 43, pp. 1-10, 2018.
[23] S. A. Juma. “Optimal Radial Distribution Network Reconfiguration Using Modified Shark Smell Optimization”. MSc. Thesis, Jkuat- Pausti. Available from: http://www.ir.jkuat.ac.ke/bitstream/ handle/123456789/4854/Shaibu_Ali_Juma_EE300-0012%20%20 17_Thesis%20Report.pdf?sequence=1&isAllowed=y.
[24] K. M. Sagayam, D. N. Ponraj, J. Winston, E. Jeba and A. Clara. “Authentication of biometric system using fingerprint recognition with euclidean distance and neural network classifier”. International Journal of Innovative Technology Exploring Engineering, vol. 8, no. 4, pp. 766-771, 2019.
[25] T. W. A. Khairi. “Secure mobile learning system using voice authentication”. Journal of Engineering Applied Sciences, vol. 14, no. 22, pp. 8180-8186, 2019.
