Clonal Selection Algorithm for Low Quality Fingerprint Image Verification


  • Siti ‘Aisyah Sa’dan
  • Rajeswari Raju
  • Nursuriati Jamil
  • . .





Biometrics, Clonal Selection Algorithm, CSA, Fingerprint Verification, Forensic Science


Fingerprint verification has drawn a lot of attention to its approach in biometric since it is one of the most important biometric technologies nowadays and is widely used in several different applications and areas. It is applied in the forensic science area in order to identify people who were involved in criminal scenes such as the victims and the suspects. A human’s fingerprint is unique and usually has its own patterns and ridges, which differs them from other’s fingerprints. However, there are some drawbacks that can cause low accuracy and low performance of the verification. This occurs when the fingerprint images used are of low-quality and the fingerprints may be slightly incomplete (partial). Clonal Selection Algorithm (CSA) is known to be good in pattern matching and optimization of problems. Hence, this paper discusses the finding of the implementation of CSA in fingerprint verification. There were two main processes involved, which are features extraction using minutiae-based method and also the implementation of the CSA algorithm. Study shows that the FNMR result is 33.33% and the FMR is 16.67%. Further studies can be carried out by using the same algorithm, but focusing more on the feature extraction methods to improve the extraction of fingerprints.




[1] Patel, H., and Sharma, V.: ‘Fingerprint Recognition by Minutiae Matching’, International Journal of Engineering Trends and Technology (IJETT), 2013, 4, (2136-2140)

[2] Vatsa, M., Singh, R., Gupta, P., and Kaushik, A.K.: ‘Biometric Technologies’ (2005. 2005).

[3] Ravi, J., Raja, K.B., and Venugopal, K.R.: ‘Fingerprint Recognition Using Minutia Score Matching’, CoRR, 2010, abs/1001.4, (2), pp. 35-42.

[4] Prasad, D.R.S., Al-Ani, M.S., and Nejres, S.M.: ‘An Efficient Approach for Fingerprint’, 2016, (April 2015).

[5] Hong, L., Wan, Y., and Jain, A.: ‘Fingerprint image enhancement: algorithm and performance evaluation’, IEEE Transactions on Pattern Analysis and Machine Intelligence, 1998, 20, pp. 777-789.

[6] Naja, M.I., and Rajesh, R.: ‘Fingerprint image enhancement: algorithm and performance evaluation’, International Journal of Innovative Research in Computer and Communication Engineering, 2015, 3, (1).

[7] Houck, M.M., Initiative, F.S., and Rood, E.: ‘Biometrics and Forensic Science What’s the Difference?’, n.d.

[8] Meuwly, D., and Veldhuis, R.: ‘Forensic biometrics: From two communities to one discipline’, 2012, pp. 1-12.

[9] Isa, N., Sabri, N.M., Jazahanim, K.S., and Taylor, N.K.: ‘Application of the Clonal Selection Algorithm in Artificial Immune Systems for Shape Recognition’, 2010, pp. 223-228.

[10] U. S. D. o. Justice. (3 May). U.S. Marshals Service. Available:

[11] Zhong, Y., and Peng, X.: ‘SIFT-Based Low-Quality Fingerprint LSH Retrieval and Recognition Method’, International Journal of Signal Processing, Image Processing and Pattern Recognition, 2015, 8, (8), pp. 263-272.

[12] Raja K. B., Auksorius E., Raghavendra R., Boccara A. C., and Busch. C.: ‘Robust Verification With Subsurface Fingerprint Recognition Using Full Field Optical Coherence Tomography’, Computer Vision and Pattern Recognition Workshops (CVPRW), 2017 IEEE Conference on, 2017

[13] Wang, X., Deshpande, A.S., Dadi, G.B., and Salman, B.: ‘Application of Clonal Selection Algorithm in Construction Site Utilization Planning Optimization’, Procedia Engineering, 2016, 145, pp. 267-273.

[14] Thai, L.H., and Tam, H.N.: ‘Fingerprint recognition using standardized fingerprint model’, 2010, 7, (3).

[15] Alijla B. O., Saad M., and Issawi S. F.: ‘Neural Network-based Minutiae Extraction for Fingerprint Verification System’, Information Technology (ICIT), 2017 8th International Conference, 2017.

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How to Cite

‘Aisyah Sa’dan, S., Raju, R., Jamil, N., & ., . (2018). Clonal Selection Algorithm for Low Quality Fingerprint Image Verification. International Journal of Engineering & Technology, 7(4.42), 157–160.
Received 2019-01-11
Accepted 2019-01-11
Published 2018-12-29