Partial Fingerprint Image Enhancement using Region Division Technique and Morphological Transform


  • A. Ahmad Department of Electrical Engineering, University of Engineering and Technology, Taxila, Pakistan
  • I. Arshad Department of Electrical Engineering, University of Engineering and Technology, Taxila, Pakistan
  • G. Raja Department of Electrical Engineering, University of Engineering and Technology, Taxila, Pakistan


Fingerprints are the most renowned biometric trait for identification and verification. The quality of fingerprint image plays a vital role in feature extraction and matching. Existing algorithms work well for good quality fingerprint images and fail for partial fingerprint images as they are obtained from excessively dry fingers or affected by disease resulting in broken ridges. We propose an algorithm to enhance partial fingerprint images using morphological operations with region division technique. The proposed method divides low quality image into six regions from top to bottom. Morphological operations choose an appropriate Structuring Element (SE) that joins broken ridges and thus enhance the image for further processing. The proposed method uses SE “line†with suitable angle 𜃠and radius 𑟠in each region based on the orientation of the ridges. The algorithm is applied to 14 low quality fingerprint images from FVC-2002 database. Experimental results show that percentage accuracy has been improved using the proposed algorithm. The manual markup has been reduced and accuracy of 76.16% with Equal Error Rate (EER) of 3.16% is achieved.


A. Saini, “Image enhancement techniques for fingerprint imagesâ€,

International Journal of Emerging Trends and Technology in

Computer Science Communication and Information System

(IJETTCS), vol. 1, no. 3, pp. 215-217, 2012.

K. Gill, J. Ren, S. Marshall, S. Karthick and J. Gilchrist, “Qualityassured fingerprint image enhancement and extraction using hyper

spectral imaging†Proc. 4th IEEE Imaging for Crime Detection and

Prevention (ICDP), pp. 1-6, 2011.

M. Ashok, J. S. Devi and M. R. Bai, “An approach to noisy image

skeletonization using morphological methodsâ€, International

Journal of Scientific & Engineering Research, vol. 3, pp. 1-8.

K. V. Kale, R. R. Manza, V. T. Humbe and P. Deshmukh,

“Fingerprint image enhancement using morphological transformâ€

Proc. (GSPx) Signal Processing Conference, Santa Clara

Conventional Center, Santa Clara, California USA, 24-27 October,

C. Ryu, S. G. Kong, H. Kim, “Enhancement of feature extraction

for low-quality fingerprint images using stochastic resonanceâ€,

Pattern Recognition Letters, vol. 32, no. 2, pp. 107–113, 2011.

Q. Karimi-Ashtiani, “Robust technique for latent fingerprint image

segmentation and enhancementâ€, Proc. 15th IEEE International

Conference Image Processing (ICIP), pp. 1492-1495, 2008.

S. Yoont, J. Feng, and A. K. Jain, “Latent fingerprint enhancement

via robust orientation field estimationâ€, Proc. IEEE International

Joint Conference on Biometrics Compendium (IJCB), Washington,

DC, 11-13 Oct. 2011, pp. 1-8, 2011.

C. L. Deepika, A Kandaswamy, C. Vimal, and B. Sathish,

“Invariant feature extraction from fingerprint biometric using

pseudo zernike momentsâ€, International Journal of Computer

Communication and Information System, vol. 2, no.1, pp. 1-5,

Z. Yong, H. Waibin, H. Zhike, “An enhancement algorithm based

on fingerprint image with signatureâ€, Proc. IEEE Information

Technology and Applications, pp. 324-326, 2010.

P. Das, K. Karthik , B. C. Garai, “A robust alignment-free

fingerprint hashing algorithm based on minimum distance graphsâ€,

Pattern Recognition Letters, vol. 45, no. 9, pp. 3373–3388, 2012.

A. R. Patel, M. A. Zaveri, “A novel approach for fingerprint

matching using minutiaeâ€, Proc. IEEE fourth Asia International

Conference on Mathematical/Analytical Modelling and Computer

Simulation, pp.1-6, 2010.

P. Li, X. Yang, K. Cao, X. Tao, R. Wang, J. Tian, “An alignment

free-fingerprint cryptosystem based on fuzzy vault schemeâ€,

Journal of network and computer applicationsâ€, vol 33, pp. 207-

, 2010.

F. Romdhane, F. Benzarti, H. Amiri, “Fingerprint images

enhancement using diffusion tensorâ€, Proc. IEEE International

Conference on Electrical Engineering and Software Applications,

pp. 1-6, 2013.

C. Raffaele, D. Maio and D. Maltoni, "Semi-automatic

enhancement of very low quality fingerprints", Proc. 6th

IEEE International Symposium on Image and Signal

Processing and Analysis, (ISPA), Salzburg, 16-18 Sept.,

, pp. 678 – 683, 2009.

S. Malathai and C. Meena, “An efficient approach for partial

fingerprint matching based on sift and pore features using fusion

methodâ€, Int. J. of Inf. and Commun. Technol., volume 2, no. 5,

pp. 8-12, 2012.

R. C. Gonzalez and R. E. Woods, “Digital Image Processingâ€, 2nd

Edition, 2002..

F. Shafaita, D. Keysersa and T. M. Breuelb, “Efficient

implementation of local adaptive thresholding techniques using

integral imagesâ€, Proc. 15th Document Recognition and Retrieval

Conference part of the IS&T-SPIE Electronic Image Symposium,

pp.1-6, 2008..

H. Guan, A. M. Dienstfrey and M. F. Theofanos, “A new metric

for latent fingerprint image processingâ€, Proc. IEEE Conference on

Computer Vision and Pattern Recognition, pp.1-8, 2013..




How to Cite

A. Ahmad, I. Arshad, and G. Raja, “Partial Fingerprint Image Enhancement using Region Division Technique and Morphological Transform”, The Nucleus, vol. 52, no. 2, pp. 63–70, May 2015.




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