Finger Vein Image Enhancement Technique based on Gabor filter and Discrete Cosine Transform

Boucherit Ismail, Ould Zmirli Mohamed

Abstract


Biometrics is a global technique to establish the identity of a person by measuring one of their physical or behavioral characteristics such as fingerprint, signature, iris, voice and face. Compared to these biometric techniques, the finger vein technique has distinct advantages as it helps to protect privacy and anonymity in automated individual recognition. Many studies showed that the finger vein images were of a low quality because of the variation in the tissues and uneven illumination. Hence, there is a need for effective image enhancement techniques, which can improve the quality of the images. In this study, we proposed a novel technique, which enhances the image quality of the finger veins. This method includes contrast amelioration, use of Gabor filters and image fusion, which generates an image with highly connective patterns. We used three criteria to evaluate the quality of processed images, the mean of grey values, the image entropy, and the image contrast. The obtained result shows higher values when using our approach in comparison to the baseline methods considered in this work.

Keywords


Enhancement Method; Finger-vein; Gabor Filter; Image Processing;

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References


X. Yan , and al ,“Palm vein recognition based on multi-sampling and feature-level fusion,” Neuro.comput . Elsevier, vol. 151, pp. 798–807, Oct. 2015.

Y. Lu, and al ,“Efficient descriptor of histogram of salient edge orientation map for finger vein recognition,” Applied Optics. vol. 53, no. 20, pp. 4585–4593, Jul. 2014.

P. Gupta and P. Gupta , “ Multi-modal fusion of palm-dorsa vein pattern for accurate personal authentication,”. Knowledge-Based Systems. Elsevier, vol. 81, pp. 117–130, Mar. 2015.

A. Kumar, Y. Zhou, “Human Identification using Finger Images,” IEEE Trans. Image Processing, vol. 21, no. 4, pp. 2228–2244, Apr. 2012.

Puneet. Gupta, Phalguni. Gupta, “ An accurate finger vein based verification system ,” Digital Signal Processing. Elsevier, vol. 38, pp. 43–52, Dec. 2015.

T. Eiwei, and M. Z. Ibrahim , “ A Review of Finger Vein Recognition System,” Journal of Telecommunication, Electronic and Computer Engineering (JTEC) . vol. 10, no. 1–9, pp. 167–171. 2018.

C. Yu, D. Zhang, H. Li and F. Zhang, “Finger-vein image enhancement based on muti-threshold fuzzy algorithm,” 2nd International Congress on Image and Signal Processing, CISP'09, pp. 1. 2009.

C. Mei, X. Xiao, G. Liu and Y. Chen, “Feature extraction of fingervein image based on morphologic algorithm,”. 6th International Conference on Fuzzy Systems and Knowledge Discovery, pp. 407– 411.2009.

J. Zhou, H. Tian, W. Xu and X. Li, “A New Approach to Hand Vein Image Enhancement”, 2nd International conference on Intelligent computation Technology and Automation, vol. 1, pp. 499.Oct. 2009.

J. Yang, J. Yang, “Multi-Channel Gabor Filter Design for FingerVein Image Enhancement,” 5th International Conference on Image and Graphics ,pp. 87. 2009.

J. Zhang, J. Yang, “Finger-Vein Image Enhancement Based on Combination of Gray-Level Grouping and Circular Gabor Filter ,” International Conference on Information Engineering and Computer Science, pp.1–4.2009.

J. Zhou, A. Cun, and M. Do, “Nonsubsampled contourlet transform: construction and application in enhancement,” IEEE International conference on Image Processing, Genoa, Italy, vol. 1, pp. 1,Sept. 2005.

T. Phan, A. Truc and Y. Lee, S. Lee and T. Kim, “Vessel enhancement filting using directional filter bank,” Computer Vision and Image Understanding, vol. 113, no. 1, pp.101–112, 2009.

J. Yang, and M. Yan, “An Improved Method for Finger-vein Image Enhancement” 10th International conference on Signal Processing pp. 1706, Oct. 2010.

Y.H. Park and K.R. Park,“ Image quality enhancement using the direction and thickness of vein lines for finger-vein recognition,” Int. J. Adv. Rob. Syst. vol. 9, no. 4, pp. 1–10, May. 2017.

D.T Nguyen, Park, Y.H. and all , “New finger-vein recognition method based on image quality assessment,” KSII Trans. Int. Inf. Syst. vol. 7, no. 2, pp. 347–365, Feb. 2013.

E. Lee, H. Jung, and D. Kim, “New finger biometric method using near infrared imaging,” Sensors. 11, 2319–2333. vol. 11, no. 3, pp. 2319–2333. 2011.

M. S. M. Asaari, S. A. Suandi, B. A. Rosdi, “Fusion of Band Limited Phase Only Correlation and Width Centroid Contour Distance for finger based biometrics,” Expert Systems with Applications Elsevier vol. 41, no.7, pp. 3367–3382, 2014.

L. Caixia, “ The Research on Finger Vein Image Preprocessing Based on Mathematical Morphology,” College of Information Science and Engineering, Zaozhuang University, China, Springer-Verlag London Limited , 2012.

K. Wang, Z. Yuan, “ Finger vein recognition based on wavelet moment fused with PCA transform,” J Pattern Recognition and Artificial Intelligence, Chinese, . vol. 20, no. 7, pp. 692–697, 2007.

Zuiderveld, Karel, “Contrast Limited Adaptive Histograph Equalization,” Graphic Gems IV. San Diego: Academic Press Professional, pp. 474–485, 1994.

S. Khellat-Kihel, and all, “Finger vein recognition using Gabor filter and SupportVector Machine,” in IPAS’14 International Image Processing Applications and Systems Conf IEEE, pp. 1–6, 2014.

M. B. A. Haghighat, A. Aghagolzadeh , and H. Seyedarabi, “Multifocus image fusion for visual sensor networks in DCT domain,” Computers and Electrical Engineering , Elsevier . vol.37, no.5, pp. 789–797, 2011.

A. A Abbood, M. S. H. Al-Tamimi and G. Sulong, “New Combined Technique for Fingerprint Image Enhancement,”. Modern Applied Science. vol.11, no.1, pp. 222–234, 2016.

Y. Lu, G. P. Yang, Y. L. Yin, and R. Y. Xiao, “Finger vein image quality evaluation using support vector machines,” Optical Engineering, vol.52, no. 2, pp. 027003-1 – 027003-9,. 2017.

M.S.M. ASAARI, S.A. SUANDI, and R.B. Affendi. “Fusion of band limited phase only correlation and width centroid contour distance for finger based biometrics,”. Expert Systems with Applications, vol. 41, no 7, pp. 3367-3382, Jun. 2014.

YI. Yilong, L. Lili, and S. Xiwei. “SDUMLA-HMT: a multimodal biometric database,”. In : Chinese Conference on Biometric Recognition. Springer, Berlin, Heidelberg, pp. 260–268, 2011.


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ISSN: 2180-1843

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