Improvement of the Identification Rate using Finger Veins based on the Enhanced Maximum Curvature Method using Morphological Operators

  • Sayyed Abbas Mousavizadeh Mobarakeh Master Student, Islamic Azad University, Mobarakeh Branch, Department of Electrical Engineering, Mobarakeh, Isfahan, Iran
  • Mehran Emadi Assistant Professor, Faculty of Electrical Engineering,Islamic Azad University, Mobarakeh Branch, Mobarakeh, Isfahan, Iran
Keywords: Identification, Biometrics, Finger veins, Infrared, Maximum curvature, Morphological operator

Abstract

All human biological traits are unique as biometrics, such as fingerprint, palm, iris, palm veins, finger veins and other biometrics. Using these biometrics has always been challenging. One of the challenges in biometrics is physical injuries. Finger vein biometrics is one of the characteristics that is most resistant to physical injuries. Numerous algorithms for authentication have been proposed with the help of this biometrics, which have weaknesses such as high computational complexity and low identification accuracy. In this paper, a new method in identification based on maximum curvature algorithm and morphological operators is proposed. The maximum curvature algorithm extracts image properties using a set of operations based on image returns. This process has been enhanced in the proposed method with morphological operators. What distinguishes the proposed method from other methods is that this algorithm is very accurate in distinguishing images which are similar but belonging to different classes. The proposed method, in addition to having a reasonable computational complexity, has been able to record very good identification accuracy in the challenge of low image quality. The identification accuracy of the proposed method is 97.5%, which compared to other methods has been able to improve more than 3%. Also, the identification speed of the proposed method is 0.84 seconds, which is very fast in its kind.

References

[1] K. W. Bowyer and P. J. Flynn, "Biometric identification of identical twins: A survey," in Biometrics Theory, Applications and Systems (BTAS), 2016 IEEE 8th International Conference on, 2016, pp. 1-8: IEEE.
[2] A. Uhl, C. Busch, S. Marcel, and R. Veldhuis, Handbook of vascular biometrics. Springer Nature, 2020.
[3] S. Aryanmehr, M. Karimi, and F. Z. Boroujeni, "CVBL IRIS Gender Classification Database Image Processing and Biometric Research, Computer Vision and Biometric Laboratory (CVBL)," in 2018 IEEE 3rd International Conference on Image, Vision and Computing (ICIVC), 2018, pp. 433-438: IEEE.
[4] M. Kono, H. Ueki, and S.-i. Umemura, "Near-infrared finger vein patterns for personal identification," Applied Optics, vol. 41, no. 35, pp. 7429-7436, 2002.
[5] D. P. Wagh, H. Fadewar, and G. Shinde, "Biometric Finger Vein Recognition Methods for Authentication," in Computing in Engineering and Technology: Springer, 2020, pp. 45-53.
[6] A. R. Khan et al., "Authentication through gender classification from iris images using support vector machine," Microscopy research and technique, 2021.
[7] P. H. Pisani et al., "Adaptive biometric systems: Review and perspectives," ACM Computing Surveys (CSUR), vol. 52, no. 5, pp. 1-38, 2019.
[8] E. Ting and M. 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.
[9] I. Qayoom and S. Naaz, "Review on Secure and Authentic Identification System using Finger Veins," International Journal of Advanced Research in Computer Science, vol. 8, no. 5, 2017.
[10] Z. Liu, Y. Yin, H. Wang, S. Song, and Q. Li, "Finger vein recognition with manifold learning," Journal of Network and Computer Applications, vol. 33, no. 3, pp. 275-282, 2010.
[11] J. Yang and X. Li, "Efficient finger vein localization and recognition," in 2010 International Conference on Pattern Recognition, 2010, pp. 1148-1151: IEEE.
[12] F. Guan, K. Wang, and Q. Yang, "A study of two direction weighted (2D) 2 LDA for finger vein recognition," in Image and Signal Processing (CISP), 2011 4th International Congress on, 2011, vol. 2, pp. 860-864: IEEE.
[13] E. C. Lee, H. Jung, and D. Kim, "New finger biometric method using near infrared imaging," Sensors, vol. 11, no. 3, pp. 2319-2333, 2011.
[14] W. Yang, Q. Rao, and Q. Liao, "Personal identification for single sample using finger vein location and direction coding," in Hand-Based Biometrics (ICHB), 2011 International Conference on, 2011, pp. 1-6: IEEE.
[15] B. A. Rosdi, C. W. Shing, and S. A. Suandi, "Finger vein recognition using local line binary pattern," Sensors, vol. 11, no. 12, pp. 11357-11371, 2011.
[16] S. Damavandinejadmonfared, "Finger vein recognition using linear kernel entropy component analysis," in Intelligent Computer Communication and Processing (ICCP), 2012 IEEE International Conference on, 2012, pp. 249-252: IEEE.
[17] A. K. Mobarakeh, S. M. Rizi, S. M. Khaniabadi, M. A. Bagheri, and S. Nazari, "Applying Weighted K-nearest centroid neighbor as classifier to improve the finger vein recognition performance," in Control System, Computing and Engineering (ICCSCE), 2012 IEEE International Conference on, 2012, pp. 56-59: IEEE.
[18] P. Harsha and C. Subashini, "A real time embedded novel finger-vein recognition system for authenticated on teller machine," in Emerging Trends in Electrical Engineering and Energy Management (ICETEEEM), 2012 International Conference on, 2012, pp. 271-275: IEEE.
[19] X. Meng, G. Yang, Y. Yin, and R. Xiao, "Finger vein recognition based on local directional code," Sensors, vol. 12, no. 11, pp. 14937-14952, 2012.
[20] J. Yang and Y. Shi, "Towards finger-vein image restoration and enhancement for finger-vein recognition," Information Sciences, vol. 268, pp. 33-52, 2014.
[21] G. Yang, R. Xiao, Y. Yin, and L. Yang, "Finger vein recognition based on personalized weight maps," Sensors, vol. 13, no. 9, pp. 12093-12112, 2013.
[22] Y. Lu, S. Yoon, S. J. Xie, J. Yang, Z. Wang, and D. S. Park, "Finger vein recognition using histogram of competitive gabor responses," in 2014 22nd International Conference on Pattern Recognition (ICPR), 2014, pp. 1758-1763: IEEE.
[23] M. Vlachos and E. Dermatas, "Finger vein segmentation from infrared images based on a modified separable mumford shah model and local entropy thresholding," Computational and mathematical methods in medicine, vol. 2015, 2015.
[24] P. Gupta and P. Gupta, "An accurate finger vein based verification system," Digital Signal Processing, vol. 38, pp. 43-52, 2015.
[25] J.-D. Wu and C.-T. Liu, "Finger-vein pattern identification using SVM and neural network technique," Expert Systems with Applications, vol. 38, no. 11, pp. 14284-14289, 2011.
[26] J.-D. Wu and C.-T. Liu, "Finger-vein pattern identification using principal component analysis and the neural network technique," Expert Systems with Applications, vol. 38, no. 5, pp. 5423-5427, 2011.
[27] A. N. Hoshyar, R. Sulaiman, and A. N. Houshyar, "Smart access control with finger vein authentication and neural network," J. Am. Sci, vol. 7, no. 9, 2011.
[28] K.-Q. Wang, A. S. Khisa, X.-Q. Wu, and Q.-S. Zhao, "Finger vein recognition using LBP variance with global matching," in Wavelet Analysis and Pattern Recognition (ICWAPR), 2012 International Conference on, 2012, pp. 196-201: IEEE.
[29] S. Khellat-kihel, N. Cardoso, J. Monteiro, and M. Benyettou, "Finger vein recognition using Gabor filter and support vector machine," in Image Processing, Applications and Systems Conference (IPAS), 2014 First International, 2014, pp. 1-6: IEEE.
[30] S. A. RADZI, M. K. HANI, and R. Bakhteri, "Finger-vein biometric identification using convolutional neural network," Turkish Journal of Electrical Engineering & Computer Sciences, vol. 24, no. 3, pp. 1863-1878, 2016.
[31] Z. J. Geng, "Face recognition system and method," ed: Google Patents, 2007.
[32] J. Chen, H. Shao, and C. Hu, "Image Segmentation Based on Mathematical Morphological Operator," in Colorimetry and Image Processing: IntechOpen, 2017.
[33] M. D. S. B. Ramli, "TOPIC: DIGITAL IMAGE PROCESSING MOOC," UNIVERSITY CARNIVAL on e-LEARNING (IUCEL) 2018, p. 476, 2018.
[34] R. C. Gonzalez, R. E. Woods, and S. L. Eddins, Digital image processing using MATLAB. Pearson Education India, 2004.
[35] M. A. Syarif, T. S. Ong, A. B. Teoh, and C. Tee, "Enhanced maximum curvature descriptors for finger vein verification," Multimedia Tools and Applications, vol. 76, no. 5, pp. 6859-6887, 2017.
[36] A. Malhi and R. X. Gao, "PCA-based feature selection scheme for machine defect classification," IEEE Transactions on Instrumentation and Measurement, vol. 53, no. 6, pp. 1517-1525, 2004.
[37] C. Kauba and A. Uhl, "An available open-source vein recognition framework," in Handbook of Vascular Biometrics: Springer, Cham, 2020, pp. 113-142.
[38] L. Yang, G. Yang, K. Wang, H. Liu, X. Xi, and Y. Yin, "Point grouping method for finger vein recognition," IEEE Access, vol. 7, pp. 28185-28195, 2019.
Published
2022-03-01
How to Cite
Mousavizadeh Mobarakeh, S. A., & Emadi, M. (2022). Improvement of the Identification Rate using Finger Veins based on the Enhanced Maximum Curvature Method using Morphological Operators. Majlesi Journal of Telecommunication Devices, 11(1), 1-8. https://doi.org/10.52547/mjtd.11.1.1
Section
Articles